



'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
						from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor

class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self			:     List[Any]		,			__snake_case			:     Any		,			__snake_case			:     List[str]=3		,			__snake_case			:     str=32		,			__snake_case			:     Tuple=3		,			__snake_case			:     Dict=10		,			__snake_case			:     List[Any]=[10, 20, 30, 40]		,			__snake_case			:     List[Any]=[1, 1, 2, 1]		,			__snake_case			:     Any=True		,			__snake_case			:     Dict=True		,			__snake_case			:     Any="relu"		,			__snake_case			:     Union[str, Any]=3		,			__snake_case			:     List[str]=None		,			)			->		List[str]:
								UpperCAmelCase :       int 							=		parent
								UpperCAmelCase :       List[Any] 							=		batch_size
								UpperCAmelCase :       List[str] 							=		image_size
								UpperCAmelCase :       Optional[int] 							=		num_channels
								UpperCAmelCase :       List[str] 							=		embeddings_size
								UpperCAmelCase :       List[str] 							=		hidden_sizes
								UpperCAmelCase :       int 							=		depths
								UpperCAmelCase :       Union[str, Any] 							=		is_training
								UpperCAmelCase :       Dict 							=		use_labels
								UpperCAmelCase :       List[str] 							=		hidden_act
								UpperCAmelCase :       Union[str, Any] 							=		num_labels
								UpperCAmelCase :       str 							=		scope
								UpperCAmelCase :       str 							=		len(__snake_case		)







	def      A	(						self			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Optional[Any] 							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								UpperCAmelCase :       List[str] 							=		None
								if self.use_labels:
															UpperCAmelCase :       Union[str, Any] 							=		ids_tensor([self.batch_size]		,			self.num_labels		)

								UpperCAmelCase :       Optional[Any] 							=		self.get_config()

								return config, pixel_values, labels







	def      A	(						self			:     int		)			->		Tuple:
								return ResNetConfig(
								    num_channels=self.num_channels		,			embeddings_size=self.embeddings_size		,			hidden_sizes=self.hidden_sizes		,			depths=self.depths		,			hidden_act=self.hidden_act		,			num_labels=self.num_labels		,			image_size=self.image_size		,			)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     List[Any]		)			->		List[Any]:
								UpperCAmelCase :       int 							=		TFResNetModel(config=__snake_case		)
								UpperCAmelCase :       Tuple 							=		model(__snake_case		)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,			)







	def      A	(						self			:     List[str]		,			__snake_case			:     Optional[int]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     str		)			->		str:
								UpperCAmelCase :       List[Any] 							=		self.num_labels
								UpperCAmelCase :       Tuple 							=		TFResNetForImageClassification(__snake_case		)
								UpperCAmelCase :       Tuple 							=		model(__snake_case		,			labels=__snake_case		)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)		)







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

@require_tf
class 				SCREAMING_SNAKE_CASE(							A__							,		A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	lowerCamelCase__			 =       (
	    {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False







	def      A	(						self			:     Any		)			->		List[Any]:
								UpperCAmelCase :       List[Any] 							=		TFResNetModelTester(self		)
								UpperCAmelCase :       str 							=		ConfigTester(self		,			config_class=__snake_case		,			has_text_modality=__snake_case		)







	def      A	(						self			:     List[str]		)			->		int:
								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      A	(						self			:     Dict		)			->		Dict:
								return







	@unittest.skip(reason='''ResNet does not use inputs_embeds'''		)
	def      A	(						self			:     str		)			->		Dict:
								pass







	@unittest.skip(reason='''ResNet does not support input and output embeddings'''		)
	def      A	(						self			:     Dict		)			->		Any:
								pass







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

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

															UpperCAmelCase :       Any 							=		['''pixel_values''']
															self.assertListEqual(arg_names[:1]		,			__snake_case		)







	def      A	(						self			:     Any		)			->		List[Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__snake_case		)







	def      A	(						self			:     Dict		)			->		str:
								def check_hidden_states_output(__snake_case			:     int		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[int]		):
															UpperCAmelCase :       List[Any] 							=		model_class(__snake_case		)
															UpperCAmelCase :       List[str] 							=		model(**self._prepare_for_class(__snake_case		,			__snake_case		)		)

															UpperCAmelCase :       Optional[Any] 							=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															UpperCAmelCase :       List[str] 							=		self.model_tester.num_stages
															self.assertEqual(len(__snake_case		)		,			expected_num_stages + 1		)

															# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]		)		,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,			)

								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase :       List[str] 							=		['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
															for layer_type in layers_type:
																						UpperCAmelCase :       int 							=		layer_type
																						UpperCAmelCase :       Tuple 							=		True
																						check_hidden_states_output(__snake_case		,			__snake_case		,			__snake_case		)

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

																						check_hidden_states_output(__snake_case		,			__snake_case		,			__snake_case		)







	def      A	(						self			:     Tuple		)			->		int:
								UpperCAmelCase :       List[str] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__snake_case		)







	@slow
	def      A	(						self			:     str		)			->		Optional[Any]:
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase :       Optional[int] 							=		TFResNetModel.from_pretrained(__snake_case		)
															self.assertIsNotNone(__snake_case		)


def  snake_case_     (					)      ->   List[str]:
							UpperCAmelCase :       Union[str, Any] 							=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
							return image




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







	"""simple docstring"""






	@cached_property
	def      A	(						self			:     str		)			->		int:
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
								    if is_vision_available()
								    else None
								)







	@slow
	def      A	(						self			:     Dict		)			->		Optional[int]:
								UpperCAmelCase :       Optional[int] 							=		TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)

								UpperCAmelCase :       List[str] 							=		self.default_image_processor
								UpperCAmelCase :       Any 							=		prepare_img()
								UpperCAmelCase :       Tuple 							=		image_processor(images=__snake_case		,			return_tensors='''tf'''		)

								# forward pass
								UpperCAmelCase :       Dict 							=		model(**__snake_case		)

								# verify the logits
								UpperCAmelCase :       Dict 							=		tf.TensorShape((1, 1000)		)
								self.assertEqual(outputs.logits.shape		,			__snake_case		)

								UpperCAmelCase :       Any 							=		tf.constant([-11.10_69, -9.78_77, -8.37_77]		)

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










'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       ["""input_ids""", """attention_mask"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple="<s>"		,			__snake_case			:     List[Any]="</s>"		,			__snake_case			:     int="</s>"		,			__snake_case			:     Any="<s>"		,			__snake_case			:     Optional[int]="<unk>"		,			__snake_case			:     Union[str, Any]="<pad>"		,			__snake_case			:     Union[str, Any]="<mask>"		,			__snake_case			:     Optional[Dict[str, Any]] = None		,			**__snake_case			:     Dict		,			)			->		None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase :       int 							=		AddedToken(__snake_case		,			lstrip=__snake_case		,			rstrip=__snake_case		) if isinstance(__snake_case		,			__snake_case		) else mask_token

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__snake_case		,			eos_token=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			cls_token=__snake_case		,			pad_token=__snake_case		,			mask_token=__snake_case		,			sp_model_kwargs=self.sp_model_kwargs		,			**__snake_case		,			)

								UpperCAmelCase :       Optional[int] 							=		vocab_file
								UpperCAmelCase :       int 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(__snake_case		)		)

								UpperCAmelCase :       int 							=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase :       Union[str, Any] 							=		[self.cls_token_id]
								UpperCAmelCase :       Optional[Any] 							=		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case		)

								if token_ids_a is None:
															return [1] + ([0] * len(__snake_case		)) + [1]
								return [1] + ([0] * len(__snake_case		)) + [1, 1] + ([0] * len(__snake_case		)) + [1]







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







	@property
	def      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Optional[Any] 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, Any] 							=		False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

								UpperCAmelCase :       Any 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, Any] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)





