

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__		:       List[str]						    =	logging.get_logger()







@dataclass
class 			lowercase    :
				__SCREAMING_SNAKE_CASE :			nn.Module
				__SCREAMING_SNAKE_CASE :			List[nn.Module]	      =      field(default_factory=lowercase_      )
				__SCREAMING_SNAKE_CASE :			list	      =      field(default_factory=lowercase_      )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   len(list(m.modules()	)	) == 1 or isinstance(snake_case				,       nn.Convad	) or isinstance(snake_case				,       nn.BatchNormad	)
											if has_not_submodules:
																		self.traced.append(snake_case	)




				def __call__( self				,       snake_case	):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook	)	)
											self.module(snake_case	)
											[x.remove() for x in self.handles]
											return self




				@property
				def    a							( self	):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case	: len(list(x.state_dict().keys()	)	) > 0				,       self.traced	)	)







@dataclass
class 			lowercase    :
				__SCREAMING_SNAKE_CASE :			nn.Module
				__SCREAMING_SNAKE_CASE :			nn.Module
				__SCREAMING_SNAKE_CASE :			int	      =      1
				__SCREAMING_SNAKE_CASE :			List	      =      field(default_factory=lowercase_      )
				__SCREAMING_SNAKE_CASE :			List	      =      field(default_factory=lowercase_      )
				__SCREAMING_SNAKE_CASE :			bool	      =      True




				def __call__( self				,       snake_case	):
											snake_case_          =   Tracker(self.dest	)(snake_case	).parametrized
											snake_case_          =   Tracker(self.src	)(snake_case	).parametrized

											snake_case_          =   list(filter(lambda snake_case	: type(snake_case	) not in self.src_skip				,       snake_case	)	)
											snake_case_          =   list(filter(lambda snake_case	: type(snake_case	) not in self.dest_skip				,       snake_case	)	)

											if len(snake_case	) != len(snake_case	) and self.raise_if_mismatch:
																		raise Exception(
																		    F'''Numbers of operations are different. Source module has {len(snake_case	)} operations while'''
																		    F''' destination module has {len(snake_case	)}.'''	)

											for dest_m, src_m in zip(snake_case				,       snake_case	):
																		dest_m.load_state_dict(src_m.state_dict()	)
																		if self.verbose == 1:
																									print(F'''Transfered from={src_m} to={dest_m}'''	)







class 			lowercase    (	nn.Module      ):




				def __init__( self				,       snake_case	):
											super().__init__()

											snake_case_          =   []
											# - get the stem
											feature_blocks.append(('conv1', model.stem)	)
											# - get all the feature blocks
											for k, v in model.trunk_output.named_children():
																		assert k.startswith('block'	), F'''Unexpected layer name {k}'''
																		snake_case_          =   len(snake_case	) + 1
																		feature_blocks.append((F'''res{block_index}''', v)	)

											snake_case_          =   nn.ModuleDict(snake_case	)




				def    a							( self				,       snake_case	):
											return get_trunk_forward_outputs(
											    snake_case				,       out_feat_keys=snake_case				,       feature_blocks=self._feature_blocks				,       )







class 			lowercase    (	lowercase_      ):




				def    a							( self				,       snake_case	):
											snake_case_          =   x.split('-'	)
											return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]	)




				def __getitem__( self				,       snake_case	):
											# default to timm!
											if x not in self:
																		snake_case_          =   self.convert_name_to_timm(snake_case	)
																		snake_case_          =   partial(lambda: (timm.create_model(snake_case				,       pretrained=snake_case	).eval(), None)	)

											else:
																		snake_case_          =   super().__getitem__(snake_case	)

											return val







class 			lowercase    (	lowercase_      ):




				def __getitem__( self				,       snake_case	):
											if "seer" in x and "in1k" not in x:
																		snake_case_          =   RegNetModel
											else:
																		snake_case_          =   RegNetForImageClassification
											return val





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							for from_key, to_key in keys:
														snake_case_          =   from_state_dict[from_key].clone()
														print(F'''Copied key={from_key} to={to_key}'''				)
							return to_state_dict





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__ = True       ,							):







							'''simple docstring'''






							print(F'''Converting {name}...'''				)
							with torch.no_grad():
														snake_case_    ,   snake_case_          =   from_model_func()
														snake_case_          =   our_model_func(UpperCamelCase__				).eval()
														snake_case_          =   ModuleTransfer(src=UpperCamelCase__       ,							dest=UpperCamelCase__       ,							raise_if_mismatch=UpperCamelCase__				)
														snake_case_          =   torch.randn((1, 3, 224, 224)				)
														module_transfer(UpperCamelCase__				)

							if from_state_dict is not None:
														snake_case_          =   []
														# for seer - in1k finetuned we have to manually copy the head
														if "seer" in name and "in1k" in name:
																					snake_case_          =   [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
														snake_case_          =   manually_copy_vissl_head(UpperCamelCase__       ,							our_model.state_dict()       ,							UpperCamelCase__				)
														our_model.load_state_dict(UpperCamelCase__				)

							snake_case_          =   our_model(UpperCamelCase__       ,							output_hidden_states=UpperCamelCase__				)
							snake_case_          =   (
							    our_outputs.logits if isinstance(UpperCamelCase__       ,							UpperCamelCase__				) else our_outputs.last_hidden_state
							)

							snake_case_          =   from_model(UpperCamelCase__				)
							snake_case_          =   from_output[-1] if type(UpperCamelCase__				) is list else from_output

							# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
							if "seer" in name and "in1k" in name:
														snake_case_          =   our_outputs.hidden_states[-1]

							assert torch.allclose(UpperCamelCase__       ,							UpperCamelCase__				), "The model logits don't match the original one."

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / name       ,							commit_message='Add model'       ,							use_temp_dir=UpperCamelCase__       ,							)

														snake_case_          =   224 if 'seer' not in name else 384
														# we can use the convnext one
														snake_case_          =   AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,							size=UpperCamelCase__				)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name       ,							commit_message='Add image processor'       ,							use_temp_dir=UpperCamelCase__       ,							)

														print(F'''Pushed {name}'''				)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = True				):







							'''simple docstring'''






							snake_case_          =   'imagenet-1k-id2label.json'
							snake_case_          =   1000
							snake_case_          =   (1, num_labels)

							snake_case_          =   'huggingface/label-files'
							snake_case_          =   num_labels
							snake_case_          =   json.load(open(cached_download(hf_hub_url(UpperCamelCase__       ,							UpperCamelCase__       ,							repo_type='dataset'				)				)       ,							'r'				)				)
							snake_case_          =   {int(UpperCamelCase__				): v for k, v in idalabel.items()}

							snake_case_          =   idalabel
							snake_case_          =   {v: k for k, v in idalabel.items()}

							snake_case_          =   partial(UpperCamelCase__       ,							num_labels=UpperCamelCase__       ,							idalabel=UpperCamelCase__       ,							labelaid=UpperCamelCase__				)

							snake_case_          =   {
							    'regnet-x-002': ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]       ,							hidden_sizes=[24, 56, 152, 368]       ,							groups_width=8       ,							layer_type='x'				),
							    'regnet-x-004': ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]       ,							hidden_sizes=[32, 64, 160, 384]       ,							groups_width=16       ,							layer_type='x'				),
							    'regnet-x-006': ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]       ,							hidden_sizes=[48, 96, 240, 528]       ,							groups_width=24       ,							layer_type='x'				),
							    'regnet-x-008': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]       ,							hidden_sizes=[64, 128, 288, 672]       ,							groups_width=16       ,							layer_type='x'				),
							    'regnet-x-016': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]       ,							hidden_sizes=[72, 168, 408, 912]       ,							groups_width=24       ,							layer_type='x'				),
							    'regnet-x-032': ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]       ,							hidden_sizes=[96, 192, 432, 1008]       ,							groups_width=48       ,							layer_type='x'				),
							    'regnet-x-040': ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]       ,							hidden_sizes=[80, 240, 560, 1360]       ,							groups_width=40       ,							layer_type='x'				),
							    'regnet-x-064': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]       ,							hidden_sizes=[168, 392, 784, 1624]       ,							groups_width=56       ,							layer_type='x'				),
							    'regnet-x-080': ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]       ,							hidden_sizes=[80, 240, 720, 1920]       ,							groups_width=120       ,							layer_type='x'				),
							    'regnet-x-120': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]       ,							hidden_sizes=[224, 448, 896, 2240]       ,							groups_width=112       ,							layer_type='x'				),
							    'regnet-x-160': ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]       ,							hidden_sizes=[256, 512, 896, 2048]       ,							groups_width=128       ,							layer_type='x'				),
							    'regnet-x-320': ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]       ,							hidden_sizes=[336, 672, 1344, 2520]       ,							groups_width=168       ,							layer_type='x'				),
							    # y variant
							    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]       ,							hidden_sizes=[24, 56, 152, 368]       ,							groups_width=8				),
							    'regnet-y-004': ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]       ,							hidden_sizes=[48, 104, 208, 440]       ,							groups_width=8				),
							    'regnet-y-006': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]       ,							hidden_sizes=[48, 112, 256, 608]       ,							groups_width=16				),
							    'regnet-y-008': ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]       ,							hidden_sizes=[64, 128, 320, 768]       ,							groups_width=16				),
							    'regnet-y-016': ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]       ,							hidden_sizes=[48, 120, 336, 888]       ,							groups_width=24				),
							    'regnet-y-032': ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]       ,							hidden_sizes=[72, 216, 576, 1512]       ,							groups_width=24				),
							    'regnet-y-040': ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]       ,							hidden_sizes=[128, 192, 512, 1088]       ,							groups_width=64				),
							    'regnet-y-064': ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]       ,							hidden_sizes=[144, 288, 576, 1296]       ,							groups_width=72				),
							    'regnet-y-080': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]       ,							hidden_sizes=[168, 448, 896, 2016]       ,							groups_width=56				),
							    'regnet-y-120': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]       ,							hidden_sizes=[224, 448, 896, 2240]       ,							groups_width=112				),
							    'regnet-y-160': ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]       ,							hidden_sizes=[224, 448, 1232, 3024]       ,							groups_width=112				),
							    'regnet-y-320': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]       ,							hidden_sizes=[232, 696, 1392, 3712]       ,							groups_width=232				),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]       ,							hidden_sizes=[232, 696, 1392, 3712]       ,							groups_width=232				),
							    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]       ,							hidden_sizes=[328, 984, 1968, 4920]       ,							groups_width=328				),
							    'regnet-y-1280-seer': RegNetConfig(
							        depths=[2, 7, 17, 1]       ,							hidden_sizes=[528, 1056, 2904, 7392]       ,							groups_width=264				),
							    'regnet-y-2560-seer': RegNetConfig(
							        depths=[3, 7, 16, 1]       ,							hidden_sizes=[640, 1696, 2544, 5088]       ,							groups_width=640				),
							    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]       ,							hidden_sizes=[2020, 4040, 11110, 28280]       ,							groups_width=1010				),
							    # finetuned on imagenet
							    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]       ,							hidden_sizes=[232, 696, 1392, 3712]       ,							groups_width=232				),
							    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]       ,							hidden_sizes=[328, 984, 1968, 4920]       ,							groups_width=328				),
							    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]       ,							hidden_sizes=[528, 1056, 2904, 7392]       ,							groups_width=264				),
							    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]       ,							hidden_sizes=[640, 1696, 2544, 5088]       ,							groups_width=640				),
							    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]       ,							hidden_sizes=[2020, 4040, 11110, 28280]       ,							groups_width=1010				),
							}

							snake_case_          =   NameToOurModelFuncMap()
							snake_case_          =   NameToFromModelFuncMap()
							# add seer weights logic

							def load_using_classy_vision(UpperCamelCase__       ,							UpperCamelCase__				) -> Tuple[nn.Module, Dict]:
														snake_case_          =   torch.hub.load_state_dict_from_url(UpperCamelCase__       ,							model_dir=str(UpperCamelCase__				)       ,							map_location='cpu'				)
														snake_case_          =   model_func()
														# check if we have a head, if yes add it
														snake_case_          =   files['classy_state_dict']['base_model']['model']
														snake_case_          =   model_state_dict['trunk']
														model.load_state_dict(UpperCamelCase__				)
														return model.eval(), model_state_dict["heads"]

							# pretrained
							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)       ,							)

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)       ,							)

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)       ,							)

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,							lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27       ,							group_width=1010       ,							w_a=1744       ,							w_a=620.83       ,							w_m=2.52				)				)				)       ,							)

							# IN1K finetuned
							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)       ,							)

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)       ,							)

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)       ,							)

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,							lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27       ,							group_width=1010       ,							w_a=1744       ,							w_a=620.83       ,							w_m=2.52				)				)				)       ,							)

							if model_name:
														convert_weight_and_push(
														    UpperCamelCase__       ,							names_to_from_model_map[model_name]       ,							names_to_ours_model_map[model_name]       ,							names_to_config[model_name]       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    UpperCamelCase__       ,							names_to_from_model_map[model_name]       ,							names_to_ours_model_map[model_name]       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							)
							return config, expected_shape


if __name__ == "__main__":
					__magic_name__		:       Optional[int]						    =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
					        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					__magic_name__		:       str						    =	parser.parse_args()

					__magic_name__		:       Path						    =	args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    XLMForMultipleChoice,
					    XLMForQuestionAnswering,
					    XLMForQuestionAnsweringSimple,
					    XLMForSequenceClassification,
					    XLMForTokenClassification,
					    XLMModel,
					    XLMWithLMHeadModel,
					)
					from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=13				,       snake_case=7				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=False				,       snake_case=False				,       snake_case=False				,       snake_case=2				,       snake_case=99				,       snake_case=0				,       snake_case=32				,       snake_case=5				,       snake_case=4				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=512				,       snake_case=2				,       snake_case=0.02				,       snake_case=2				,       snake_case=4				,       snake_case="last"				,       snake_case=True				,       snake_case=None				,       snake_case=0				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   seq_length
											snake_case_          =   is_training
											snake_case_          =   use_input_lengths
											snake_case_          =   use_token_type_ids
											snake_case_          =   use_labels
											snake_case_          =   gelu_activation
											snake_case_          =   sinusoidal_embeddings
											snake_case_          =   causal
											snake_case_          =   asm
											snake_case_          =   n_langs
											snake_case_          =   vocab_size
											snake_case_          =   n_special
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   max_position_embeddings
											snake_case_          =   type_sequence_label_size
											snake_case_          =   initializer_range
											snake_case_          =   num_labels
											snake_case_          =   num_choices
											snake_case_          =   summary_type
											snake_case_          =   use_proj
											snake_case_          =   scope
											snake_case_          =   bos_token_id




				def    a							( self	):
											snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size	)
											snake_case_          =   random_attention_mask([self.batch_size, self.seq_length]	)

											snake_case_          =   None
											if self.use_input_lengths:
																		snake_case_          =   (
																		    ids_tensor([self.batch_size]				,       vocab_size=2	) + self.seq_length - 2
																		)  # small variation of seq_length

											snake_case_          =   None
											if self.use_token_type_ids:
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.n_langs	)

											snake_case_          =   None
											snake_case_          =   None
											snake_case_          =   None
											if self.use_labels:
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.type_sequence_label_size	)
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels	)
																		snake_case_          =   ids_tensor([self.batch_size]				,       2	).float()
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.num_choices	)

											snake_case_          =   self.get_config()

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_lengths,
											    sequence_labels,
											    token_labels,
											    is_impossible_labels,
											    choice_labels,
											    input_mask,
											)




				def    a							( self	):
											return XLMConfig(
											    vocab_size=self.vocab_size				,       n_special=self.n_special				,       emb_dim=self.hidden_size				,       n_layers=self.num_hidden_layers				,       n_heads=self.num_attention_heads				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       gelu_activation=self.gelu_activation				,       sinusoidal_embeddings=self.sinusoidal_embeddings				,       asm=self.asm				,       causal=self.causal				,       n_langs=self.n_langs				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       summary_type=self.summary_type				,       use_proj=self.use_proj				,       num_labels=self.num_labels				,       bos_token_id=self.bos_token_id				,       )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMModel(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   model(snake_case				,       lengths=snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case	)
											self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMWithLMHeadModel(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       token_type_ids=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnsweringSimple(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)
											snake_case_          =   outputs
											self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)	)
											self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnswering(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       p_mask=snake_case				,       )

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       )

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											self.parent.assertEqual(result_with_labels.loss.shape				,       ()	)
											self.parent.assertEqual(result.start_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(result.start_top_index.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(
											    result.end_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(
											    result.end_top_index.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(result.cls_logits.shape				,       (self.batch_size,)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForSequenceClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)
											snake_case_          =   model(snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_labels
											snake_case_          =   XLMForTokenClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       attention_mask=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_choices
											snake_case_          =   XLMForMultipleChoice(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   input_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   token_type_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   input_mask.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   model(
											    snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=snake_case				,       )
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)	)




				def    a							( self	):
											snake_case_          =   self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   
											)          =   config_and_inputs
											snake_case_          =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
											return config, inputs_dict







@require_torch
class 			lowercase    (	lowercase_     ,  lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      (
				    (
				        XLMModel,
				        XLMWithLMHeadModel,
				        XLMForQuestionAnswering,
				        XLMForSequenceClassification,
				        XLMForQuestionAnsweringSimple,
				        XLMForTokenClassification,
				        XLMForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE :			Tuple	      =      (
				    (XLMWithLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				__SCREAMING_SNAKE_CASE :			int	      =      (
				    {
				        '''feature-extraction''': XLMModel,
				        '''fill-mask''': XLMWithLMHeadModel,
				        '''question-answering''': XLMForQuestionAnsweringSimple,
				        '''text-classification''': XLMForSequenceClassification,
				        '''text-generation''': XLMWithLMHeadModel,
				        '''token-classification''': XLMForTokenClassification,
				        '''zero-shot''': XLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											if (
											    pipeline_test_casse_name == "QAPipelineTests"
											    and tokenizer_name is not None
											    and not tokenizer_name.endswith('Fast'	)
											):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

											return False




				def    a							( self				,       snake_case				,       snake_case				,       snake_case=False	):
											snake_case_          =   super()._prepare_for_class(snake_case				,       snake_case				,       return_labels=snake_case	)

											if return_labels:
																		if model_class.__name__ == "XLMForQuestionAnswering":
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)

											return inputs_dict




				def    a							( self	):
											snake_case_          =   XLMModelTester(self	)
											snake_case_          =   ConfigTester(self				,       config_class=snake_case				,       emb_dim=37	)




				def    a							( self	):
											self.config_tester.run_common_tests()




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_model(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_lm_head(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_simple_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_sequence_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_token_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_attentions in attentions]				,       [True] * len(snake_case	)	)
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_attentions in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   min_length + idx + 1

																		snake_case_          =   (
																		    batch_size * num_beam_groups,
																		    config.num_attention_heads,
																		    tgt_len,
																		    src_len,
																		)
																		# check attn size
																		self.assertListEqual(
																		    [layer_attention.shape for layer_attention in iter_attentions]				,       [expected_shape] * len(snake_case	)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_hidden_states in hidden_states]				,       [True] * len(snake_case	)				,       )
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_hidden_states in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
																		# check hidden size
																		self.assertListEqual(
																		    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,       [expected_shape] * len(snake_case	)				,       )
											pass




				@slow
				def    a							( self	):
											for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_          =   XLMModel.from_pretrained(snake_case	)
																		self.assertIsNotNone(snake_case	)







@require_torch
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'	)
											model.to(snake_case	)
											snake_case_          =   torch.tensor([[14, 447]]				,       dtype=torch.long				,       device=snake_case	)  # the president
											snake_case_          =   [
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											]  # the president the president the president the president the president the president the president the president the president the president
											# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
											snake_case_          =   model.generate(snake_case				,       do_sample=snake_case	)
											self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,       snake_case	)




