




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE:     Optional[Any]									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     List[Any]									= {'''vocab_file''': '''sentencepiece.model'''}

SCREAMING_SNAKE_CASE:     Tuple									= {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

SCREAMING_SNAKE_CASE:     Dict									= {
    '''google/rembert''': 2_5_6,
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =VOCAB_FILES_NAMES
						lowerCAmelCase__			      =PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__			      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   str					,							snake_case__	:   List[str]=False					,							snake_case__	:   Optional[int]=True					,							snake_case__	:   Tuple=True					,							snake_case__	:   List[Any]="[CLS]"					,							snake_case__	:   int="[SEP]"					,							snake_case__	:   Any="[UNK]"					,							snake_case__	:   Dict="[SEP]"					,							snake_case__	:   List[str]="[PAD]"					,							snake_case__	:   int="[CLS]"					,							snake_case__	:   Optional[Any]="[MASK]"					,							**snake_case__	:   Tuple					,							):

								"""simple docstring"""

								super().__init__(
								    do_lower_case=snake_case__					,							remove_space=snake_case__					,							keep_accents=snake_case__					,							bos_token=snake_case__					,							eos_token=snake_case__					,							unk_token=snake_case__					,							sep_token=snake_case__					,							pad_token=snake_case__					,							cls_token=snake_case__					,							mask_token=snake_case__					,							**snake_case__					,							)

								SCREAMING_SNAKE_CASE_					      =							do_lower_case
								SCREAMING_SNAKE_CASE_					      =							remove_space
								SCREAMING_SNAKE_CASE_					      =							keep_accents
								SCREAMING_SNAKE_CASE_					      =							vocab_file

								SCREAMING_SNAKE_CASE_					      =							spm.SentencePieceProcessor()
								self.sp_model.Load(snake_case__  )



						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return len(self.sp_model  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{self.convert_ids_to_tokens(snake_case__  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab



						def __getstate__(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.__dict__.copy()
								SCREAMING_SNAKE_CASE_					      =							None
								return state



						def __setstate__(			self	:   Union[str, Any]					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							d
								SCREAMING_SNAKE_CASE_					      =							spm.SentencePieceProcessor()
								self.sp_model.Load(self.vocab_file  )



						def 	__a					(			self	:   int					,							snake_case__	:   Any					,							snake_case__	:   List[Any]=False  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.sp_model.EncodeAsPieces(snake_case__  )
								return pieces



						def 	__a					(			self	:   str					,							snake_case__	:   Any  ):

								"""simple docstring"""

								return self.sp_model.PieceToId(snake_case__  )



						def 	__a					(			self	:   Dict					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.sp_model.IdToPiece(snake_case__  )



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.sp_model.decode_pieces(snake_case__  )
								return out_string



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   List[int]					,							snake_case__	:   Optional[List[int]] = None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[self.sep_token_id]
								SCREAMING_SNAKE_CASE_					      =							[self.cls_token_id]
								if token_ids_a is None:
										return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



						def 	__a					(			self	:   Tuple					,							snake_case__	:   List[int]					,							snake_case__	:   Optional[List[int]] = None					,							snake_case__	:   bool = False  ):

								"""simple docstring"""

								if already_has_special_tokens:
										if token_ids_a is not None:
												raise ValueError(
												    'You should not supply a second sequence if the provided sequence of '
												    'ids is already formatted with special tokens for the model.'  )
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
										return [1] + ([0] * len(snake_case__  )) + [1] + ([0] * len(snake_case__  )) + [1]
								return [1] + ([0] * len(snake_case__  )) + [1]



						def 	__a					(			self	:   Dict					,							snake_case__	:   List[int]					,							snake_case__	:   Optional[List[int]] = None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[self.sep_token_id]
								SCREAMING_SNAKE_CASE_					      =							[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  ) * [0] + len(token_ids_a + sep  ) * [1]






						def 	__a					(			self	:   Dict					,							snake_case__	:   str					,							snake_case__	:   Optional[str] = None  ):

								"""simple docstring"""

								if not os.path.isdir(snake_case__  ):
										logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__  )  )
										return
								SCREAMING_SNAKE_CASE_					      =							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__  ):
										copyfile(self.vocab_file					,							snake_case__  )

								return (out_vocab_file,)





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    lowercase_		:



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   Tuple=3					,							snake_case__	:   Union[str, Any]=32					,							snake_case__	:   Union[str, Any]=3					,							snake_case__	:   str=10					,							snake_case__	:   List[Any]=[10, 20, 30, 40]					,							snake_case__	:   Optional[Any]=[1, 1, 2, 1]					,							snake_case__	:   str=True					,							snake_case__	:   str=True					,							snake_case__	:   List[str]="relu"					,							snake_case__	:   Tuple=3					,							snake_case__	:   str=None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							parent
								SCREAMING_SNAKE_CASE_					      =							batch_size
								SCREAMING_SNAKE_CASE_					      =							image_size
								SCREAMING_SNAKE_CASE_					      =							num_channels
								SCREAMING_SNAKE_CASE_					      =							embeddings_size
								SCREAMING_SNAKE_CASE_					      =							hidden_sizes
								SCREAMING_SNAKE_CASE_					      =							depths
								SCREAMING_SNAKE_CASE_					      =							is_training
								SCREAMING_SNAKE_CASE_					      =							use_labels
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							num_labels
								SCREAMING_SNAKE_CASE_					      =							scope
								SCREAMING_SNAKE_CASE_					      =							len(snake_case__  )



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								SCREAMING_SNAKE_CASE_					      =							None
								if self.use_labels:
										SCREAMING_SNAKE_CASE_					      =							ids_tensor([self.batch_size]					,							self.num_labels  )

								SCREAMING_SNAKE_CASE_					      =							self.get_config()

								return config, pixel_values, labels



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								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	:   str					,							snake_case__	:   str					,							snake_case__	:   Tuple					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModel(config=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							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	:   int					,							snake_case__	:   Optional[Any]					,							snake_case__	:   int					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.num_labels
								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							labels=snake_case__  )
								self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.num_labels)  )






						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							config_and_inputs
								SCREAMING_SNAKE_CASE_					      =							{'pixel_values': pixel_values}
								return config, inputs_dict



@require_tf
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						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	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModelTester(self  )
								SCREAMING_SNAKE_CASE_					      =							ConfigTester(self					,							config_class=snake_case__					,							has_text_modality=snake_case__  )



						def 	__a					(			self	:   Optional[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 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								return



						@unittest.skip(reason='ResNet does not use inputs_embeds'  )
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								pass



						@unittest.skip(reason='ResNet does not support input and output embeddings'  )
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								pass



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()

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

										SCREAMING_SNAKE_CASE_					      =							['pixel_values']
										self.assertListEqual(arg_names[:1]					,							snake_case__  )



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								def check_hidden_states_output(snake_case__	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   Dict  ):
										SCREAMING_SNAKE_CASE_					      =							model_class(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							model(**self._prepare_for_class(snake_case__					,							snake_case__  )  )

										SCREAMING_SNAKE_CASE_					      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										SCREAMING_SNAKE_CASE_					      =							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]					,							)

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE_					      =							['basic', 'bottleneck']
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												SCREAMING_SNAKE_CASE_					      =							layer_type
												SCREAMING_SNAKE_CASE_					      =							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"]
												SCREAMING_SNAKE_CASE_					      =							True

												check_hidden_states_output(snake_case__					,							snake_case__					,							snake_case__  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case__  )






						@slow
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_					      =							TFResNetModel.from_pretrained(snake_case__  )
										self.assertIsNotNone(snake_case__  )


def 					_a							(					)->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image




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



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

								"""simple docstring"""

								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
								    if is_vision_available()
								    else None
								)






						@slow
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

								SCREAMING_SNAKE_CASE_					      =							self.default_image_processor
								SCREAMING_SNAKE_CASE_					      =							prepare_img()
								SCREAMING_SNAKE_CASE_					      =							image_processor(images=snake_case__					,							return_tensors='tf'  )

								# forward pass
								SCREAMING_SNAKE_CASE_					      =							model(**snake_case__  )

								# verify the logits
								SCREAMING_SNAKE_CASE_					      =							tf.TensorShape((1, 10_00)  )
								self.assertEqual(outputs.logits.shape					,							snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							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  )  )