

"""simple docstring"""




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class    lowerCamelCase						(unittest.TestCase			):

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Union[str, Any]=1_3    ,				__UpperCAmelCase				:						Optional[Any]=3_0    ,				__UpperCAmelCase				:						Dict=2    ,				__UpperCAmelCase				:						Tuple=3    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						str=3_2    ,				__UpperCAmelCase				:						List[Any]=5    ,				__UpperCAmelCase				:						List[str]=4    ,				__UpperCAmelCase				:						Dict=3_7    ,				__UpperCAmelCase				:						List[Any]="gelu"    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=0.1    ,				__UpperCAmelCase				:						Optional[int]=1_0    ,				__UpperCAmelCase				:						List[str]=0.02    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__								=      (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE__								=      num_patches + 1

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						SCREAMING_SNAKE_CASE__								=      ViTConfig(
						    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    ,				)

						return config, pixel_values

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      FlaxViTModel(config=A_       )
						SCREAMING_SNAKE_CASE__								=      model(A_       )
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__								=      (self.image_size, self.image_size)
						SCREAMING_SNAKE_CASE__								=      (self.patch_size, self.patch_size)
						SCREAMING_SNAKE_CASE__								=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, num_patches + 1, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Tuple       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      FlaxViTForImageClassification(config=A_       )
						SCREAMING_SNAKE_CASE__								=      model(A_       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      FlaxViTForImageClassification(A_       )

						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
						SCREAMING_SNAKE_CASE__								=      model(A_       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__	
						)							,		(
						    SCREAMING_SNAKE_CASE__	
						)							,			
						)								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_flax
class    lowerCamelCase						(_UpperCAmelCase			,unittest.TestCase			):
			lowerCamelCase__   :		Any							=							(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      FlaxViTModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=A_    ,				has_text_modality=A_    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Any:
						self.config_tester.run_common_tests()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A_       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A_       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    List[Any]:
						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(A_       )
									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]    ,				A_       )

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

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__       ):
												SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(A_    ,				A_       )
												SCREAMING_SNAKE_CASE__								=      model_class(A_       )

												@jax.jit
												def model_jitted(__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						int       ):
															return model(pixel_values=A_    ,				**A_       )

												with self.subTest("""JIT Enabled"""       ):
															SCREAMING_SNAKE_CASE__								=      model_jitted(**A_       ).to_tuple()

												with self.subTest("""JIT Disabled"""       ):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE__								=      model_jitted(**A_       ).to_tuple()

												self.assertEqual(len(A_       )    ,				len(A_       )       )
												for jitted_output, output in zip(A_    ,				A_       ):
															self.assertEqual(jitted_output.shape    ,				output.shape       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Dict:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class_name.from_pretrained("""google/vit-base-patch16-224"""       )
									SCREAMING_SNAKE_CASE__								=      model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
									self.assertIsNotNone(A_       )





"""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


A_							:			Optional[int]											= logging.get_logger(__name__)

A_							:			Union[str, Any]											= {"vocab_file": "sentencepiece.bpe.model"}

A_							:			str											= {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

A_							:			Tuple											= {
    "camembert-base": 512,
}

A_							:			int											= "▁"





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		List[str]							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		Optional[int]							=							['input_ids', 'attention_mask']

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

						SCREAMING_SNAKE_CASE__								=      {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				mask_token=__UpperCAmelCase    ,				additional_special_tokens=__UpperCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCAmelCase    ,				)
						SCREAMING_SNAKE_CASE__								=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(str(__UpperCAmelCase       )       )
						SCREAMING_SNAKE_CASE__								=      vocab_file
						# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
						# sentencepiece vocabulary (this is the case for <s> and </s>
						SCREAMING_SNAKE_CASE__								=      {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
						SCREAMING_SNAKE_CASE__								=      len(self.fairseq_tokens_to_ids       )
						SCREAMING_SNAKE_CASE__								=      len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:

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

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						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 + sep + token_ids_a + sep       ) * [0]

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Any:
						return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      {self.convert_ids_to_tokens(__UpperCAmelCase       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						str       )   ->    List[str]:
						return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Tuple       )   ->    Optional[Any]:
						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						elif self.sp_model.PieceToId(__UpperCAmelCase       ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
						return self.fairseq_offset + self.sp_model.PieceToId(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						int       )   ->    Dict:
						if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      """"""
						SCREAMING_SNAKE_CASE__								=      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(__UpperCAmelCase       ) + token
												SCREAMING_SNAKE_CASE__								=      True
												SCREAMING_SNAKE_CASE__								=      []
									else:
												current_sub_tokens.append(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      False
						out_string += self.sp_model.decode(__UpperCAmelCase       )
						return out_string.strip()

			def __getstate__(					self				:						Optional[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.__dict__.copy()
						SCREAMING_SNAKE_CASE__								=      None
						return state

			def __setstate__(					self				:						int    ,				__UpperCAmelCase				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      d

						# for backward compatibility
						if not hasattr(self    ,				"""sp_model_kwargs"""       ):
									SCREAMING_SNAKE_CASE__								=      {}

						SCREAMING_SNAKE_CASE__								=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(self.vocab_file       )





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

						if os.path.abspath(self.vocab_file       ) != os.path.abspath(__UpperCAmelCase       ) and os.path.isfile(self.vocab_file       ):
									copyfile(self.vocab_file    ,				__UpperCAmelCase       )
						elif not os.path.isfile(self.vocab_file       ):
									with open(__UpperCAmelCase    ,				"""wb"""       ) as fi:
												SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
												fi.write(__UpperCAmelCase       )

						return (out_vocab_file,)


