



import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFEfficientFormerForImageClassification,
						    TFEfficientFormerForImageClassificationWithTeacher,
						    TFEfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_tf_efficientformer import (
						    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = 13			,	UpperCAmelCase__ = 64			,	UpperCAmelCase__ = 2			,	UpperCAmelCase__ = 3			,	UpperCAmelCase__ = 3			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = 128			,	UpperCAmelCase__=[16, 32, 64, 128]			,	UpperCAmelCase__ = 7			,	UpperCAmelCase__ = 4			,	UpperCAmelCase__ = 37			,	UpperCAmelCase__ = "gelu"			,	UpperCAmelCase__ = 0.1			,	UpperCAmelCase__ = 0.1			,	UpperCAmelCase__ = 10			,	UpperCAmelCase__ = 0.02			,	UpperCAmelCase__ = 2			,	UpperCAmelCase__ = 1			,	UpperCAmelCase__ = 128			,	UpperCAmelCase__ = [2, 2, 2, 2]			,	UpperCAmelCase__ = 2			,	UpperCAmelCase__ = 2			,	):
							A__							=       parent
							A__							=       batch_size
							A__							=       image_size
							A__							=       patch_size
							A__							=       num_channels
							A__							=       is_training
							A__							=       use_labels
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       hidden_dropout_prob
							A__							=       attention_probs_dropout_prob
							A__							=       type_sequence_label_size
							A__							=       initializer_range
							A__							=       encoder_stride
							A__							=       num_attention_outputs
							A__							=       embed_dim
							A__							=       embed_dim + 1
							A__							=       resolution
							A__							=       depths
							A__							=       hidden_sizes
							A__							=       dim
							A__							=       mlp_expansion_ratio



	def 			__A							(       self		):
							A__							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							A__							=       None
							if self.use_labels:
													A__							=       ids_tensor([self.batch_size]			,	self.type_sequence_label_size		)

							A__							=       self.get_config()

							return config, pixel_values, labels



	def 			__A							(       self		):
							return EfficientFormerConfig(
							    image_size=self.image_size			,	patch_size=self.patch_size			,	num_channels=self.num_channels			,	hidden_size=self.hidden_size			,	num_hidden_layers=self.num_hidden_layers			,	num_attention_heads=self.num_attention_heads			,	intermediate_size=self.intermediate_size			,	hidden_act=self.hidden_act			,	hidden_dropout_prob=self.hidden_dropout_prob			,	attention_probs_dropout_prob=self.attention_probs_dropout_prob			,	is_decoder=UpperCAmelCase__			,	initializer_range=self.initializer_range			,	encoder_stride=self.encoder_stride			,	resolution=self.resolution			,	depths=self.depths			,	hidden_sizes=self.hidden_sizes			,	dim=self.dim			,	mlp_expansion_ratio=self.mlp_expansion_ratio			,	)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TFEfficientFormerModel(config=UpperCAmelCase__		)
							A__							=       model(UpperCAmelCase__			,	training=UpperCAmelCase__		)
							self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.type_sequence_label_size
							A__							=       TFEfficientFormerForImageClassification(UpperCAmelCase__		)
							A__							=       model(UpperCAmelCase__			,	labels=UpperCAmelCase__			,	training=UpperCAmelCase__		)
							self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.type_sequence_label_size)		)

							# test greyscale images
							A__							=       1
							A__							=       TFEfficientFormerForImageClassification(UpperCAmelCase__		)

							A__							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							A__							=       model(UpperCAmelCase__			,	labels=UpperCAmelCase__		)
							self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.type_sequence_label_size)		)



	def 			__A							(       self		):
							A__							=       self.prepare_config_and_inputs()
							A__						,       A__						,       A__							=       config_and_inputs
							A__							=       {"pixel_values": pixel_values}
							return config, inputs_dict






@require_tf
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   List[str]				=	(
	    (
	        TFEfficientFormerModel,
	        TFEfficientFormerForImageClassificationWithTeacher,
	        TFEfficientFormerForImageClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase	:   List[str]				=	(
	    {
	        """feature-extraction""": TFEfficientFormerModel,
	        """image-classification""": (
	            TFEfficientFormerForImageClassification,
	            TFEfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_tf_available()
	    else {}
	)

	lowerCAmelCase	:   str				=	False

	lowerCAmelCase	:   str				=	False
	lowerCAmelCase	:   str				=	False
	lowerCAmelCase	:   int				=	False
	lowerCAmelCase	:   Union[str, Any]				=	False



	def 			__A							(       self		):
							A__							=       TFEfficientFormerModelTester(self		)
							A__							=       ConfigTester(
							    self			,	config_class=UpperCAmelCase__			,	has_text_modality=UpperCAmelCase__			,	hidden_size=37		)



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



	@unittest.skip(reason="EfficientFormer does not use inputs_embeds"		)
	def 			__A							(       self		):
							pass



	@unittest.skip(reason="EfficientFormer does not support input and output embeddings"		)
	def 			__A							(       self		):
							pass



	def 			__A							(       self		):
							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													A__							=       model_class(UpperCAmelCase__		)
													A__							=       inspect.signature(model.call		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													A__							=       [*signature.parameters.keys()]

													A__							=       ["pixel_values"]
													self.assertListEqual(arg_names[:1]			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							def check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       model_class(UpperCAmelCase__		)

													A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)			,	training=UpperCAmelCase__		)
													A__							=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A__							=       getattr(
													    self.model_tester			,	"expected_num_hidden_layers"			,	self.model_tester.num_hidden_layers + 1		)
													self.assertEqual(len(UpperCAmelCase__		)			,	UpperCAmelCase__		)

													if hasattr(self.model_tester			,	"encoder_seq_length"		):
																			A__							=       self.model_tester.encoder_seq_length
																			if hasattr(self.model_tester			,	"chunk_length"		) and self.model_tester.chunk_length > 1:
																									A__							=       seq_length * self.model_tester.chunk_length
													else:
																			A__							=       self.model_tester.seq_length

													self.assertListEqual(
													    list(hidden_states[-1].shape[-2:]		)			,	[seq_length, self.model_tester.hidden_size]			,	)

													if config.is_encoder_decoder:
																			A__							=       outputs.decoder_hidden_states

																			self.asseretIsInstance(UpperCAmelCase__			,	(list, tuple)		)
																			self.assertEqual(len(UpperCAmelCase__		)			,	UpperCAmelCase__		)
																			A__							=       getattr(self.model_tester			,	"seq_length"			,	UpperCAmelCase__		)
																			A__							=       getattr(self.model_tester			,	"decoder_seq_length"			,	UpperCAmelCase__		)

																			self.assertListEqual(
																			    list(hidden_states[-1].shape[-2:]		)			,	[decoder_seq_length, self.model_tester.hidden_size]			,	)

							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													A__							=       True
													check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													A__							=       True

													check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=False		):
							A__							=       super()._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__			,	return_labels=UpperCAmelCase__		)

							if return_labels:
													if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
																			del inputs_dict["labels"]

							return inputs_dict



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase__		)



	@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"		)
	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)



	@slow
	def 			__A							(       self		):
							for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A__							=       TFEfficientFormerModel.from_pretrained(UpperCAmelCase__		)
													self.assertIsNotNone(UpperCAmelCase__		)



	def 			__A							(       self		):
							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()
							A__							=       True

							A__							=       getattr(self.model_tester			,	"seq_length"			,	UpperCAmelCase__		)
							A__							=       getattr(self.model_tester			,	"encoder_seq_length"			,	UpperCAmelCase__		)
							A__							=       getattr(self.model_tester			,	"key_length"			,	UpperCAmelCase__		)
							A__							=       getattr(self.model_tester			,	"chunk_length"			,	UpperCAmelCase__		)

							if chunk_length is not None and hasattr(self.model_tester			,	"num_hashes"		):
													A__							=       encoder_seq_length * self.model_tester.num_hashes

							for model_class in self.all_model_classes:
													A__							=       True
													A__							=       False
													A__							=       True
													A__							=       model_class(UpperCAmelCase__		)

													A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)			,	training=UpperCAmelCase__		)
													A__							=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(UpperCAmelCase__		)			,	self.model_tester.num_attention_outputs		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													A__							=       True
													A__							=       model_class(UpperCAmelCase__		)
													A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)			,	training=UpperCAmelCase__		)

													A__							=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(UpperCAmelCase__		)			,	self.model_tester.num_attention_outputs		)

													if chunk_length is not None:
																			self.assertListEqual(
																			    list(attentions[0].shape[-4:]		)			,	[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]			,	)
													else:
																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]		)			,	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,	)



	def 			__A							(       self		):
							# We use a simplified version of this test for EfficientFormer because it requires training=False
							# and Keras refuses to let us force that during functional construction
							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													# Prepare our model
													A__							=       model_class(UpperCAmelCase__		)
													# These are maximally general inputs for the model, with multiple None dimensions
													# Hopefully this will catch any conditionals that fail for flexible shapes
													A__							=       {
													    key: tf.keras.Input(shape=val.shape[1:]			,	dtype=val.dtype			,	name=UpperCAmelCase__		)
													    for key, val in model.input_signature.items()
													    if key in model.dummy_inputs
													}
													A__							=       model(UpperCAmelCase__		)
													self.assertTrue(outputs_dict is not None		)





def  UpperCamelCase						(       )->       List[str]:





						"""simple docstring"""





						A__							=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image






@require_tf
@require_vision
class      UpperCamelCase    (						unittest.TestCase     ):



	@cached_property
	def 			__A							(       self		):
							return (
							    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"		)
							    if is_vision_available()
							    else None
							)



	@slow
	def 			__A							(       self		):
							A__							=       TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"		)
							A__							=       self.default_image_processor
							A__							=       prepare_img()
							A__							=       image_processor(images=UpperCAmelCase__			,	return_tensors="tf"		)
							# forward pass
							A__							=       model(**UpperCAmelCase__			,	training=UpperCAmelCase__		)
							# verify the logits
							A__							=       tf.TensorShape((1, 1_000)		)
							self.assertEqual(outputs.logits.shape			,	UpperCAmelCase__		)
							A__							=       tf.constant([-0.0_555, 0.4_825, -0.0_852]		)
							self.assertTrue(np.allclose(outputs.logits[0, :3]			,	UpperCAmelCase__			,	atol=1e-4		)		)



	@slow
	def 			__A							(       self		):
							A__							=       TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
							    "snap-research/efficientformer-l1-300"		)
							A__							=       self.default_image_processor
							A__							=       prepare_img()
							A__							=       image_processor(images=UpperCAmelCase__			,	return_tensors="tf"		)
							# forward pass
							A__							=       model(**UpperCAmelCase__			,	training=UpperCAmelCase__		)
							# verify the logits
							A__							=       tf.TensorShape((1, 1_000)		)
							self.assertEqual(outputs.logits.shape			,	UpperCAmelCase__		)
							A__							=       tf.constant([-0.1_312, 0.4_353, -1.0_499]		)
							self.assertTrue(np.allclose(outputs.logits[0, :3]			,	UpperCAmelCase__			,	atol=1e-4		)		)










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





