




import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
							    FlaubertForMultipleChoice,
							    FlaubertForQuestionAnswering,
							    FlaubertForQuestionAnsweringSimple,
							    FlaubertForSequenceClassification,
							    FlaubertForTokenClassification,
							    FlaubertModel,
							    FlaubertWithLMHeadModel,
							)
							from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST




class 						__lowercase		(lowerCamelCase_							):




						"""simple docstring"""





						def __init__(				self    ,				A    ,				A=1_3    ,				A=7    ,				A=True    ,				A=True    ,				A=True    ,				A=True    ,				A=True    ,				A=False    ,				A=False    ,				A=False    ,				A=2    ,				A=9_9    ,				A=0    ,				A=3_2    ,				A=5    ,				A=4    ,				A=0.1    ,				A=0.1    ,				A=5_1_2    ,				A=1_2    ,				A=2    ,				A=0.02    ,				A=3    ,				A=4    ,				A="last"    ,				A=None    ,				A=None    ,				)		->    int:
									snake_case				:    List[Any]           =	parent
									snake_case				:    str           =	batch_size
									snake_case				:    Any           =	seq_length
									snake_case				:    Dict           =	is_training
									snake_case				:    List[Any]           =	use_input_lengths
									snake_case				:    Dict           =	use_token_type_ids
									snake_case				:    Any           =	use_labels
									snake_case				:    str           =	gelu_activation
									snake_case				:    Union[str, Any]           =	sinusoidal_embeddings
									snake_case				:    List[str]           =	causal
									snake_case				:    Optional[int]           =	asm
									snake_case				:    Union[str, Any]           =	n_langs
									snake_case				:    Dict           =	vocab_size
									snake_case				:    List[str]           =	n_special
									snake_case				:    Tuple           =	hidden_size
									snake_case				:    Dict           =	num_hidden_layers
									snake_case				:    str           =	num_attention_heads
									snake_case				:    int           =	hidden_dropout_prob
									snake_case				:    int           =	attention_probs_dropout_prob
									snake_case				:    Tuple           =	max_position_embeddings
									snake_case				:    List[str]           =	type_vocab_size
									snake_case				:    Optional[int]           =	type_sequence_label_size
									snake_case				:    Any           =	initializer_range
									snake_case				:    Dict           =	num_labels
									snake_case				:    int           =	num_choices
									snake_case				:    List[Any]           =	summary_type
									snake_case				:    Optional[int]           =	use_proj
									snake_case				:    str           =	scope

						def      UpperCAmelCase				(				self     )		->    Tuple:
									snake_case				:    Optional[Any]           =	ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size     )
									snake_case				:    List[Any]           =	random_attention_mask([self.batch_size, self.seq_length]     )

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

									snake_case				:    List[Any]           =	None
									if self.use_token_type_ids:
												snake_case				:    Dict           =	ids_tensor([self.batch_size, self.seq_length]    ,				self.n_langs     )

									snake_case				:    Optional[int]           =	None
									snake_case				:    Optional[Any]           =	None
									snake_case				:    Union[str, Any]           =	None
									if self.use_labels:
												snake_case				:    Any           =	ids_tensor([self.batch_size]    ,				self.type_sequence_label_size     )
												snake_case				:    Union[str, Any]           =	ids_tensor([self.batch_size, self.seq_length]    ,				self.num_labels     )
												snake_case				:    Dict           =	ids_tensor([self.batch_size]    ,				2     ).float()
												snake_case				:    List[str]           =	ids_tensor([self.batch_size]    ,				self.num_choices     )

									snake_case				:    Dict           =	self.get_config()

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

						def      UpperCAmelCase				(				self     )		->    Tuple:
									return FlaubertConfig(
									    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    ,				)

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    int:
									snake_case				:    Union[str, Any]           =	FlaubertModel(config=lowerCAmelCase__     )
									model.to(lowerCAmelCase__     )
									model.eval()
									snake_case				:    int           =	model(lowerCAmelCase__    ,				lengths=lowerCAmelCase__    ,				langs=lowerCAmelCase__     )
									snake_case				:    Optional[Any]           =	model(lowerCAmelCase__    ,				langs=lowerCAmelCase__     )
									snake_case				:    List[Any]           =	model(lowerCAmelCase__     )
									self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)     )

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    Tuple:
									snake_case				:    str           =	FlaubertWithLMHeadModel(lowerCAmelCase__     )
									model.to(lowerCAmelCase__     )
									model.eval()

									snake_case				:    Optional[Any]           =	model(lowerCAmelCase__    ,				token_type_ids=lowerCAmelCase__    ,				labels=lowerCAmelCase__     )
									self.parent.assertEqual(result.loss.shape    ,				()     )
									self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)     )

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    int:
									snake_case				:    int           =	FlaubertForQuestionAnsweringSimple(lowerCAmelCase__     )
									model.to(lowerCAmelCase__     )
									model.eval()

									snake_case				:    Tuple           =	model(lowerCAmelCase__     )

									snake_case				:    int           =	model(lowerCAmelCase__    ,				start_positions=lowerCAmelCase__    ,				end_positions=lowerCAmelCase__     )
									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      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    Optional[int]:
									snake_case				:    str           =	FlaubertForQuestionAnswering(lowerCAmelCase__     )
									model.to(lowerCAmelCase__     )
									model.eval()

									snake_case				:    int           =	model(lowerCAmelCase__     )

									snake_case				:    Any           =	model(
									    lowerCAmelCase__    ,				start_positions=lowerCAmelCase__    ,				end_positions=lowerCAmelCase__    ,				cls_index=lowerCAmelCase__    ,				is_impossible=lowerCAmelCase__    ,				p_mask=lowerCAmelCase__    ,				)

									snake_case				:    Union[str, Any]           =	model(
									    lowerCAmelCase__    ,				start_positions=lowerCAmelCase__    ,				end_positions=lowerCAmelCase__    ,				cls_index=lowerCAmelCase__    ,				is_impossible=lowerCAmelCase__    ,				)

									((snake_case    )     ,     )				:    Any           =	result_with_labels.to_tuple()

									snake_case				:    Tuple           =	model(lowerCAmelCase__    ,				start_positions=lowerCAmelCase__    ,				end_positions=lowerCAmelCase__     )

									((snake_case    )     ,     )				:    Any           =	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      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    str:
									snake_case				:    Any           =	FlaubertForSequenceClassification(lowerCAmelCase__     )
									model.to(lowerCAmelCase__     )
									model.eval()

									snake_case				:    Union[str, Any]           =	model(lowerCAmelCase__     )
									snake_case				:    Union[str, Any]           =	model(lowerCAmelCase__    ,				labels=lowerCAmelCase__     )

									self.parent.assertEqual(result.loss.shape    ,				()     )
									self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)     )

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    int:
									snake_case				:    List[Any]           =	self.num_labels
									snake_case				:    Union[str, Any]           =	FlaubertForTokenClassification(lowerCAmelCase__     )
									model.to(lowerCAmelCase__     )
									model.eval()

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

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    Optional[Any]:
									snake_case				:    Dict           =	self.num_choices
									snake_case				:    List[Any]           =	FlaubertForMultipleChoice(config=lowerCAmelCase__     )
									model.to(lowerCAmelCase__     )
									model.eval()
									snake_case				:    Optional[Any]           =	input_ids.unsqueeze(1     ).expand(-1    ,				self.num_choices    ,				-1     ).contiguous()
									snake_case				:    str           =	token_type_ids.unsqueeze(1     ).expand(-1    ,				self.num_choices    ,				-1     ).contiguous()
									snake_case				:    Optional[Any]           =	input_mask.unsqueeze(1     ).expand(-1    ,				self.num_choices    ,				-1     ).contiguous()
									snake_case				:    Union[str, Any]           =	model(
									    lowerCAmelCase__    ,				attention_mask=lowerCAmelCase__    ,				token_type_ids=lowerCAmelCase__    ,				labels=lowerCAmelCase__    ,				)
									self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_choices)     )

						def      UpperCAmelCase				(				self     )		->    List[str]:
									snake_case				:    Dict           =	self.prepare_config_and_inputs()
									(
									    (
									    snake_case    
									)     , (
									    snake_case    
									)     , (
									    snake_case    
									)     , (
									    snake_case    
									)     , (
									    snake_case    
									)     , (
									    snake_case    
									)     , (
									    snake_case    
									)     , (
									    snake_case    
									)     , (
									    snake_case    
									)     ,     
									)				:    Optional[Any]           =	config_and_inputs
									snake_case				:    Any           =	{
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """lengths""": input_lengths,
									    """attention_mask""": input_mask,
									}
									return config, inputs_dict




@require_torch
class 						__lowercase		(lowerCamelCase_					,		lowerCamelCase_					,		unittest.TestCase							):




						"""simple docstring"""





						_snake_case												=						(
						    (
						        FlaubertModel,
						        FlaubertWithLMHeadModel,
						        FlaubertForQuestionAnswering,
						        FlaubertForQuestionAnsweringSimple,
						        FlaubertForSequenceClassification,
						        FlaubertForTokenClassification,
						        FlaubertForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						_snake_case												=						(
						    {
						        """feature-extraction""": FlaubertModel,
						        """fill-mask""": FlaubertWithLMHeadModel,
						        """question-answering""": FlaubertForQuestionAnsweringSimple,
						        """text-classification""": FlaubertForSequenceClassification,
						        """token-classification""": FlaubertForTokenClassification,
						        """zero-shot""": FlaubertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    Any:
									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      UpperCAmelCase				(				self    ,				A    ,				A    ,				A=False     )		->    Tuple:
									snake_case				:    str           =	super()._prepare_for_class(lowerCAmelCase__    ,				lowerCAmelCase__    ,				return_labels=lowerCAmelCase__     )

									if return_labels:
												if model_class.__name__ == "FlaubertForQuestionAnswering":
															snake_case				:    Optional[int]           =	torch.zeros(
															    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=lowerCAmelCase__     )
															snake_case				:    str           =	torch.zeros(
															    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=lowerCAmelCase__     )

									return inputs_dict

						def      UpperCAmelCase				(				self     )		->    List[str]:
									snake_case				:    str           =	FlaubertModelTester(self     )
									snake_case				:    Union[str, Any]           =	ConfigTester(self    ,				config_class=lowerCAmelCase__    ,				emb_dim=3_7     )

						def      UpperCAmelCase				(				self     )		->    Any:
									self.config_tester.run_common_tests()

						def      UpperCAmelCase				(				self     )		->    int:
									snake_case				:    List[str]           =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*lowerCAmelCase__     )

						def      UpperCAmelCase				(				self     )		->    str:
									snake_case				:    List[Any]           =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*lowerCAmelCase__     )

						def      UpperCAmelCase				(				self     )		->    str:
									snake_case				:    Any           =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_simple_qa(*lowerCAmelCase__     )

						def      UpperCAmelCase				(				self     )		->    int:
									snake_case				:    Optional[Any]           =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*lowerCAmelCase__     )

						def      UpperCAmelCase				(				self     )		->    int:
									snake_case				:    List[Any]           =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*lowerCAmelCase__     )

						def      UpperCAmelCase				(				self     )		->    Dict:
									snake_case				:    Tuple           =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_token_classif(*lowerCAmelCase__     )

						def      UpperCAmelCase				(				self     )		->    str:
									snake_case				:    Any           =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_multiple_choice(*lowerCAmelCase__     )

						@slow
						def      UpperCAmelCase				(				self     )		->    List[Any]:
									for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												snake_case				:    Optional[int]           =	FlaubertModel.from_pretrained(lowerCAmelCase__     )
												self.assertIsNotNone(lowerCAmelCase__     )

						@slow
						@require_torch_gpu
						def      UpperCAmelCase				(				self     )		->    Tuple:
									snake_case     , snake_case				:    str           =	self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
												if model_class == FlaubertForMultipleChoice:
															return

												snake_case				:    List[str]           =	True
												snake_case				:    Dict           =	model_class(config=lowerCAmelCase__     )

												snake_case				:    Optional[int]           =	self._prepare_for_class(lowerCAmelCase__    ,				lowerCAmelCase__     )
												snake_case				:    Tuple           =	torch.jit.trace(
												    lowerCAmelCase__    ,				(inputs_dict["""input_ids"""].to("""cpu"""     ), inputs_dict["""attention_mask"""].to("""cpu"""     ))     )

												with tempfile.TemporaryDirectory() as tmp:
															torch.jit.save(lowerCAmelCase__    ,				os.path.join(lowerCAmelCase__    ,				"""traced_model.pt"""     )     )
															snake_case				:    Dict           =	torch.jit.load(os.path.join(lowerCAmelCase__    ,				"""traced_model.pt"""     )    ,				map_location=lowerCAmelCase__     )
															loaded(inputs_dict["""input_ids"""].to(lowerCAmelCase__     )    ,				inputs_dict["""attention_mask"""].to(lowerCAmelCase__     )     )




@require_torch
class 						__lowercase		(unittest.TestCase							):




						"""simple docstring"""





						@slow
						def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
									snake_case				:    Dict           =	FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""     )
									snake_case				:    Optional[int]           =	torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
									with torch.no_grad():
												snake_case				:    Tuple           =	model(lowerCAmelCase__     )[0]
									snake_case				:    Dict           =	torch.Size((1, 1_1, 7_6_8)     )
									self.assertEqual(output.shape    ,				lowerCAmelCase__     )
									snake_case				:    List[str]           =	torch.tensor(
									    [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]]     )

									self.assertTrue(torch.allclose(output[:, :3, :3]    ,				lowerCAmelCase__    ,				atol=1e-4     )     )











import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:	List[Any]				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Dict           =	SwinConfig.from_pretrained(
       """microsoft/swin-tiny-patch4-window7-224"""						,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   snake_case				:    List[str]           =	MaskFormerConfig(backbone_config=lowercase					)

   snake_case				:    List[Any]           =	"""huggingface/label-files"""
   if "ade20k-full" in model_name:
      # this should be ok
      snake_case				:    Dict           =	847
      snake_case				:    List[str]           =	"""maskformer-ade20k-full-id2label.json"""
   elif "ade" in model_name:
      # this should be ok
      snake_case				:    Union[str, Any]           =	150
      snake_case				:    List[Any]           =	"""ade20k-id2label.json"""
   elif "coco-stuff" in model_name:
      # this should be ok
      snake_case				:    Union[str, Any]           =	171
      snake_case				:    int           =	"""maskformer-coco-stuff-id2label.json"""
   elif "coco" in model_name:
      # TODO
      snake_case				:    Optional[Any]           =	133
      snake_case				:    Optional[Any]           =	"""coco-panoptic-id2label.json"""
   elif "cityscapes" in model_name:
      # this should be ok
      snake_case				:    Tuple           =	19
      snake_case				:    int           =	"""cityscapes-id2label.json"""
   elif "vistas" in model_name:
      # this should be ok
      snake_case				:    int           =	65
      snake_case				:    Any           =	"""mapillary-vistas-id2label.json"""

   snake_case				:    Optional[Any]           =	json.load(open(hf_hub_download(lowercase						,lowercase						,repo_type="""dataset"""					)						,"""r"""					)					)
   snake_case				:    List[Any]           =	{int(lowercase					): v for k, v in idalabel.items()}

   return config




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   snake_case				:    Union[str, Any]           =	[]
   # stem
   # fmt: off
   rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")					)
   rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")					)
   rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")					)
   rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")					)
   # stages
   for i in range(len(config.backbone_config.depths					)					):
      for j in range(config.backbone_config.depths[i]					):
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

      if i < 3:
         rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
      rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
      rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

   # FPN
   rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")					)
   rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")					)
   rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")					)
   for source_index, target_index in zip(range(3						,0						,-1					)						,range(0						,3					)					):
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
   rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")					)
   rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")					)

   # Transformer decoder
   for idx in range(config.decoder_config.decoder_layers					):
      # self-attention out projection
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
      # cross-attention out projection
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
      # MLP 1
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
      # MLP 2
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
      # layernorm 1 (self-attention layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
      # layernorm 2 (cross-attention layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
      # layernorm 3 (final layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

   rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")					)
   rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")					)

   # heads on top
   rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")					)

   rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")					)
   rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")					)

   rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")					)
   rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")					)

   for i in range(3					):
      rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")					)
   # fmt: on

   return rename_keys




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   snake_case				:    Tuple           =	dct.pop(lowercase					)
   snake_case				:    int           =	val




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   snake_case				:    List[str]           =	[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
   for i in range(len(backbone_config.depths					)					):
      snake_case				:    Optional[Any]           =	num_features[i]
      for j in range(backbone_config.depths[i]					):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         snake_case				:    Tuple           =	state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
         snake_case				:    Optional[Any]           =	state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
         # next, add query, keys and values (in that order) to the state dict
         snake_case				:    Optional[Any]           =	in_proj_weight[:dim, :]
         snake_case				:    Optional[int]           =	in_proj_bias[: dim]
         snake_case				:    Union[str, Any]           =	in_proj_weight[
             dim : dim * 2, :
         ]
         snake_case				:    Tuple           =	in_proj_bias[
             dim : dim * 2
         ]
         snake_case				:    List[Any]           =	in_proj_weight[
             -dim :, :
         ]
         snake_case				:    Any           =	in_proj_bias[-dim :]
         # fmt: on




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Any:
   # fmt: off
   snake_case				:    int           =	config.decoder_config.hidden_size
   for idx in range(config.decoder_config.decoder_layers					):
      # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
      snake_case				:    Any           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
      snake_case				:    Tuple           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
      # next, add query, keys and values (in that order) to the state dict
      snake_case				:    Optional[int]           =	in_proj_weight[: hidden_size, :]
      snake_case				:    Any           =	in_proj_bias[:config.hidden_size]
      snake_case				:    Any           =	in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case				:    int           =	in_proj_bias[hidden_size : hidden_size * 2]
      snake_case				:    Any           =	in_proj_weight[-hidden_size :, :]
      snake_case				:    Union[str, Any]           =	in_proj_bias[-hidden_size :]
      # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
      snake_case				:    Dict           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
      snake_case				:    str           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
      # next, add query, keys and values (in that order) to the state dict
      snake_case				:    Dict           =	in_proj_weight[: hidden_size, :]
      snake_case				:    Dict           =	in_proj_bias[:config.hidden_size]
      snake_case				:    List[Any]           =	in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case				:    Optional[Any]           =	in_proj_bias[hidden_size : hidden_size * 2]
      snake_case				:    Tuple           =	in_proj_weight[-hidden_size :, :]
      snake_case				:    str           =	in_proj_bias[-hidden_size :]
   # fmt: on




def 						SCREAMING_SNAKE_CASE__ (   )  ->       torch.Tensor:
   snake_case				:    Any           =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case				:    Any           =	Image.open(requests.get(lowercase						,stream=lowercase					).raw					)
   return im




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase = False					)  ->       Dict:
   snake_case				:    List[str]           =	get_maskformer_config(lowercase					)

   # load original state_dict
   with open(lowercase						,"""rb"""					) as f:
      snake_case				:    Optional[Any]           =	pickle.load(lowercase					)
   snake_case				:    Optional[Any]           =	data["""model"""]

   # for name, param in state_dict.items():
   #     print(name, param.shape)

   # rename keys
   snake_case				:    str           =	create_rename_keys(lowercase					)
   for src, dest in rename_keys:
      rename_key(lowercase						,lowercase						,lowercase					)
   read_in_swin_q_k_v(lowercase						,config.backbone_config					)
   read_in_decoder_q_k_v(lowercase						,lowercase					)

   # update to torch tensors
   for key, value in state_dict.items():
      snake_case				:    List[Any]           =	torch.from_numpy(lowercase					)

   # load 🤗 model
   snake_case				:    int           =	MaskFormerForInstanceSegmentation(lowercase					)
   model.eval()

   for name, param in model.named_parameters():
      print(lowercase						,param.shape					)

   snake_case     , snake_case				:    Optional[int]           =	model.load_state_dict(lowercase						,strict=lowercase					)
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(lowercase					) == 0, f"""Unexpected keys: {unexpected_keys}"""

   # verify results
   snake_case				:    List[str]           =	prepare_img()
   if "vistas" in model_name:
      snake_case				:    Optional[int]           =	65
   elif "cityscapes" in model_name:
      snake_case				:    int           =	65535
   else:
      snake_case				:    List[str]           =	255
   snake_case				:    List[Any]           =	True if """ade""" in model_name else False
   snake_case				:    Optional[int]           =	MaskFormerImageProcessor(ignore_index=lowercase						,reduce_labels=lowercase					)

   snake_case				:    Tuple           =	image_processor(lowercase						,return_tensors="""pt"""					)

   snake_case				:    str           =	model(**lowercase					)

   print("""Logits:"""						,outputs.class_queries_logits[0, :3, :3]					)

   if model_name == "maskformer-swin-tiny-ade":
      snake_case				:    Union[str, Any]           =	torch.tensor(
          [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,lowercase						,atol=1E-4					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
      Path(lowercase					).mkdir(exist_ok=lowercase					)
      model.save_pretrained(lowercase					)
      image_processor.save_pretrained(lowercase					)

   if push_to_hub:
      print("""Pushing model and image processor to the hub..."""					)
      model.push_to_hub(f"""nielsr/{model_name}"""					)
      image_processor.push_to_hub(f"""nielsr/{model_name}"""					)


if __name__ == "__main__":
       lowerCamelCase	:	Optional[Any]				=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='maskformer-swin-tiny-ade',
           type=str,
           help=('Name of the MaskFormer model you\'d like to convert',),
       )
       parser.add_argument(
           '--checkpoint_path',
           default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
           type=str,
           help='Path to the original state dict (.pth file).',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       lowerCamelCase	:	List[str]				=   parser.parse_args()
       convert_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )





