




'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		)
		from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DeiTImageProcessor




class  _snake_case		:




			def __init__(					self				,							a__				,							a__=13				,							a__=30				,							a__=2				,							a__=3				,							a__=True				,							a__=True				,							a__=32				,							a__=2				,							a__=4				,							a__=37				,							a__="gelu"				,							a__=0.1				,							a__=0.1				,							a__=10				,							a__=0.0_2				,							a__=3				,							a__=None				,							a__=2				,							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      parent
										snake_case_						=      batch_size
										snake_case_						=      image_size
										snake_case_						=      patch_size
										snake_case_						=      num_channels
										snake_case_						=      is_training
										snake_case_						=      use_labels
										snake_case_						=      hidden_size
										snake_case_						=      num_hidden_layers
										snake_case_						=      num_attention_heads
										snake_case_						=      intermediate_size
										snake_case_						=      hidden_act
										snake_case_						=      hidden_dropout_prob
										snake_case_						=      attention_probs_dropout_prob
										snake_case_						=      type_sequence_label_size
										snake_case_						=      initializer_range
										snake_case_						=      scope
										snake_case_						=      encoder_stride

										# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
										snake_case_						=      (image_size // patch_size) ** 2
										snake_case_						=      num_patches + 2




			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										snake_case_						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

										snake_case_						=      self.get_config()

										return config, pixel_values, labels




			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										return DeiTConfig(
										    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=a__				,							initializer_range=self.initializer_range				,							encoder_stride=self.encoder_stride				,							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      TFDeiTModel(config=a__							)
										snake_case_						=      model(a__							)
										self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				str:

										'''simple docstring'''




										snake_case_						=      TFDeiTForMaskedImageModeling(config=a__							)
										snake_case_						=      model(a__							)
										self.parent.assertEqual(
										    result.reconstruction.shape				,							(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

										# test greyscale images
										snake_case_						=      1
										snake_case_						=      TFDeiTForMaskedImageModeling(a__							)

										snake_case_						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
										snake_case_						=      model(a__							)
										self.parent.assertEqual(result.reconstruction.shape				,							(self.batch_size, 1, self.image_size, self.image_size)							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				Union[str, Any]:

										'''simple docstring'''




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

										# test greyscale images
										snake_case_						=      1
										snake_case_						=      TFDeiTForImageClassification(a__							)

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




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




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




@require_tf
class  _snake_case		(	lowercase_						,						lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      Optional[Any]						       =    (
			    (
			        TFDeiTModel,
			        TFDeiTForImageClassification,
			        TFDeiTForImageClassificationWithTeacher,
			        TFDeiTForMaskedImageModeling,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCAmelCase_     :      List[Any]						       =    (
			    {
			        "feature-extraction": TFDeiTModel,
			        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCAmelCase_     :      List[Any]						       =    False
			lowerCAmelCase_     :      Optional[int]						       =    False
			lowerCAmelCase_     :      Optional[int]						       =    False
			lowerCAmelCase_     :      List[str]						       =    False




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      TFDeiTModelTester(self							)
										snake_case_						=      ConfigTester(self				,							config_class=a__				,							has_text_modality=a__				,							hidden_size=37							)




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										self.config_tester.run_common_tests()




			@unittest.skip(reason="DeiT does not use inputs_embeds"							)
			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										pass




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										snake_case_					,      snake_case_						=      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	snake_case_						=      model_class(a__							)
																	self.assertIsInstance(model.get_input_embeddings()				,							(tf.keras.layers.Layer)							)
																	snake_case_						=      model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(a__				,							tf.keras.layers.Dense							)							)




			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										snake_case_					,      snake_case_						=      self.model_tester.prepare_config_and_inputs_for_common()

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

																	snake_case_						=      ["pixel_values"]
																	self.assertListEqual(arg_names[:1]				,							a__							)




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a__							)




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*a__							)




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*a__							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__=False							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      super()._prepare_for_class(a__				,							a__				,							return_labels=a__							)

										if return_labels:
																	if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call							).parameters:
																								del inputs_dict["labels"]

										return inputs_dict




			@slow
			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_						=      TFDeiTModel.from_pretrained(a__							)
																	self.assertIsNotNone(a__							)


def      UpperCamelCase_(	):

							'''simple docstring'''






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




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




			@cached_property
			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										return (
										    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"							)
										    if is_vision_available()
										    else None
										)




			@slow
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"							)

										snake_case_						=      self.default_image_processor
										snake_case_						=      prepare_img()
										snake_case_						=      image_processor(images=a__				,							return_tensors="tf"							)

										# forward pass
										snake_case_						=      model(**a__							)

										# verify the logits
										snake_case_						=      tf.TensorShape((1, 1_000)							)
										self.assertEqual(outputs.logits.shape				,							a__							)

										snake_case_						=      tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]							)

										self.assertTrue(np.allclose(outputs.logits[0, :3]				,							a__				,							atol=1e-4							)							)










import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






