






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 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=13		,				_SCREAMING_SNAKE_CASE=30		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=37		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=2		,				) ->				List[Any]:
								snake_case_				:							int						 =       parent
								snake_case_				:							Any						 =       batch_size
								snake_case_				:							Any						 =       image_size
								snake_case_				:							str						 =       patch_size
								snake_case_				:							int						 =       num_channels
								snake_case_				:							int						 =       is_training
								snake_case_				:							Any						 =       use_labels
								snake_case_				:							Optional[int]						 =       hidden_size
								snake_case_				:							int						 =       num_hidden_layers
								snake_case_				:							List[Any]						 =       num_attention_heads
								snake_case_				:							int						 =       intermediate_size
								snake_case_				:							Union[str, Any]						 =       hidden_act
								snake_case_				:							Union[str, Any]						 =       hidden_dropout_prob
								snake_case_				:							Tuple						 =       attention_probs_dropout_prob
								snake_case_				:							Optional[int]						 =       type_sequence_label_size
								snake_case_				:							Optional[Any]						 =       initializer_range
								snake_case_				:							str						 =       scope
								snake_case_				:							List[Any]						 =       encoder_stride

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






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							Union[str, Any]						 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

								snake_case_				:							int						 =       self.get_config()

								return config, pixel_values, labels






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								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=_SCREAMING_SNAKE_CASE		,				initializer_range=self.initializer_range		,				encoder_stride=self.encoder_stride		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							str						 =       TFDeiTModel(config=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Optional[Any]						 =       TFDeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       model(_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(
								    result.reconstruction.shape		,				(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

								# test greyscale images
								snake_case_				:							List[Any]						 =       1
								snake_case_				:							Dict						 =       TFDeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Union[str, Any]						 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
								snake_case_				:							Optional[Any]						 =       model(_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.reconstruction.shape		,				(self.batch_size, 1, self.image_size, self.image_size)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								snake_case_				:							Dict						 =       self.type_sequence_label_size
								snake_case_				:							List[str]						 =       TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)				)

								# test greyscale images
								snake_case_				:							Dict						 =       1
								snake_case_				:							Union[str, Any]						 =       TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
								snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Union[str, Any]						 =       self.prepare_config_and_inputs()
								snake_case_				:							Dict						 =       config_and_inputs
								snake_case_				:							Optional[int]						 =       {"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Tuple						=					(
						    (
						        TFDeiTModel,
						        TFDeiTForImageClassification,
						        TFDeiTForImageClassificationWithTeacher,
						        TFDeiTForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						A     :   Dict						=					(
						    {
						        'feature-extraction': TFDeiTModel,
						        'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
						    }
						    if is_tf_available()
						    else {}
						)

						A     :   Optional[int]						=					False
						A     :   Union[str, Any]						=					False
						A     :   List[str]						=					False
						A     :   int						=					False






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							Optional[Any]						 =       TFDeiTModelTester(self				)
								snake_case_				:							Union[str, Any]						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE		,				hidden_size=37				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								self.config_tester.run_common_tests()






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






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs_for_common()

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






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							Tuple						 =       self.model_tester.prepare_config_and_inputs_for_common()

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

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






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Optional[int]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Optional[int]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Optional[Any]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=False				) ->				Any:
								snake_case_				:							int						 =       super()._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)

								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				) ->				Optional[Any]:
								for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case_				:							Any						 =       TFDeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		snake_case_				:							List[Any]						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




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


						'''simple docstring'''




						@cached_property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return (
								    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
								    if is_vision_available()
								    else None
								)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Dict						 =       TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				)

								snake_case_				:							Any						 =       self.default_image_processor
								snake_case_				:							Union[str, Any]						 =       prepare_img()
								snake_case_				:							Any						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="tf"				)

								# forward pass
								snake_case_				:							Optional[Any]						 =       model(**_SCREAMING_SNAKE_CASE				)

								# verify the logits
								snake_case_				:							Tuple						 =       tf.TensorShape((1, 1000)				)
								self.assertEqual(outputs.logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Union[str, Any]						 =       tf.constant([-1.0266, 0.1912, -1.2861]				)

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
