
"""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  lowerCAmelCase_ :



						'''simple docstring'''


						def __init__(       self   :					Optional[Any]     ,A_   :					Optional[Any]     ,A_   :					Optional[Any]=13     ,A_   :					List[Any]=30     ,A_   :					Optional[Any]=2     ,A_   :					Optional[int]=3     ,A_   :					int=True     ,A_   :					Optional[Any]=True     ,A_   :					List[str]=32     ,A_   :					str=2     ,A_   :					str=4     ,A_   :					int=37     ,A_   :					Tuple="gelu"     ,A_   :					Any=0.1     ,A_   :					int=0.1     ,A_   :					str=10     ,A_   :					List[str]=0.02     ,A_   :					int=3     ,A_   :					List[Any]=None     ,A_   :					int=2     ,)   ->      Dict:
							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        =      scope
							A        =      encoder_stride

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Union[str, Any]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      List[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=A_     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,A_   :					Any     ,A_   :					Any     ,A_   :					int					)   ->      int:
							A        =      TFDeiTModel(config=A_					)
							A        =      model(A_					)
							self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					List[str]     ,A_   :					List[str]     ,A_   :					Optional[Any]					)   ->      List[Any]:
							A        =      TFDeiTForMaskedImageModeling(config=A_					)
							A        =      model(A_					)
							self.parent.assertEqual(
							    result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

							# test greyscale images
							A        =      1
							A        =      TFDeiTForMaskedImageModeling(A_					)

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Union[str, Any]     ,A_   :					str     ,A_   :					Tuple					)   ->      List[Any]:
							A        =      self.type_sequence_label_size
							A        =      TFDeiTForImageClassification(A_					)
							A        =      model(A_     ,labels=A_					)
							self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)					)

							# test greyscale images
							A        =      1
							A        =      TFDeiTForImageClassification(A_					)

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




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Any:
							A        =      self.prepare_config_and_inputs()
							A      ,					A      ,					A        =      config_and_inputs
							A        =      {'pixel_values': pixel_values}
							return config, inputs_dict







@require_tf
class  lowerCAmelCase_ (					_lowercase						,    _lowercase						,    unittest.TestCase			):



						'''simple docstring'''


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

						_lowerCamelCase:			Union[str, Any]					=			False
						_lowerCamelCase:			Dict					=			False
						_lowerCamelCase:			Any					=			False
						_lowerCamelCase:			Dict					=			False


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Tuple:
							A        =      TFDeiTModelTester(self					)
							A        =      ConfigTester(self     ,config_class=A_     ,has_text_modality=A_     ,hidden_size=37					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Optional[int]:
							self.config_tester.run_common_tests()


						@unittest.skip(reason='DeiT does not use inputs_embeds'					)
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      str:
							pass


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Dict:
							A      ,					A        =      self.model_tester.prepare_config_and_inputs_for_common()

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      List[Any]:
							A      ,					A        =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A        =      model_class(A_					)
								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]     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Any:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Optional[Any]:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      int:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,A_   :					List[str]     ,A_   :					Any     ,A_   :					List[Any]=False					)   ->      str:
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      List[str]:
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A        =      TFDeiTModel.from_pretrained(A_					)
								self.assertIsNotNone(A_					)






def 					_snake_case (						):
	A        =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
	return image







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



						'''simple docstring'''


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




						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Any:
							A        =      TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'					)

							A        =      self.default_image_processor
							A        =      prepare_img()
							A        =      image_processor(images=A_     ,return_tensors='tf'					)

							# forward pass
							A        =      model(**A_					)

							# verify the logits
							A        =      tf.TensorShape((1, 1000)					)
							self.assertEqual(outputs.logits.shape     ,A_					)

							A        =      tf.constant([-1.02_66, 0.19_12, -1.28_61]					)

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





'''simple docstring'''







from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('''Unable to interleave an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."     )
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

    if not dsets:
        raise ValueError('''Unable to concatenate an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )
    else:
        return _concatenate_iterable_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )




