



'''simple docstring'''




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 		_UpperCamelCase						(							lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	List[Any]    					=	BarthezTokenizer
			_A   :	Optional[Any]    					=	BarthezTokenizerFast
			_A   :	Tuple    					=	True
			_A   :	List[str]    					=	True

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().setUp()

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""		)
					tokenizer.save_pretrained(self.tmpdirname		)
					tokenizer.save_pretrained(self.tmpdirname							,  legacy_format=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	"""<pad>"""
					__SCREAMING_SNAKE_CASE :	Any					 =	1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__		)							,  lowerCAmelCase__		)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__		)							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	list(self.get_tokenizer().get_vocab().keys()		)

					self.assertEqual(vocab_keys[0]							,  """<s>"""		)
					self.assertEqual(vocab_keys[1]							,  """<pad>"""		)
					self.assertEqual(vocab_keys[-1]							,  """<mask>"""		)
					self.assertEqual(len(lowerCAmelCase__		)							,  1_0_1_1_2_2		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,  1_0_1_1_2_2		)

			@require_torch
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					__SCREAMING_SNAKE_CASE :	Any					 =	[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

					__SCREAMING_SNAKE_CASE :	int					 =	self.tokenizer(
					    lowerCAmelCase__							,  max_length=len(lowerCAmelCase__		)							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  return_tensors="""pt"""		)
					self.assertIsInstance(lowerCAmelCase__							,  lowerCAmelCase__		)

					self.assertEqual((2, 6)							,  batch.input_ids.shape		)
					self.assertEqual((2, 6)							,  batch.attention_mask.shape		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	batch.input_ids.tolist()[0]
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					if not self.test_rust_tokenizer:
							return

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.get_tokenizer()
					__SCREAMING_SNAKE_CASE :	int					 =	self.get_rust_tokenizer()

					__SCREAMING_SNAKE_CASE :	List[str]					 =	"""I was born in 92000, and this is falsé."""

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	tokenizer.tokenize(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	rust_tokenizer.tokenize(lowerCAmelCase__		)
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	tokenizer.encode(lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	rust_tokenizer.encode(lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__		)
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_rust_tokenizer()
					__SCREAMING_SNAKE_CASE :	Dict					 =	tokenizer.encode(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	rust_tokenizer.encode(lowerCAmelCase__		)
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)



			@slow
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
					# fmt: on

					# moussaKam/mbarthez is a french model. So we also use french texts.
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[
					    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
					    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
					    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
					    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
					    """telles que la traduction et la synthèse de texte.""",
					]

					self.tokenizer_integration_test_util(
					    expected_encoding=lowerCAmelCase__							,  model_name="""moussaKam/mbarthez"""							,  revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""							,  sequences=lowerCAmelCase__							,  )




'''simple docstring'''




import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import CvtForImageClassification, CvtModel
						from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.config_class(**self.inputs_dict		)
					self.parent.assertTrue(hasattr(lowerCAmelCase__							,  """embed_dim"""		)		)
					self.parent.assertTrue(hasattr(lowerCAmelCase__							,  """num_heads"""		)		)






class 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				str							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				int=1_3							,  lowerCAmelCase__  :				Union[str, Any]=6_4							,  lowerCAmelCase__  :				Optional[Any]=3							,  lowerCAmelCase__  :				str=[1_6, 4_8, 9_6]							,  lowerCAmelCase__  :				Any=[1, 3, 6]							,  lowerCAmelCase__  :				Any=[1, 2, 1_0]							,  lowerCAmelCase__  :				Union[str, Any]=[7, 3, 3]							,  lowerCAmelCase__  :				Tuple=[4, 2, 2]							,  lowerCAmelCase__  :				Union[str, Any]=[2, 1, 1]							,  lowerCAmelCase__  :				Dict=[2, 2, 2]							,  lowerCAmelCase__  :				Optional[int]=[False, False, True]							,  lowerCAmelCase__  :				List[Any]=[0.0, 0.0, 0.0]							,  lowerCAmelCase__  :				List[Any]=0.02							,  lowerCAmelCase__  :				List[str]=1E-12							,  lowerCAmelCase__  :				Any=True							,  lowerCAmelCase__  :				Union[str, Any]=True							,  lowerCAmelCase__  :				Optional[Any]=2							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	parent
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	batch_size
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	image_size
					__SCREAMING_SNAKE_CASE :	Any					 =	patch_sizes
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	patch_stride
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	patch_padding
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	is_training
					__SCREAMING_SNAKE_CASE :	Dict					 =	use_labels
					__SCREAMING_SNAKE_CASE :	int					 =	num_labels
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	num_channels
					__SCREAMING_SNAKE_CASE :	List[str]					 =	embed_dim
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_heads
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	stride_kv
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	depth
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	cls_token
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	attention_drop_rate
					__SCREAMING_SNAKE_CASE :	str					 =	initializer_range
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	layer_norm_eps

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE :	Dict					 =	None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE :	int					 =	ids_tensor([self.batch_size]							,  self.num_labels		)

					__SCREAMING_SNAKE_CASE :	Any					 =	self.get_config()
					return config, pixel_values, labels

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					return CvtConfig(
					    image_size=self.image_size							,  num_labels=self.num_labels							,  num_channels=self.num_channels							,  embed_dim=self.embed_dim							,  num_heads=self.num_heads							,  patch_sizes=self.patch_sizes							,  patch_padding=self.patch_padding							,  patch_stride=self.patch_stride							,  stride_kv=self.stride_kv							,  depth=self.depth							,  cls_token=self.cls_token							,  attention_drop_rate=self.attention_drop_rate							,  initializer_range=self.initializer_range							,  )

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	CvtModel(config=lowerCAmelCase__		)
					model.to(lowerCAmelCase__		)
					model.eval()
					__SCREAMING_SNAKE_CASE :	int					 =	model(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	(self.image_size, self.image_size)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	image_size[0], image_size[1]
					for i in range(len(self.depth		)		):
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
							__SCREAMING_SNAKE_CASE :	Any					 =	floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
					self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.embed_dim[-1], height, width)		)

			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.num_labels
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	CvtForImageClassification(lowerCAmelCase__		)
					model.to(lowerCAmelCase__		)
					model.eval()
					__SCREAMING_SNAKE_CASE :	str					 =	model(lowerCAmelCase__							,  labels=lowerCAmelCase__		)
					self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)		)



			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Any					 =	config_and_inputs
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_torch
class 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	(CvtModel, CvtForImageClassification) if is_torch_available() else ()
			_A   :	Optional[Any]    					=	(
			    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
			    if is_torch_available()
			    else {}
			)

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

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	CvtModelTester(self		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	ConfigTester(self							,  config_class=lowerCAmelCase__							,  has_text_modality=lowerCAmelCase__							,  hidden_size=3_7		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					return

			@unittest.skip(reason="""Cvt does not output attentions"""		)
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Cvt does not use inputs_embeds"""		)
			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Cvt does not support input and output embeddings"""		)
			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					pass

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE :	Any					 =	model_class(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	inspect.signature(model.forward		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE :	int					 =	[*signature.parameters.keys()]

							__SCREAMING_SNAKE_CASE :	str					 =	["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					def check_hidden_states_output(lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Union[str, Any]		):
							__SCREAMING_SNAKE_CASE :	List[str]					 =	model_class(lowerCAmelCase__		)
							model.to(lowerCAmelCase__		)
							model.eval()

							with torch.no_grad():
									__SCREAMING_SNAKE_CASE :	Dict					 =	model(**self._prepare_for_class(lowerCAmelCase__							,  lowerCAmelCase__		)		)

							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	outputs.hidden_states

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	len(self.model_tester.depth		)
							self.assertEqual(len(lowerCAmelCase__		)							,  lowerCAmelCase__		)

							# verify the first hidden states (first block)
							self.assertListEqual(
							    list(hidden_states[0].shape[-3:]		)							,  [
							        self.model_tester.embed_dim[0],
							        self.model_tester.image_size // 4,
							        self.model_tester.image_size // 4,
							    ]							,  )

					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	True
							check_hidden_states_output(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	True

							check_hidden_states_output(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__		)

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					pass



			@slow
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	CvtModel.from_pretrained(lowerCAmelCase__		)
							self.assertIsNotNone(lowerCAmelCase__		)


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	str					 =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
		return image






@require_torch
@require_vision
class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)



			@slow
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.default_image_processor
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	prepare_img()
					__SCREAMING_SNAKE_CASE :	int					 =	image_processor(images=lowerCAmelCase__							,  return_tensors="""pt"""		).to(lowerCAmelCase__		)

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	model(**lowerCAmelCase__		)

					# verify the logits
					__SCREAMING_SNAKE_CASE :	str					 =	torch.Size((1, 1_0_0_0)		)
					self.assertEqual(outputs.logits.shape							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.tensor([0.92_85, 0.90_15, -0.31_50]		).to(lowerCAmelCase__		)

					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,  lowerCAmelCase__							,  atol=1E-4		)		)