



'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class 							a__(      unittest.TestCase			):







    def     lowercase_ (   self				:						Dict			):
      a		:      Any       =  tempfile.mkdtemp()

      # fmt: off
      a		:      Union[str, Any]       =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      a		:      Optional[int]       =  os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['vocab_file']			)
      with open(self.vocab_file		,    'w'		,    encoding='utf-8'			) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)

      a		:      Dict       =  {
          'do_resize': True,
          'size': {'height': 18, 'width': 18},
          'do_normalize': True,
          'image_mean': [0.5, 0.5, 0.5],
          'image_std': [0.5, 0.5, 0.5],
      }
      a		:      Dict       =  os.path.join(self.tmpdirname		,    __snake_case			)
      with open(self.image_processor_file		,    'w'		,    encoding='utf-8'			) as fp:
        json.dump(__snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						Optional[int]		,    **__snake_case				:						List[str]			):
      return BertTokenizer.from_pretrained(self.tmpdirname		,    **__snake_case			)







    def     lowercase_ (   self				:						Tuple		,    **__snake_case				:						str			):
      return ViTImageProcessor.from_pretrained(self.tmpdirname		,    **__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      shutil.rmtree(self.tmpdirname			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Union[str, Any]       =  [np.random.randint(2_55		,    size=(3, 30, 4_00)		,    dtype=np.uinta			)]

      a		:      Dict       =  [Image.fromarray(np.moveaxis(__snake_case		,    0		,    -1			)			) for x in image_inputs]

      return image_inputs







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      int       =  self.get_tokenizer()
      a		:      Dict       =  self.get_image_processor()

      a		:      Optional[int]       =  VisionTextDualEncoderProcessor(tokenizer=__snake_case		,    image_processor=__snake_case			)

      processor.save_pretrained(self.tmpdirname			)
      a		:      int       =  VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor.tokenizer.get_vocab()		,    tokenizer.get_vocab()			)
      self.assertIsInstance(processor.tokenizer		,    (BertTokenizer, BertTokenizerFast)			)

      self.assertEqual(processor.image_processor.to_json_string()		,    image_processor.to_json_string()			)
      self.assertIsInstance(processor.image_processor		,    __snake_case			)







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Dict       =  VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer()		,    image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      a		:      str       =  self.get_tokenizer(bos_token='(BOS)'		,    eos_token='(EOS)'			)
      a		:      Tuple       =  self.get_image_processor(do_normalize=__snake_case		,    padding_value=1.0			)

      a		:      Optional[int]       =  VisionTextDualEncoderProcessor.from_pretrained(
          self.tmpdirname		,    bos_token='(BOS)'		,    eos_token='(EOS)'		,    do_normalize=__snake_case		,    padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()		,    tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer		,    (BertTokenizer, BertTokenizerFast)			)

      self.assertEqual(processor.image_processor.to_json_string()		,    image_processor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.image_processor		,    __snake_case			)







    def     lowercase_ (   self				:						Dict			):
      a		:      Optional[int]       =  self.get_image_processor()
      a		:      str       =  self.get_tokenizer()

      a		:      Union[str, Any]       =  VisionTextDualEncoderProcessor(tokenizer=__snake_case		,    image_processor=__snake_case			)

      a		:      Optional[int]       =  self.prepare_image_inputs()

      a		:      List[Any]       =  image_processor(__snake_case		,    return_tensors='np'			)
      a		:      Optional[int]       =  processor(images=__snake_case		,    return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()		,    input_processor[key].sum()		,    delta=1e-2			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      int       =  self.get_image_processor()
      a		:      str       =  self.get_tokenizer()

      a		:      int       =  VisionTextDualEncoderProcessor(tokenizer=__snake_case		,    image_processor=__snake_case			)

      a		:      List[str]       =  'lower newer'

      a		:      Dict       =  processor(text=__snake_case			)

      a		:      Optional[int]       =  tokenizer(__snake_case			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,    encoded_processor[key]			)







    def     lowercase_ (   self				:						str			):
      a		:      List[str]       =  self.get_image_processor()
      a		:      Union[str, Any]       =  self.get_tokenizer()

      a		:      Optional[int]       =  VisionTextDualEncoderProcessor(tokenizer=__snake_case		,    image_processor=__snake_case			)

      a		:      Optional[int]       =  'lower newer'
      a		:      Union[str, Any]       =  self.prepare_image_inputs()

      a		:      Tuple       =  processor(text=__snake_case		,    images=__snake_case			)

      self.assertListEqual(list(inputs.keys()			)		,    ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']			)

      # test if it raises when no input is passed
      with self.assertRaises(__snake_case			):
        processor()







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Any       =  self.get_image_processor()
      a		:      Optional[int]       =  self.get_tokenizer()

      a		:      int       =  VisionTextDualEncoderProcessor(tokenizer=__snake_case		,    image_processor=__snake_case			)

      a		:      Union[str, Any]       =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      a		:      Tuple       =  processor.batch_decode(__snake_case			)
      a		:      List[str]       =  tokenizer.batch_decode(__snake_case			)

      self.assertListEqual(__snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						Any			):
      a		:      Any       =  self.get_image_processor()
      a		:      List[str]       =  self.get_tokenizer()

      a		:      Optional[Any]       =  VisionTextDualEncoderProcessor(tokenizer=__snake_case		,    image_processor=__snake_case			)

      a		:      Dict       =  'lower newer'
      a		:      Any       =  self.prepare_image_inputs()

      a		:      int       =  processor(text=__snake_case		,    images=__snake_case			)

      self.assertListEqual(list(inputs.keys()			)		,    processor.model_input_names			)




'''simple docstring'''






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCAmelCase:      Tuple		 =		'src/transformers'
lowerCAmelCase:      Union[str, Any]		 =		'docs/source/en'
lowerCAmelCase:      Dict		 =		'.'
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  with open(_A				,    'r'				,    encoding='utf-8'				,    newline='\n'       ) as f:
    a		:      Optional[Any]       =  f.readlines()
  # Find the start prompt.
  a		:      Dict       =  0
  while not lines[start_index].startswith(_A       ):
    start_index += 1
  start_index += 1

  a		:      Optional[Any]       =  start_index
  while not lines[end_index].startswith(_A       ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]       ) <= 1:
    start_index += 1
  while len(lines[end_index]       ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowerCAmelCase:      List[str]		 =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:      Dict		 =		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:      Any		 =		re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase:      Dict		 =		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:      List[Any]		 =		direct_transformers_import(TRANSFORMERS_PATH)
def    lowerCamelCase__ (		_A       ):
  a		:      Tuple       =  re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'				,    _A       )
  return [m.group(0       ) for m in matches]
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      List[Any]       =  2 if text == '✅' or text == '❌' else len(_A       )
  a		:      Optional[int]       =  (width - text_length) // 2
  a		:      List[Any]       =  width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent
def    lowerCamelCase__ (		):
  a		:      Union[str, Any]       =  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:      Union[str, Any]       =  {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  a		:      Tuple       =  {name: config.replace('Config'				,    ''       ) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  a		:      int       =  collections.defaultdict(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      Union[str, Any]       =  collections.defaultdict(_A       )
  a		:      int       =  collections.defaultdict(_A       )

  # Let's lookup through all transformers object (once).
  for attr_name in dir(_A       ):
    a		:      Optional[Any]       =  None
    if attr_name.endswith('Tokenizer'       ):
      a		:      int       =  slow_tokenizers
      a		:      Any       =  attr_name[:-9]
    elif attr_name.endswith('TokenizerFast'       ):
      a		:      str       =  fast_tokenizers
      a		:      List[Any]       =  attr_name[:-13]
    elif _re_tf_models.match(_A       ) is not None:
      a		:      Optional[Any]       =  tf_models
      a		:      Dict       =  _re_tf_models.match(_A       ).groups()[0]
    elif _re_flax_models.match(_A       ) is not None:
      a		:      int       =  flax_models
      a		:      Optional[Any]       =  _re_flax_models.match(_A       ).groups()[0]
    elif _re_pt_models.match(_A       ) is not None:
      a		:      Tuple       =  pt_models
      a		:      Optional[int]       =  _re_pt_models.match(_A       ).groups()[0]

    if lookup_dict is not None:
      while len(_A       ) > 0:
        if attr_name in model_name_to_prefix.values():
          a		:      Tuple       =  True
          break
        # Try again after removing the last word in the name
        a		:      Tuple       =  ''.join(camel_case_split(_A       )[:-1]       )

    # Let's build that table!
  a		:      List[Any]       =  list(model_name_to_config.keys()       )
  model_names.sort(key=str.lower       )
  a		:      Tuple       =  ['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  a		:      Tuple       =  [len(_A       ) + 2 for c in columns]
  a		:      str       =  max([len(_A       ) for name in model_names]       ) + 2

  # Build the table per se
  a		:      List[Any]       =  '|' + '|'.join([_center_text(_A				,    _A       ) for c, w in zip(_A				,    _A       )]       ) + '|\n'
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]       ) + "|\n"

  a		:      str       =  {True: '✅', False: '❌'}
  for name in model_names:
    a		:      Any       =  model_name_to_prefix[name]
    a		:      Optional[Any]       =  [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(_A				,    _A       ) for l, w in zip(_A				,    _A       )]       ) + "|\n"
  return table
def    lowerCamelCase__ (		_A=False       ):
  a     ,			a     ,			a     ,			a		:      Tuple       =  _find_text_in_file(
      filename=os.path.join(_A				,    'index.md'       )				,    start_prompt='<!--This table is updated automatically from the auto modules'				,    end_prompt='<!-- End table-->'				,    )
  a		:      Optional[int]       =  get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(_A				,    'index.md'       )				,    'w'				,    encoding='utf-8'				,    newline='\n'       ) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]       )
    else:
      raise ValueError(
          'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'       )


if __name__ == "__main__":
     lowerCAmelCase:      Dict		 =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     lowerCAmelCase:      Any		 =		parser.parse_args()

     check_model_table(args.fix_and_overwrite)