
'''simple docstring'''


import os
import re
import unicodedata
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


__lowercase							:				int					=       logging.get_logger(__name__)
__lowercase							:				Union[str, Any]					=       {'''vocab_file''': '''spiece.model'''}

__lowercase							:				Any					=       {
    '''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''',
    },
}

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

__lowercase							:				Optional[int]					=       '''▁'''

class 		__lowercase			(					_lowercase      ):
   lowerCamelCase     :						List[Any]		 =     VOCAB_FILES_NAMES
   lowerCamelCase     :						Optional[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase     :						Optional[int]		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase     :						Union[str, Any]		 =     ["input_ids", "token_type_ids"]

   def __init__(self     ,				A     ,				A=False     ,				A=True     ,				A=True     ,				A="<unk>"     ,				A="[SEP]"     ,				A="<pad>"     ,				A="[CLS]"     ,				A="[MASK]"     ,				A = None     ,				**A     ,				):
        lowerCamelCase_   : Optional[Any]       =						(
            AddedToken(lowercase__     ,				lstrip=lowercase__     ,				rstrip=lowercase__     ,				normalized=lowercase__ )
            if isinstance(lowercase__     ,				lowercase__ )
            else mask_token
        )

        lowerCamelCase_   : str       =						{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            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__     ,				sp_model_kwargs=self.sp_model_kwargs     ,				**lowercase__     ,				)

        lowerCamelCase_   : Optional[Any]       =						do_lower_case
        lowerCamelCase_   : Dict       =						remove_space
        lowerCamelCase_   : List[str]       =						keep_accents
        lowerCamelCase_   : Tuple       =						vocab_file

        lowerCamelCase_   : List[Any]       =						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(lowercase__ )

   @property
   def     UpperCAmelCase__	(self ):
        return len(self.sp_model )

   def     UpperCAmelCase__	(self ):
        lowerCamelCase_   : Optional[int]       =						{self.convert_ids_to_tokens(lowercase__ ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab

   def __getstate__(self ):
        lowerCamelCase_   : Optional[int]       =						self.__dict__.copy()
        lowerCamelCase_   : Any       =						None
        return state

   def __setstate__(self     ,				A ):
        lowerCamelCase_   : List[str]       =						d

        # for backward compatibility
        if not hasattr(self     ,				'''sp_model_kwargs''' ):
             lowerCamelCase_   : Tuple       =						{}

        lowerCamelCase_   : Dict       =						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )

   def     UpperCAmelCase__	(self     ,				A ):







        if self.remove_space:
             lowerCamelCase_   : Any       =						''' '''.join(inputs.strip().split() )
        else:
             lowerCamelCase_   : Dict       =						inputs
        lowerCamelCase_   : Dict       =						outputs.replace('''``'''     ,				'''"''' ).replace('''\'\''''     ,				'''"''' )

        if not self.keep_accents:
             lowerCamelCase_   : int       =						unicodedata.normalize('''NFKD'''     ,				lowercase__ )
             lowerCamelCase_   : Dict       =						''''''.join([c for c in outputs if not unicodedata.combining(lowercase__ )] )
        if self.do_lower_case:
             lowerCamelCase_   : List[Any]       =						outputs.lower()

        return outputs

   def     UpperCAmelCase__	(self     ,				A ):
        lowerCamelCase_   : Any       =						self.preprocess_text(lowercase__ )
        lowerCamelCase_   : int       =						self.sp_model.encode(lowercase__     ,				out_type=lowercase__ )
        lowerCamelCase_   : List[str]       =						[]
        for piece in pieces:
             if len(lowercase__ ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
                  lowerCamelCase_   : Optional[Any]       =						self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase__     ,				'''''' ) )
                  if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                       if len(cur_pieces[0] ) == 1:
                            lowerCamelCase_   : int       =						cur_pieces[1:]
                       else:
                            lowerCamelCase_   : Any       =						cur_pieces[0][1:]
                  cur_pieces.append(piece[-1] )
                  new_pieces.extend(lowercase__ )
             else:
                  new_pieces.append(lowercase__ )

        return new_pieces

   def     UpperCAmelCase__	(self     ,				A ):
        return self.sp_model.PieceToId(lowercase__ )

   def     UpperCAmelCase__	(self     ,				A ):
        return self.sp_model.IdToPiece(lowercase__ )

   def     UpperCAmelCase__	(self     ,				A ):
        lowerCamelCase_   : Any       =						[]
        lowerCamelCase_   : Union[str, Any]       =						''''''
        lowerCamelCase_   : int       =						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(lowercase__ ) + token
                  lowerCamelCase_   : Optional[int]       =						True
                  lowerCamelCase_   : Tuple       =						[]
             else:
                  current_sub_tokens.append(lowercase__ )
                  lowerCamelCase_   : Dict       =						False
        out_string += self.sp_model.decode(lowercase__ )
        return out_string.strip()

   def     UpperCAmelCase__	(self     ,				A     ,				A = False     ,				A = None     ,				A = True     ,				**A     ,				):
        lowerCamelCase_   : Union[str, Any]       =						kwargs.pop('''use_source_tokenizer'''     ,				lowercase__ )

        lowerCamelCase_   : Union[str, Any]       =						self.convert_ids_to_tokens(lowercase__     ,				skip_special_tokens=lowercase__ )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        lowerCamelCase_   : Tuple       =						[]
        lowerCamelCase_   : Optional[int]       =						[]
        for token in filtered_tokens:
             if skip_special_tokens and token in self.all_special_ids:
                  continue
             if token in self.added_tokens_encoder:
                  if current_sub_text:
                       sub_texts.append(self.convert_tokens_to_string(lowercase__ ) )
                       lowerCamelCase_   : int       =						[]
                  sub_texts.append(lowercase__ )
             else:
                  current_sub_text.append(lowercase__ )
        if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(lowercase__ ) )

        # Mimic the behavior of the Rust tokenizer:
        # No space after <unk>
        if spaces_between_special_tokens:
             lowerCamelCase_   : Optional[Any]       =						re.sub(R'''(<unk>) '''     ,				R'''\1'''     ,				''' '''.join(lowercase__ ) )
        else:
             lowerCamelCase_   : str       =						''''''.join(lowercase__ )

        lowerCamelCase_   : Optional[Any]       =						(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
             lowerCamelCase_   : str       =						self.clean_up_tokenization(lowercase__ )
             return clean_text
        else:
             return text

   def     UpperCAmelCase__	(self     ,				A     ,				A = None ):
        lowerCamelCase_   : Optional[int]       =						[self.sep_token_id]
        lowerCamelCase_   : Dict       =						[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     ,				A     ,				A = None     ,				A = False ):







        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowercase__     ,				token_ids_a=lowercase__     ,				already_has_special_tokens=lowercase__ )

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

   def     UpperCAmelCase__	(self     ,				A     ,				A = None ):
        lowerCamelCase_   : Optional[Any]       =						[self.sep_token_id]
        lowerCamelCase_   : List[str]       =						[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     ,				A     ,				A = None ):







        if not os.path.isdir(lowercase__ ):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
             return
        lowerCamelCase_   : Optional[int]       =						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__ ) and os.path.isfile(self.vocab_file ):
             copyfile(self.vocab_file     ,				lowercase__ )
        elif not os.path.isfile(self.vocab_file ):
             with open(lowercase__     ,				'''wb''' ) as fi:
                  lowerCamelCase_   : Any       =						self.sp_model.serialized_model_proto()
                  fi.write(lowercase__ )

        return (out_vocab_file,)




'''simple docstring'''


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor

class 		__lowercase			:

			def __init__(self     ,				A     ,				A=1_3     ,				A=3_2     ,				A=3     ,				A=4     ,				A=[1_0, 2_0, 3_0, 4_0]     ,				A=[2, 2, 3, 2]     ,				A=True     ,				A=True     ,				A=3_7     ,				A="gelu"     ,				A=1_0     ,				A=0.02     ,				A=3     ,				A=None     ,				):
								lowerCamelCase_   : List[str]       =						parent
								lowerCamelCase_   : List[Any]       =						batch_size
								lowerCamelCase_   : List[str]       =						image_size
								lowerCamelCase_   : List[str]       =						num_channels
								lowerCamelCase_   : Union[str, Any]       =						num_stages
								lowerCamelCase_   : Optional[Any]       =						hidden_sizes
								lowerCamelCase_   : Optional[int]       =						depths
								lowerCamelCase_   : Optional[Any]       =						is_training
								lowerCamelCase_   : Dict       =						use_labels
								lowerCamelCase_   : Optional[int]       =						intermediate_size
								lowerCamelCase_   : Union[str, Any]       =						hidden_act
								lowerCamelCase_   : Tuple       =						type_sequence_label_size
								lowerCamelCase_   : Optional[Any]       =						initializer_range
								lowerCamelCase_   : str       =						scope

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[int]       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

								lowerCamelCase_   : str       =						None
								if self.use_labels:
													lowerCamelCase_   : Optional[Any]       =						ids_tensor([self.batch_size]     ,				self.type_sequence_label_size )

								lowerCamelCase_   : List[Any]       =						self.get_config()

								return config, pixel_values, labels

			def     UpperCAmelCase__	(self ):
								return ConvNextConfig(
								    num_channels=self.num_channels     ,				hidden_sizes=self.hidden_sizes     ,				depths=self.depths     ,				num_stages=self.num_stages     ,				hidden_act=self.hidden_act     ,				is_decoder=A     ,				initializer_range=self.initializer_range     ,				)

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : List[str]       =						TFConvNextModel(config=A )
								lowerCamelCase_   : Tuple       =						model(A     ,				training=A )
								# 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 // 3_2, self.image_size // 3_2)     ,				)

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : Union[str, Any]       =						self.type_sequence_label_size
								lowerCamelCase_   : Dict       =						TFConvNextForImageClassification(A )
								lowerCamelCase_   : Optional[Any]       =						model(A     ,				labels=A     ,				training=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size) )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.prepare_config_and_inputs()
								lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : List[str]       =						config_and_inputs
								lowerCamelCase_   : Tuple       =						{'''pixel_values''': pixel_values}
								return config, inputs_dict

@require_tf
class 		__lowercase			(					_lowercase ,       _lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						Tuple		 =     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
			lowerCamelCase     :						List[str]		 =     (
			    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase     :						List[Any]		 =     False
			lowerCamelCase     :						List[str]		 =     False
			lowerCamelCase     :						Optional[int]		 =     False
			lowerCamelCase     :						List[Any]		 =     False
			lowerCamelCase     :						List[Any]		 =     False

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextModelTester(self )
								lowerCamelCase_   : int       =						ConfigTester(
								    self     ,				config_class=A     ,				has_text_modality=A     ,				hidden_size=3_7     ,				)

			@unittest.skip(reason='''ConvNext does not use inputs_embeds''' )
			def     UpperCAmelCase__	(self ):
								pass

			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0     ,				reason='''TF does not support backprop for grouped convolutions on CPU.'''     ,				)
			@slow
			def     UpperCAmelCase__	(self ):
								super().test_keras_fit()

			@unittest.skip(reason='''ConvNext does not support input and output embeddings''' )
			def     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : Tuple       =						self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCamelCase_   : str       =						['''pixel_values''']
													self.assertListEqual(arg_names[:1]     ,				A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A )

			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0     ,				reason='''TF does not support backprop for grouped convolutions on CPU.'''     ,				)
			def     UpperCAmelCase__	(self ):
								super().test_dataset_conversion()

			def     UpperCAmelCase__	(self ):
								def check_hidden_states_output(A     ,				A     ,				A ):
													lowerCamelCase_   : str       =						model_class(A )

													lowerCamelCase_   : Dict       =						model(**self._prepare_for_class(A     ,				A ) )
													lowerCamelCase_   : List[str]       =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowerCamelCase_   : Tuple       =						self.model_tester.num_stages
													self.assertEqual(len(A )     ,				expected_num_stages + 1 )

													# ConvNext'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]     ,				)

								lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Optional[Any]       =						True
													check_hidden_states_output(A     ,				A     ,				A )

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

													check_hidden_states_output(A     ,				A     ,				A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : Dict       =						self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(A     ,				A     ,				A     ,				A={} ):
													lowerCamelCase_   : Tuple       =						model(A     ,				return_dict=A     ,				**A )
													lowerCamelCase_   : List[str]       =						model(A     ,				return_dict=A     ,				**A ).to_tuple()

													def recursive_check(A     ,				A ):
																		if isinstance(A     ,				(List, Tuple) ):
																							for tuple_iterable_value, dict_iterable_value in zip(A     ,				A ):
																												recursive_check(A     ,				A )
																		elif tuple_object is None:
																							return
																		else:
																							self.assertTrue(
																							    all(tf.equal(A     ,				A ) )     ,				msg=(
																							        '''Tuple and dict output are not equal. Difference:'''
																							        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
																							    )     ,				)

																		recursive_check(A     ,				A )

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Optional[Any]       =						model_class(A )

													lowerCamelCase_   : Any       =						self._prepare_for_class(A     ,				A )
													lowerCamelCase_   : Any       =						self._prepare_for_class(A     ,				A )
													check_equivalence(A     ,				A     ,				A )

													lowerCamelCase_   : Optional[Any]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													lowerCamelCase_   : int       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													check_equivalence(A     ,				A     ,				A )

													lowerCamelCase_   : Union[str, Any]       =						self._prepare_for_class(A     ,				A )
													lowerCamelCase_   : List[str]       =						self._prepare_for_class(A     ,				A )
													check_equivalence(A     ,				A     ,				A     ,				{'''output_hidden_states''': True} )

													lowerCamelCase_   : Optional[int]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													lowerCamelCase_   : List[str]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													check_equivalence(A     ,				A     ,				A     ,				{'''output_hidden_states''': True} )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A )






			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224''' )
								self.assertIsNotNone(A )




def lowercase_		(				)       ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[Any]       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					return image






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

			@cached_property
			def     UpperCAmelCase__	(self ):
								return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224''' ) if is_vision_available() else None






			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''' )

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

								# forward pass
								lowerCamelCase_   : Optional[int]       =						model(**A )

								# verify the logits
								lowerCamelCase_   : List[str]       =						tf.TensorShape((1, 1_0_0_0) )
								self.assertEqual(outputs.logits.shape     ,				A )

								lowerCamelCase_   : Optional[int]       =						tf.constant([-0.02_60, -0.47_39, 0.19_11] )

								tf.debugging.assert_near(outputs.logits[0, :3]     ,				A     ,				atol=1E-4 )


