



'''simple docstring'''


from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
  from transformers import (
      TFBertModel,
      TFCLIPVisionModel,
      TFDeiTModel,
      TFRobertaModel,
      TFVisionTextDualEncoderModel,
      TFViTModel,
      VisionTextDualEncoderConfig,
  )

if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     if isinstance(lowercase__       ,	collections.abc.Iterable       ):
          return x
     return (x, x)







@require_tf
class 	UpperCAmelCase	:


  '''simple docstring'''




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



       """simple docstring"""







       pass




  def       lowercase_  (   self) ->      int:



       """simple docstring"""







       pass




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



       """simple docstring"""







       pass




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



       """simple docstring"""







       a_       		=VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_							,  lowerCAmelCase_)

       a_       		=TFVisionTextDualEncoderModel(lowerCAmelCase_)

       a_       		=model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)

       self.assertEqual(output["text_embeds"].shape							,  (input_ids.shape[0], config.projection_dim))
       self.assertEqual(output["image_embeds"].shape							,  (pixel_values.shape[0], config.projection_dim))




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Any:



       """simple docstring"""







       a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
       a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

       a_       		=model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)

       self.assertEqual(output["text_embeds"].shape							,  (input_ids.shape[0], model.config.projection_dim))
       self.assertEqual(output["image_embeds"].shape							,  (pixel_values.shape[0], model.config.projection_dim))




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



       """simple docstring"""







       a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
       a_       		={"vision_model": vision_model, "text_model": text_model}
       a_       		=TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_)

       a_       		=model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)

       self.assertEqual(output["text_embeds"].shape							,  (input_ids.shape[0], model.config.projection_dim))
       self.assertEqual(output["image_embeds"].shape							,  (pixel_values.shape[0], model.config.projection_dim))




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



       """simple docstring"""







       a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
       a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

       a_       		=model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)
       a_       		=output[0].numpy()

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(lowerCAmelCase_)
            a_       		=TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_)

            a_       		=model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)
            a_       		=after_output[0].numpy()
            a_       		=np.amax(np.abs(out_a - out_a))
            self.assertLessEqual(lowerCAmelCase_							,  1e-5)




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



       """simple docstring"""







       a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
       a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

       a_       		=model(
           input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  output_attentions=lowerCAmelCase_)

       a_       		=output.vision_model_output.attentions
       self.assertEqual(len(lowerCAmelCase_)							,  vision_config.num_hidden_layers)

       # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
       a_       		=to_atuple(vision_model.config.image_size)
       a_       		=to_atuple(vision_model.config.patch_size)
       a_       		=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       a_       		=num_patches + 1
       self.assertEqual(vision_attentions[0].shape[-3:]							,  (vision_config.num_attention_heads, seq_len, seq_len))

       a_       		=output.text_model_output.attentions
       self.assertEqual(len(lowerCAmelCase_)							,  text_config.num_hidden_layers)

       self.assertEqual(
           text_attentions[0].shape[-3:]							,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])							,  )




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Dict:



       """simple docstring"""







       a_       		=np.abs((a - b)).max()
       self.assertLessEqual(lowerCAmelCase_							,  lowerCAmelCase_							,  f"""Difference between torch and flax is {diff} (>= {tol}).""")




  def       lowercase_  (   self) ->      int:



       """simple docstring"""







       a_       		=self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_model(**lowerCAmelCase_)




  def       lowercase_  (   self) ->      Any:



       """simple docstring"""







       a_       		=self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**lowerCAmelCase_)




  def       lowercase_  (   self) ->      Optional[int]:



       """simple docstring"""







       a_       		=self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase_)




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



       """simple docstring"""







       a_       		=self.prepare_config_and_inputs()
       self.check_save_load(**lowerCAmelCase_)




  def       lowercase_  (   self) ->      Dict:



       """simple docstring"""







       a_       		=self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**lowerCAmelCase_)




  @slow
  def       lowercase_  (   self) ->      Any:



       """simple docstring"""







       a_		,						a_       		=self.get_pretrained_model_and_inputs()

       a_       		=model_a(**lowerCAmelCase_)
       a_       		=outputs[0].numpy()

       with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(lowerCAmelCase_)
            a_       		=TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_)

            a_       		=model_a(**lowerCAmelCase_)
            a_       		=after_outputs[0].numpy()
            a_       		=np.amax(np.abs(out_a - out_a))
            self.assertLessEqual(lowerCAmelCase_							,  1e-5)







@require_tf
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


  '''simple docstring'''




  def       lowercase_  (   self) ->      List[Any]:



       """simple docstring"""







       a_       		=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
           "hf-internal-testing/tiny-random-vit"							,  "hf-internal-testing/tiny-random-bert")
       a_       		=1_3
       a_       		=floats_tensor(
           [
               batch_size,
               model.vision_model.config.num_channels,
               model.vision_model.config.image_size,
               model.vision_model.config.image_size,
           ])
       a_       		=ids_tensor([batch_size, 4]							,  model.text_model.config.vocab_size)
       a_       		=random_attention_mask([batch_size, 4])
       a_       		={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

       return model, inputs




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



       """simple docstring"""







       a_       		=TFViTModel(lowerCAmelCase_							,  name="vision_model")
       a_       		=TFBertModel(lowerCAmelCase_							,  name="text_model")
       return vision_model, text_model




  def       lowercase_  (   self) ->      Optional[int]:



       """simple docstring"""







       a_       		=TFViTModelTester(self)
       a_       		=TFBertModelTester(self)
       a_       		=vit_model_tester.prepare_config_and_inputs()
       a_       		=bert_model_tester.prepare_config_and_inputs()

       a_		,						a_		,						a_       		=vision_config_and_inputs

       (
           (
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,												
       )       		=text_config_and_inputs

       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": input_mask,
           "input_ids": input_ids,
           "text_token_type_ids": token_type_ids,
           "text_sequence_labels": sequence_labels,
           "text_token_labels": token_labels,
           "text_choice_labels": choice_labels,
       }







@require_tf
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


  '''simple docstring'''




  def       lowercase_  (   self) ->      Tuple:



       """simple docstring"""







       a_       		=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
           "Rocketknight1/tiny-random-deit-tf"							,  "hf-internal-testing/tiny-random-roberta")
       a_       		=1_3
       a_       		=floats_tensor(
           [
               batch_size,
               model.vision_model.config.num_channels,
               model.vision_model.config.image_size,
               model.vision_model.config.image_size,
           ])
       a_       		=ids_tensor([batch_size, 4]							,  model.text_model.config.vocab_size)
       a_       		=random_attention_mask([batch_size, 4])
       a_       		={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

       return model, inputs




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      int:



       """simple docstring"""







       a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
       a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

       a_       		=model(
           input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  output_attentions=lowerCAmelCase_)

       a_       		=output.vision_model_output.attentions
       self.assertEqual(len(lowerCAmelCase_)							,  vision_config.num_hidden_layers)

       # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
       a_       		=to_atuple(vision_model.config.image_size)
       a_       		=to_atuple(vision_model.config.patch_size)
       a_       		=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       a_       		=num_patches + 2
       self.assertEqual(vision_attentions[0].shape[-3:]							,  (vision_config.num_attention_heads, seq_len, seq_len))

       a_       		=output.text_model_output.attentions
       self.assertEqual(len(lowerCAmelCase_)							,  text_config.num_hidden_layers)

       self.assertEqual(
           text_attentions[0].shape[-3:]							,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])							,  )




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Tuple:



       """simple docstring"""







       a_       		=TFDeiTModel(lowerCAmelCase_							,  name="vision_model")
       a_       		=TFRobertaModel(lowerCAmelCase_							,  name="text_model")
       return vision_model, text_model




  def       lowercase_  (   self) ->      Any:



       """simple docstring"""







       a_       		=TFDeiTModelTester(self)
       a_       		=TFRobertaModelTester(self)
       a_       		=vit_model_tester.prepare_config_and_inputs()
       a_       		=bert_model_tester.prepare_config_and_inputs()

       a_		,						a_		,						a_       		=vision_config_and_inputs

       (
           (
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,												
       )       		=text_config_and_inputs

       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": input_mask,
           "input_ids": input_ids,
           "text_token_type_ids": token_type_ids,
           "text_sequence_labels": sequence_labels,
           "text_token_labels": token_labels,
           "text_choice_labels": choice_labels,
       }







@require_tf
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


  '''simple docstring'''




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



       """simple docstring"""







       a_       		=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
           "Rocketknight1/tiny-random-clip-tf"							,  "hf-internal-testing/tiny-random-bert")
       a_       		=1_3
       a_       		=floats_tensor(
           [
               batch_size,
               model.vision_model.config.num_channels,
               model.vision_model.config.image_size,
               model.vision_model.config.image_size,
           ])
       a_       		=ids_tensor([batch_size, 4]							,  model.text_model.config.vocab_size)
       a_       		=random_attention_mask([batch_size, 4])
       a_       		={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

       return model, inputs




  def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      int:



       """simple docstring"""







       a_       		=TFCLIPVisionModel(lowerCAmelCase_							,  name="vision_model")
       a_       		=TFBertModel(lowerCAmelCase_							,  name="text_model")
       return vision_model, text_model




  def       lowercase_  (   self) ->      int:



       """simple docstring"""







       a_       		=TFCLIPVisionModelTester(self)
       a_       		=TFBertModelTester(self)
       a_       		=clip_model_tester.prepare_config_and_inputs()
       a_       		=bert_model_tester.prepare_config_and_inputs()

       a_		,						a_       		=vision_config_and_inputs

       (
           (
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,						(
           a_						
       )		,												
       )       		=text_config_and_inputs

       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": input_mask,
           "input_ids": input_ids,
           "text_token_type_ids": token_type_ids,
           "text_sequence_labels": sequence_labels,
           "text_token_labels": token_labels,
           "text_choice_labels": choice_labels,
       }







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


  '''simple docstring'''




  @slow
  def       lowercase_  (   self) ->      Optional[Any]:



       """simple docstring"""







       a_       		=TFVisionTextDualEncoderModel.from_pretrained(
           "clip-italian/clip-italian"							,  logit_scale_init_value=1.0							,  from_pt=lowerCAmelCase_)
       a_       		=VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")

       a_       		=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
       a_       		=processor(
           text=["una foto di un gatto", "una foto di un cane"]							,  images=lowerCAmelCase_							,  padding=lowerCAmelCase_							,  return_tensors="np")

       a_       		=model(**lowerCAmelCase_)

       # verify the logits
       self.assertEqual(outputs.logits_per_image.shape							,  (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
       self.assertEqual(
           outputs.logits_per_text.shape							,  (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])							,  )

       a_       		=np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]])

       self.assertTrue(np.allclose(outputs.logits_per_image.numpy()							,  lowerCAmelCase_							,  atol=1e-3))







'''simple docstring'''


import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

lowercase							       =			{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

lowercase							       =			{
    '''abeja/gpt-neox-japanese-2.7b''': 2_048,
}






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     with open(lowercase__       ,	"r"       ,	encoding="utf-8"       ) as f:
          a_       		=json.loads(f.read()       )

     a_       		=collections.OrderedDict()
     a_       		=collections.OrderedDict()
     a_       		=collections.OrderedDict()
     with open(lowercase__       ,	"r"       ,	encoding="utf-8"       ) as f:
          a_       		=f.readlines()
     a_       		=[[t.rstrip("\n"       )] if (t == "," or "," not in t) else t.rstrip("\n"       ).split(","       ) for t in token]
     for idx, b in enumerate(lowercase__       ):
          a_       		=b
          a_       		=idx
          for wd in b:
               a_       		=idx

     return vocab, raw_vocab, ids_to_tokens, emoji







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      VOCAB_FILES_NAMES
 __magic_name__					: str  =      PRETRAINED_VOCAB_FILES_MAP
 __magic_name__					: Union[str, Any]  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__					: str  =      ["input_ids", "attention_mask"]




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_="<|endoftext|>"							,  lowerCAmelCase_="<|endoftext|>"							,  lowerCAmelCase_="<|startoftext|>"							,  lowerCAmelCase_="<|endoftext|>"							,  lowerCAmelCase_=False							,  **lowerCAmelCase_							,  ) ->      List[Any]:



      """simple docstring"""







      super().__init__(
          unk_token=lowerCAmelCase_							,  pad_token=lowerCAmelCase_							,  bos_token=lowerCAmelCase_							,  eos_token=lowerCAmelCase_							,  do_clean_text=lowerCAmelCase_							,  **lowerCAmelCase_							,  )
      if not os.path.isfile(lowerCAmelCase_):
           raise ValueError(
               f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
               " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`")
      if not os.path.isfile(lowerCAmelCase_):
           raise ValueError(
               f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
               " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`")
      a_       		=do_clean_text
      a_		,						a_		,						a_		,						a_       		=load_vocab_and_emoji(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		=SubWordJapaneseTokenizer(
          vocab=self.vocab							,  ids_to_tokens=self.ids_to_tokens							,  emoji=self.emoji)




 @property
 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      return len(self.raw_vocab)




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



      """simple docstring"""







      return dict(self.raw_vocab							,  **self.added_tokens_encoder)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      return self.subword_tokenizer.tokenize(lowerCAmelCase_							,  clean=self.do_clean_text)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







      return self.vocab.get(lowerCAmelCase_							,  self.vocab.get(self.unk_token))




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      return self.subword_tokenizer.convert_id_to_token(lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		="".join(lowerCAmelCase_).strip()
      return out_string




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[int]:



      """simple docstring"""







      a_       		=[]
      for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(lowerCAmelCase_							,  add_special_tokens=lowerCAmelCase_) + [self.eos_token_id])

      if len(lowerCAmelCase_) > self.model_max_length:
           a_       		=input_ids[-self.model_max_length :]
      return input_ids




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      Tuple[str]:



      """simple docstring"""







      a_       		=0
      if os.path.isdir(lowerCAmelCase_):
           a_       		=os.path.join(
               lowerCAmelCase_							,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
           a_       		=os.path.join(
               lowerCAmelCase_							,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"])
      else:
           a_       		=(
               (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
           )
           a_       		=(
               (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
           )
      with open(lowerCAmelCase_							,  "w"							,  encoding="utf-8") as writer:
           for token_index, token in self.ids_to_tokens.items():
                if index != token_index:
                     logger.warning(
                         f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                         " Please check that the vocabulary is not corrupted!")
                     a_       		=token_index
                writer.write(",".join(lowerCAmelCase_) + "\n")
                index += 1
      with open(lowerCAmelCase_							,  "w"							,  encoding="utf-8") as writer:
           json.dump(self.emoji							,  lowerCAmelCase_)
      return vocab_file, emoji_file







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      str:



      """simple docstring"""







      a_       		=vocab  # same as swe
      a_       		=ids_to_tokens  # same as bpe
      a_       		=emoji
      a_       		=np.max([len(lowerCAmelCase_) for w in self.vocab.keys()])
      a_       		=re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)")
      a_       		=re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*")
      a_       		=re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}")
      a_       		=re.compile(
          r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*")
      a_       		=re.compile(
          r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*")
      a_       		=re.compile(
          r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*")
      a_       		="─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
      a_       		="▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
      a_       		=str.maketrans({k: "<BLOCK>" for k in keisen + blocks})




 def __len__(   self) ->      Tuple:



      """simple docstring"""







      return len(self.ids_to_tokens)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		=self.content_repattera.sub("<URL>"							,  lowerCAmelCase_)
      a_       		=self.content_repattera.sub("<EMAIL>"							,  lowerCAmelCase_)
      a_       		=self.content_repattera.sub("<TEL>"							,  lowerCAmelCase_)
      a_       		=self.content_repattera.sub("<DATE>"							,  lowerCAmelCase_)
      a_       		=self.content_repattera.sub("<DATE>"							,  lowerCAmelCase_)
      a_       		=self.content_repattera.sub("<PRICE>"							,  lowerCAmelCase_)
      a_       		=content.translate(self.content_transa)
      while "<BLOCK><BLOCK>" in content:
           a_       		=content.replace("<BLOCK><BLOCK>"							,  "<BLOCK>")
      return content




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=False) ->      Union[str, Any]:



      """simple docstring"""







      a_       		=text.replace(" "							,  "<SP>")
      a_       		=text.replace("　"							,  "<SP>")
      a_       		=text.replace("\r\n"							,  "<BR>")
      a_       		=text.replace("\n"							,  "<BR>")
      a_       		=text.replace("\r"							,  "<BR>")
      a_       		=text.replace("\t"							,  "<TAB>")
      a_       		=text.replace("—"							,  "ー")
      a_       		=text.replace("−"							,  "ー")
      for k, v in self.emoji["emoji"].items():
           if k in text:
                a_       		=text.replace(lowerCAmelCase_							,  lowerCAmelCase_)
      if clean:
           a_       		=self.clean_text(lowerCAmelCase_)

      def check_simbol(lowerCAmelCase_):
           a_       		=x.encode()
           if len(lowerCAmelCase_) == 1 and len(lowerCAmelCase_) == 2:
                a_       		=(int(e[0]) << 8) + int(e[1])
                if (
                    (c >= 0xc2a1 and c <= 0xc2bf)
                    or (c >= 0xc780 and c <= 0xc783)
                    or (c >= 0xcab9 and c <= 0xcbbf)
                    or (c >= 0xcc80 and c <= 0xcda2)
                ):
                     return True
           return False

      def checkuae(lowerCAmelCase_):
           a_       		=x.encode()
           if len(lowerCAmelCase_) == 1 and len(lowerCAmelCase_) == 3:
                a_       		=(int(e[0]) << 1_6) + (int(e[1]) << 8) + int(e[2])
                if c >= 0xe2_8080 and c <= 0xe2_b07f:
                     return True
           return False

      a_       		=0
      a_       		=[]
      while pos < len(lowerCAmelCase_):
           a_       		=min(len(lowerCAmelCase_)							,  pos + self.maxlen + 1) if text[pos] == "<" else pos + 3
           a_       		=[]  # (token_id, token, pos)
           for e in range(lowerCAmelCase_							,  lowerCAmelCase_							,  -1):
                a_       		=text[pos:e]
                if wd in self.vocab:
                     if wd[0] == "<" and len(lowerCAmelCase_) > 2:
                          a_       		=[(self.vocab[wd], wd, e)]
                          break
                     else:
                          candidates.append((self.vocab[wd], wd, e))
           if len(lowerCAmelCase_) > 0:
                # the smallest token_id is adopted
                a_		,						a_		,						a_       		=sorted(lowerCAmelCase_							,  key=lambda lowerCAmelCase_: x[0])[0]
                result.append(lowerCAmelCase_)
                a_       		=e
           else:
                a_       		=pos + 1
                a_       		=text[pos:end]
                if check_simbol(lowerCAmelCase_):
                     result.append("<KIGOU>")
                elif checkuae(lowerCAmelCase_):
                     result.append("<U2000U2BFF>")
                else:
                     for i in wd.encode("utf-8"):
                          result.append("<|byte%d|>" % i)
                a_       		=end
      return result




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_="\n") ->      List[Any]:



      """simple docstring"""







      a_       		=[]
      a_       		=[]
      a_       		=self.ids_to_tokens[index][0]
      if word[:6] == "<|byte" and word[-2:] == "|>":
           byte_tokens.append(int(word[6:-2]))
      else:
           if len(lowerCAmelCase_) > 0:
                words.append(bytearray(lowerCAmelCase_).decode("utf-8"							,  errors="replace"))
                a_       		=[]
           if word[:7] == "<|emoji" and word[-2:] == "|>":
                words.append(self.emoji["emoji_inv"][word])
           elif word == "<SP>":
                words.append(" ")
           elif word == "<BR>":
                words.append(lowerCAmelCase_)
           elif word == "<TAB>":
                words.append("\t")
           elif word == "<BLOCK>":
                words.append("▀")
           elif word == "<KIGOU>":
                words.append("ǀ")
           elif word == "<U2000U2BFF>":
                words.append("‖")
           else:
                words.append(lowerCAmelCase_)
      if len(lowerCAmelCase_) > 0:
           words.append(bytearray(lowerCAmelCase_).decode("utf-8"							,  errors="replace"))
      a_       		="".join(lowerCAmelCase_)
      return text


