





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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_ta import TaTokenizer
else:
						_SCREAMING_SNAKE_CASE      	=		None


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE      	=		{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_SCREAMING_SNAKE_CASE      	=		{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Optional[int]       =       VOCAB_FILES_NAMES
						__magic_name__:			int       =       PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:			List[str]       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:			List[str]       =       ["input_ids", "attention_mask"]
						__magic_name__:			Union[str, Any]       =       TaTokenizer

						__magic_name__:			List[int]       =       []






						def __init__(      self      :      Optional[int]		,   _A      :      Optional[Any]=None		,   _A      :      Union[str, Any]=None		,   _A      :      int="</s>"		,   _A      :      Optional[Any]="<unk>"		,   _A      :      Dict="<pad>"		,   _A      :      Optional[Any]=100		,   _A      :      Any=None		,   **_A      :      int		,   )							->    Tuple:







							"""simple docstring"""


							if extra_ids > 0 and additional_special_tokens is None:
								snake_case_		:			Union[str, Any]											=	[F"""<extra_id_{i}>""" for i in range(_A	)]
							elif extra_ids > 0 and additional_special_tokens is not None:
								# Check that we have the right number of extra special tokens
								snake_case_		:			Optional[int]											=	len(set(filter(lambda _A	: bool('extra_id_' in str(_A	)	)		,   _A	)	)	)
								if extra_tokens != extra_ids:
									raise ValueError(
									    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
									    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
									    ' tokens'	)

							super().__init__(
							    _A		,   tokenizer_file=_A		,   eos_token=_A		,   unk_token=_A		,   pad_token=_A		,   extra_ids=_A		,   additional_special_tokens=_A		,   **_A		,   )

							snake_case_		:			str											=	vocab_file
							snake_case_		:			Union[str, Any]											=	False if not self.vocab_file else True
							snake_case_		:			int											=	extra_ids






						@staticmethod
						def UpperCAmelCase_				(      _A      :      List[str]		,   _A      :      Optional[Any]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
								snake_case_		:			Optional[int]											=	TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
								if init_max_model_length is not None and init_max_model_length != max_model_length:
									return init_max_model_length
								elif init_max_model_length is None:
									warnings.warn(
									    'This tokenizer was incorrectly instantiated with a model max length of'
									    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
									    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
									    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
									    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
									    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
									    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
									    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
									    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'		,   _A		,   )

							return max_model_length






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      str		,   _A      :      Optional[str] = None	)							->    Tuple[str]:







							"""simple docstring"""


							if not self.can_save_slow_tokenizer:
								raise ValueError(
								    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
								    'tokenizer.'	)

							if not os.path.isdir(_A	):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
								return
							snake_case_		:			Any											=	os.path.join(
							    _A		,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

							if os.path.abspath(self.vocab_file	) != os.path.abspath(_A	):
								copyfile(self.vocab_file		,   _A	)
								logger.info(F"""Copy vocab file to {out_vocab_file}"""	)

							return (out_vocab_file,)






						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			int											=	token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
								return self.prefix_tokens + token_ids_a
							else:
								snake_case_		:			Tuple											=	token_ids_a + [self.eos_token_id]
								return self.prefix_tokens + token_ids_a + token_ids_a






						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			Any											=	[self.eos_token_id]

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






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[Any]:







							"""simple docstring"""


							return list(
							    set(filter(lambda _A	: bool(re.search(R'<extra_id_\d+>'		,   _A	)	) is not None		,   self.additional_special_tokens	)	)	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    str:







							"""simple docstring"""


							return [self.convert_tokens_to_ids(_A	) for token in self.get_sentinel_tokens()]








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 SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def __init__(      self      :      Dict		,   _A      :      List[str]		,   _A      :      Dict=13		,   _A      :      str=30		,   _A      :      Any=2		,   _A      :      Dict=3		,   _A      :      Optional[Any]=True		,   _A      :      Tuple=True		,   _A      :      List[str]=32		,   _A      :      int=5		,   _A      :      Optional[int]=4		,   _A      :      Dict=37		,   _A      :      Tuple="gelu"		,   _A      :      Dict=0.1		,   _A      :      str=0.1		,   _A      :      int=10		,   _A      :      Union[str, Any]=0.0_2		,   )							->    int:







							"""simple docstring"""


							snake_case_		:			int											=	parent
							snake_case_		:			str											=	batch_size
							snake_case_		:			List[str]											=	image_size
							snake_case_		:			Tuple											=	patch_size
							snake_case_		:			str											=	num_channels
							snake_case_		:			List[str]											=	is_training
							snake_case_		:			List[str]											=	use_labels
							snake_case_		:			str											=	hidden_size
							snake_case_		:			Union[str, Any]											=	num_hidden_layers
							snake_case_		:			Any											=	num_attention_heads
							snake_case_		:			List[Any]											=	intermediate_size
							snake_case_		:			Any											=	hidden_act
							snake_case_		:			List[Any]											=	hidden_dropout_prob
							snake_case_		:			List[Any]											=	attention_probs_dropout_prob
							snake_case_		:			Tuple											=	type_sequence_label_size
							snake_case_		:			Any											=	initializer_range

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






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

							snake_case_		:			Optional[Any]											=	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 UpperCAmelCase_				(      self      :      int		,   _A      :      Union[str, Any]		,   _A      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	FlaxViTModel(config=_A	)
							snake_case_		:			Optional[Any]											=	model(_A	)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							snake_case_		:			Union[str, Any]											=	(self.image_size, self.image_size)
							snake_case_		:			int											=	(self.patch_size, self.patch_size)
							snake_case_		:			Optional[Any]											=	(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 UpperCAmelCase_				(      self      :      str		,   _A      :      Any		,   _A      :      List[str]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	self.type_sequence_label_size
							snake_case_		:			Any											=	FlaxViTForImageClassification(config=_A	)
							snake_case_		:			Optional[int]											=	model(_A	)
							self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.type_sequence_label_size)	)

							# test greyscale images
							snake_case_		:			List[str]											=	1
							snake_case_		:			Dict											=	FlaxViTForImageClassification(_A	)

							snake_case_		:			Optional[int]											=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
							snake_case_		:			Any											=	model(_A	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)						,(
							    snake_case_
							)						,
							)		:			Any											=	config_and_inputs
							snake_case_		:			Tuple											=	{'pixel_values': pixel_values}
							return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Tuple       =       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






						def UpperCAmelCase_				(      self      :      List[str]	)							->    None:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	FlaxViTModelTester(self	)
							snake_case_		:			Any											=	ConfigTester(self		,   config_class=_A		,   has_text_modality=_A		,   hidden_size=37	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Dict:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_A	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Tuple											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		:			str											=	model_class(_A	)
								snake_case_		:			Dict											=	inspect.signature(model.__call__	)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								snake_case_		:			Optional[Any]											=	[*signature.parameters.keys()]

								snake_case_		:			Dict											=	['pixel_values']
								self.assertListEqual(arg_names[:1]		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    str:







							"""simple docstring"""


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

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									snake_case_		:			Dict											=	self._prepare_for_class(_A		,   _A	)
									snake_case_		:			Tuple											=	model_class(_A	)

									@jax.jit
									def model_jitted(_A      :      Optional[int]		,   **_A      :      Any	):
										return model(pixel_values=_A		,   **_A	)

									with self.subTest('JIT Enabled'	):
										snake_case_		:			Optional[Any]											=	model_jitted(**_A	).to_tuple()

									with self.subTest('JIT Disabled'	):
										with jax.disable_jit():
											snake_case_		:			Optional[Any]											=	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 UpperCAmelCase_				(      self      :      List[str]	)							->    Any:







							"""simple docstring"""


							for model_class_name in self.all_model_classes:
								snake_case_		:			List[Any]											=	model_class_name.from_pretrained('google/vit-base-patch16-224'	)
								snake_case_		:			Optional[Any]											=	model(np.ones((1, 3, 224, 224)	)	)
								self.assertIsNotNone(_A	)

