

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


UpperCamelCase__					: Optional[Any]								=      logging.get_logger(__name__)
UpperCamelCase__					: Union[str, Any]								=      {"vocab_file": "spiece.model"}

UpperCamelCase__					: Union[str, 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",
    },
}

UpperCamelCase__					: Union[str, Any]								=      {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

UpperCamelCase__					: List[Any]								=      "▁"



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					VOCAB_FILES_NAMES
  __lowerCAmelCase       :		str  	=					PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :		List[str]  	=					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  ,						):
      # 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.
      SCREAMING_SNAKE_CASE_													=  (
          AddedToken(_A  ,						lstrip=_A  ,						rstrip=_A  ,						normalized=_A)
          if isinstance(_A  ,						_A)
          else mask_token
      )

      SCREAMING_SNAKE_CASE_													=  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=_A  ,						remove_space=_A  ,						keep_accents=_A  ,						unk_token=_A  ,						sep_token=_A  ,						pad_token=_A  ,						cls_token=_A  ,						mask_token=_A  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**_A  ,						)

      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_kwargs)
      self.sp_model.Load(_A)

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

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

  def __getstate__(   self):
      SCREAMING_SNAKE_CASE_													=  self.__dict__.copy()
      SCREAMING_SNAKE_CASE_													=  None
      return state

  def __setstate__(   self  ,						_A):
      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   lowerCAmelCase__       (   self  ,						_A):
      if self.remove_space:
          SCREAMING_SNAKE_CASE_													=  ' '.join(inputs.strip().split())
      else:
          SCREAMING_SNAKE_CASE_													=  inputs
      SCREAMING_SNAKE_CASE_													=  outputs.replace('``'  ,						'"').replace('\'\''  ,						'"')

      if not self.keep_accents:
          SCREAMING_SNAKE_CASE_													=  unicodedata.normalize('NFKD'  ,						_A)
          SCREAMING_SNAKE_CASE_													=  ''.join([c for c in outputs if not unicodedata.combining(_A)])
      if self.do_lower_case:
          SCREAMING_SNAKE_CASE_													=  outputs.lower()

      return outputs

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.preprocess_text(_A)
      SCREAMING_SNAKE_CASE_													=  self.sp_model.encode(_A  ,						out_type=_A)
      SCREAMING_SNAKE_CASE_													=  []
      for piece in pieces:
          if len(_A) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
              SCREAMING_SNAKE_CASE_													=  self.sp_model.EncodeAsPieces(piece[:-1].replace(_A  ,						''))
              if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                  if len(cur_pieces[0]) == 1:
                      SCREAMING_SNAKE_CASE_													=  cur_pieces[1:]
                  else:
                      SCREAMING_SNAKE_CASE_													=  cur_pieces[0][1:]
              cur_pieces.append(piece[-1])
              new_pieces.extend(_A)
          else:
              new_pieces.append(_A)

      return new_pieces

  def   lowerCAmelCase__       (   self  ,						_A):
      return self.sp_model.PieceToId(_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      return self.sp_model.IdToPiece(_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      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(_A) + token
              SCREAMING_SNAKE_CASE_													=  True
              SCREAMING_SNAKE_CASE_													=  []
          else:
              current_sub_tokens.append(_A)
              SCREAMING_SNAKE_CASE_													=  False
      out_string += self.sp_model.decode(_A)
      return out_string.strip()

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False  ,						_A = None  ,						_A = True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('use_source_tokenizer'  ,						_A)

      SCREAMING_SNAKE_CASE_													=  self.convert_ids_to_tokens(_A  ,						skip_special_tokens=_A)

      # 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
      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  []
      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(_A))
                  SCREAMING_SNAKE_CASE_													=  []
              sub_texts.append(_A)
          else:
              current_sub_text.append(_A)
      if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(_A))

      # Mimic the behavior of the Rust tokenizer:
      # No space after <unk>
      if spaces_between_special_tokens:
          SCREAMING_SNAKE_CASE_													=  re.sub(r'(<unk>) '  ,						r'\1'  ,						' '.join(_A))
      else:
          SCREAMING_SNAKE_CASE_													=  ''.join(_A)

      SCREAMING_SNAKE_CASE_													=  (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
          SCREAMING_SNAKE_CASE_													=  self.clean_up_tokenization(_A)
          return clean_text
      else:
          return text

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None):
      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   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = False):

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

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

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None):
      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   lowerCAmelCase__       (   self  ,						_A  ,						_A = None):
      if not os.path.isdir(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_													=  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) and os.path.isfile(self.vocab_file):
          copyfile(self.vocab_file  ,						_A)
      elif not os.path.isfile(self.vocab_file):
          with open(_A  ,						'wb') as fi:
              SCREAMING_SNAKE_CASE_													=  self.sp_model.serialized_model_proto()
              fi.write(_A)

      return (out_vocab_file,)





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  __snake_case					:

  def __init__(   self  ,						_A  ,						_A=13  ,						_A=32  ,						_A=3  ,						_A=4  ,						_A=[10, 20, 30, 40]  ,						_A=[2, 2, 3, 2]  ,						_A=True  ,						_A=True  ,						_A=37  ,						_A="gelu"  ,						_A=10  ,						_A=0.0_2  ,						_A=3  ,						_A=None  ,						):
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  num_stages
      SCREAMING_SNAKE_CASE_													=  hidden_sizes
      SCREAMING_SNAKE_CASE_													=  depths
      SCREAMING_SNAKE_CASE_													=  is_training
      SCREAMING_SNAKE_CASE_													=  use_labels
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  scope

  def   lowerCAmelCase__       (   self):
      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.type_sequence_label_size)

      SCREAMING_SNAKE_CASE_													=  self.get_config()

      return config, pixel_values, labels

  def   lowerCAmelCase__       (   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   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel(config=_A)
      SCREAMING_SNAKE_CASE_													=  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 // 32, self.image_size // 32)  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification(_A)
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						labels=_A  ,						training=_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))


  def   lowerCAmelCase__       (   self):
      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  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__       ,	unittest.TestCase    ):
  __lowerCAmelCase       :		str  	=					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  __lowerCAmelCase       :		List[str]  	=					(
      {'feature-extraction': TFConvNextModel, 'image-classification': TFConvNextForImageClassification}
      if is_tf_available()
      else {}
  )

  __lowerCAmelCase       :		Union[str, Any]  	=					False
  __lowerCAmelCase       :		Dict  	=					False
  __lowerCAmelCase       :		Optional[int]  	=					False
  __lowerCAmelCase       :		Optional[Any]  	=					False
  __lowerCAmelCase       :		List[Any]  	=					False

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModelTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(
          self  ,						config_class=_A  ,						has_text_modality=_A  ,						hidden_size=37  ,						)

  @unittest.skip(reason='ConvNext does not use inputs_embeds')
  def   lowerCAmelCase__       (   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   lowerCAmelCase__       (   self):
      super().test_keras_fit()

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

  def   lowerCAmelCase__       (   self):
      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   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__       (   self):
      super().test_dataset_conversion()

  def   lowerCAmelCase__       (   self):
      def check_hidden_states_output(_A  ,						_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          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(_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]  ,						)

      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_													=  True
          check_hidden_states_output(_A  ,						_A  ,						_A)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          SCREAMING_SNAKE_CASE_													=  True

          check_hidden_states_output(_A  ,						_A  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(_A  ,						_A  ,						_A  ,						_A={}):
          SCREAMING_SNAKE_CASE_													=  model(_A  ,						return_dict=_A  ,						**_A)
          SCREAMING_SNAKE_CASE_													=  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:
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel.from_pretrained('facebook/convnext-tiny-224')
      self.assertIsNotNone(_A)






def    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
    return image





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

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


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224')

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

      # forward pass
      SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1])

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


