



"""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_=1_3			,     a_=3_0			,     a_=2			,     a_=3			,     a_=True			,     a_=True			,     a_=3_2			,     a_=2			,     a_=4			,     a_=3_7			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=1_0			,     a_=0.02			,     a_=3			,     a_=None			,     a_=2			,     ):
									a_     :      Any 				=     parent
									a_     :      List[Any] 				=     batch_size
									a_     :      Optional[int] 				=     image_size
									a_     :      Optional[int] 				=     patch_size
									a_     :      Tuple 				=     num_channels
									a_     :      Union[str, Any] 				=     is_training
									a_     :      Optional[int] 				=     use_labels
									a_     :      List[Any] 				=     hidden_size
									a_     :      Optional[Any] 				=     num_hidden_layers
									a_     :      Dict 				=     num_attention_heads
									a_     :      Any 				=     intermediate_size
									a_     :      int 				=     hidden_act
									a_     :      Tuple 				=     hidden_dropout_prob
									a_     :      Tuple 				=     attention_probs_dropout_prob
									a_     :      Tuple 				=     type_sequence_label_size
									a_     :      List[Any] 				=     initializer_range
									a_     :      Tuple 				=     scope
									a_     :      List[str] 				=     encoder_stride

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






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 				=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									a_     :      Dict 				=     None
									if self.use_labels:
											a_     :      Any 				=     ids_tensor([self.batch_size]			,     self.type_sequence_label_size      )

									a_     :      int 				=     self.get_config()

									return config, pixel_values, labels






							def snake_case_  (   self      ):
									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 snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      List[Any] 				=     TFDeiTModel(config=_A      )
									a_     :      Dict 				=     model(_A      )
									self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      List[Any] 				=     TFDeiTForMaskedImageModeling(config=_A      )
									a_     :      Tuple 				=     model(_A      )
									self.parent.assertEqual(
									    result.reconstruction.shape			,     (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

									# test greyscale images
									a_     :      Optional[int] 				=     1
									a_     :      List[Any] 				=     TFDeiTForMaskedImageModeling(_A      )

									a_     :      List[str] 				=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									a_     :      Tuple 				=     model(_A      )
									self.parent.assertEqual(result.reconstruction.shape			,     (self.batch_size, 1, self.image_size, self.image_size)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Any 				=     self.type_sequence_label_size
									a_     :      str 				=     TFDeiTForImageClassification(_A      )
									a_     :      Dict 				=     model(_A			,     labels=_A      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )

									# test greyscale images
									a_     :      Optional[Any] 				=     1
									a_     :      Any 				=     TFDeiTForImageClassification(_A      )

									a_     :      Tuple 				=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									a_     :      Union[str, Any] 				=     model(_A			,     labels=_A      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )






							def snake_case_  (   self      ):
									a_     :      List[str] 				=     self.prepare_config_and_inputs()
									a_				,				a_				,				a_     :      Optional[int] 				=     config_and_inputs
									a_     :      Tuple 				=     {"pixel_values": pixel_values}
									return config, inputs_dict




@require_tf
class 			snake_case_						(						__lowercase     ,__lowercase     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(
							    (
							        TFDeiTModel,
							        TFDeiTForImageClassification,
							        TFDeiTForImageClassificationWithTeacher,
							        TFDeiTForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							__lowerCAmelCase    					=					(
							    {
							        "feature-extraction": TFDeiTModel,
							        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
							    }
							    if is_tf_available()
							    else {}
							)

							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									a_     :      Any 				=     TFDeiTModelTester(self      )
									a_     :      Tuple 				=     ConfigTester(self			,     config_class=_A			,     has_text_modality=_A			,     hidden_size=3_7      )






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






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






							def snake_case_  (   self      ):
									a_				,				a_     :      int 				=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      Optional[int] 				=     model_class(_A      )
											self.assertIsInstance(model.get_input_embeddings()			,     (tf.keras.layers.Layer)      )
											a_     :      List[str] 				=     model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_A			,     tf.keras.layers.Dense      )      )






							def snake_case_  (   self      ):
									a_				,				a_     :      List[Any] 				=     self.model_tester.prepare_config_and_inputs_for_common()

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

											a_     :      List[Any] 				=     ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,     _A      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 				=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 				=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*_A      )






							def snake_case_  (   self      ):
									a_     :      Dict 				=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_A      )






							def snake_case_  (   self			,     a_			,     a_			,     a_=False      ):
									a_     :      List[Any] 				=     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 snake_case_  (   self      ):
									for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_     :      Optional[Any] 				=     TFDeiTModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )



def        lowerCAmelCase_						(							)			->      Optional[int]:
		a_     :      Tuple 				=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image




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






							@cached_property
							def snake_case_  (   self      ):
									return (
									    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
									    if is_vision_available()
									    else None
									)






							@slow
							def snake_case_  (   self      ):
									a_     :      Any 				=     TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

									a_     :      Optional[int] 				=     self.default_image_processor
									a_     :      Any 				=     prepare_img()
									a_     :      Any 				=     image_processor(images=_A			,     return_tensors="tf"      )

									# forward pass
									a_     :      int 				=     model(**_A      )

									# verify the logits
									a_     :      Tuple 				=     tf.TensorShape((1, 1_0_0_0)      )
									self.assertEqual(outputs.logits.shape			,     _A      )

									a_     :      Optional[int] 				=     tf.constant([-1.0_266, 0.1_912, -1.2_861]      )

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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)