
"""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					)					)

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'convbert'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=1						,   _a=0						,   _a=2						,   _a=768						,   _a=2						,   _a=9						,   _a=1						,   _a=None						,   **_a						,   ):
				super().__init__(
				    pad_token_id=_a						,   bos_token_id=_a						,   eos_token_id=_a						,   **_a						,   )

				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     hidden_size
				__magic_name__		:		Union[str, Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		str	   =     intermediate_size
				__magic_name__		:		Any	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     max_position_embeddings
				__magic_name__		:		str	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     initializer_range
				__magic_name__		:		Tuple	   =     layer_norm_eps
				__magic_name__		:		List[Any]	   =     embedding_size
				__magic_name__		:		List[Any]	   =     head_ratio
				__magic_name__		:		str	   =     conv_kernel_size
				__magic_name__		:		Dict	   =     num_groups
				__magic_name__		:		str	   =     classifier_dropout







class       _snake_case					(     snake_case				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		Dict	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Dict	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)
