


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        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Any=13			,			lowerCAmelCase__			:Tuple=30			,			lowerCAmelCase__			:List[Any]=2			,			lowerCAmelCase__			:List[str]=3			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:Tuple=32			,			lowerCAmelCase__			:Optional[Any]=5			,			lowerCAmelCase__			:List[Any]=4			,			lowerCAmelCase__			:Union[str, Any]=37			,			lowerCAmelCase__			:Any="gelu"			,			lowerCAmelCase__			:List[str]=0.1			,			lowerCAmelCase__			:Tuple=0.1			,			lowerCAmelCase__			:Dict=10			,			lowerCAmelCase__			:Tuple=0.02			,			)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		parent
										__SCREAMING_SNAKE_CASE						:     List[str]					=		batch_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		image_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		patch_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_channels
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		is_training
										__SCREAMING_SNAKE_CASE						:     Dict					=		use_labels
										__SCREAMING_SNAKE_CASE						:     Any					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     Dict					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Tuple					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Dict					=		type_sequence_label_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		initializer_range

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


				def 		__magic_name__(       self			:Tuple	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										__SCREAMING_SNAKE_CASE						:     Dict					=		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=lowercase_			,			initializer_range=self.initializer_range			,			)

										return config, pixel_values


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:Union[str, Any]	)		->			int:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		FlaxViTModel(config=lowercase_	)
										__SCREAMING_SNAKE_CASE						:     int					=		model(lowercase_	)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		(self.image_size, self.image_size)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		(self.patch_size, self.patch_size)
										__SCREAMING_SNAKE_CASE						:     str					=		(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 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.type_sequence_label_size
										__SCREAMING_SNAKE_CASE						:     Tuple					=		FlaxViTForImageClassification(config=lowercase_	)
										__SCREAMING_SNAKE_CASE						:     str					=		model(lowercase_	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size)	)

										# test greyscale images
										__SCREAMING_SNAKE_CASE						:     Any					=		1
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		FlaxViTForImageClassification(lowercase_	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		model(lowercase_	)



				def 		__magic_name__(       self			:List[Any]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.prepare_config_and_inputs()
										(
										    __SCREAMING_SNAKE_CASE   
										)						:     Tuple					=		config_and_inputs
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{"pixel_values": pixel_values}
										return config, inputs_dict




@require_flax
class        _lowercase      (			lowercase__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Any						=		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


				def 		__magic_name__(       self			:List[Any]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		FlaxViTModelTester(self	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		ConfigTester(self			,			config_class=lowercase_			,			has_text_modality=lowercase_			,			hidden_size=37	)


				def 		__magic_name__(       self			:List[Any]	)		->			Tuple:
										self.config_tester.run_common_tests()


				def 		__magic_name__(       self			:List[Any]	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_	)


				def 		__magic_name__(       self			:str	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase_	)


				def 		__magic_name__(       self			:List[str]	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.model_tester.prepare_config_and_inputs_for_common()

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

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


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		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						:     Union[str, Any]					=		self._prepare_for_class(lowercase_			,			lowercase_	)
																						__SCREAMING_SNAKE_CASE						:     Tuple					=		model_class(lowercase_	)

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

																						with self.subTest('''JIT Enabled'''	):
																												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model_jitted(**lowercase_	).to_tuple()

																						with self.subTest('''JIT Disabled'''	):
																												with jax.disable_jit():
																																		__SCREAMING_SNAKE_CASE						:     Tuple					=		model_jitted(**lowercase_	).to_tuple()

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



				@slow
				def 		__magic_name__(       self			:Any	)		->			Dict:
										for model_class_name in self.all_model_classes:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model_class_name.from_pretrained('''google/vit-base-patch16-224'''	)
																__SCREAMING_SNAKE_CASE						:     List[str]					=		model(np.ones((1, 3, 224, 224)	)	)
																self.assertIsNotNone(lowercase_	)


"""simple docstring"""





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

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


if is_sentencepiece_available():
    from .tokenization_fnet import FNetTokenizer
else:
    _a		       =							None

_a		       =							logging.get_logger(__name__)
_a		       =							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_a		       =							{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_a		       =							{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

_a		       =							'▁'


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[str]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Tuple       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Any       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		["""input_ids""", """token_type_ids"""]
  SCREAMING_SNAKE_CASE__			:						Tuple       =		FNetTokenizer







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=False				,   lowercase_=True				,   lowercase_=True				,   lowercase_="<unk>"				,   lowercase_="[SEP]"				,   lowercase_="<pad>"				,   lowercase_="[CLS]"				,   lowercase_="[MASK]"				,   **lowercase_				,   ):

     """simple docstring"""
     # Mask token behave like a normal word, i.e. include the space before it and
     # is included in the raw text, there should be a match in a non-normalized sentence.
     UpperCAmelCase_					:					int			     =    (
         AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_				,   normalized=lowercase_  )
         if isinstance(lowercase_				,   lowercase_  )
         else mask_token
     )

     super().__init__(
         lowercase_				,   tokenizer_file=lowercase_				,   do_lower_case=lowercase_				,   remove_space=lowercase_				,   keep_accents=lowercase_				,   unk_token=lowercase_				,   sep_token=lowercase_				,   pad_token=lowercase_				,   cls_token=lowercase_				,   mask_token=lowercase_				,   **lowercase_				,   )

     UpperCAmelCase_					:					Any			     =    do_lower_case
     UpperCAmelCase_					:					Tuple			     =    remove_space
     UpperCAmelCase_					:					str			     =    keep_accents
     UpperCAmelCase_					:					Any			     =    vocab_file
     UpperCAmelCase_					:					List[Any]			     =    False if not self.vocab_file else True







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     if not os.path.isdir(lowercase_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
     UpperCAmelCase_					:					List[str]			     =    os.path.join(
         lowercase_				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase_  ):
        copyfile(self.vocab_file				,   lowercase_  )

     return (out_vocab_file,)


