


'''simple docstring'''






from manim import *

class 							a__(      lowerCamelCase__			):







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Dict       =  Rectangle(height=0.5		,    width=0.5			)
      a		:      Union[str, Any]       =  Rectangle(height=0.46		,    width=0.46			).set_stroke(width=0			)

      a		:      Tuple       =  Rectangle(height=0.25		,    width=0.25			)

      a		:      Union[str, Any]       =  [mem.copy() for i in range(6			)]
      a		:      Optional[int]       =  [mem.copy() for i in range(6			)]
      a		:      List[Any]       =  VGroup(*__snake_case			).arrange(__snake_case		,    buff=0			)
      a		:      Optional[Any]       =  VGroup(*__snake_case			).arrange(__snake_case		,    buff=0			)
      a		:      Any       =  VGroup(__snake_case		,    __snake_case			).arrange(__snake_case		,    buff=0			)
      a		:      Dict       =  Text('CPU'		,    font_size=24			)
      a		:      int       =  Group(__snake_case		,    __snake_case			).arrange(__snake_case		,    buff=0.5		,    aligned_edge=__snake_case			)
      cpu.move_to([-2.5, -0.5, 0]			)
      self.add(__snake_case			)

      a		:      Union[str, Any]       =  [mem.copy() for i in range(4			)]
      a		:      Optional[int]       =  VGroup(*__snake_case			).arrange(__snake_case		,    buff=0			)
      a		:      Optional[Any]       =  Text('GPU'		,    font_size=24			)
      a		:      List[str]       =  Group(__snake_case		,    __snake_case			).arrange(__snake_case		,    buff=0.5		,    aligned_edge=__snake_case			)
      gpu.move_to([-1, -1, 0]			)
      self.add(__snake_case			)

      a		:      Tuple       =  [mem.copy() for i in range(6			)]
      a		:      Optional[int]       =  VGroup(*__snake_case			).arrange(__snake_case		,    buff=0			)

      a		:      Tuple       =  Text('Model'		,    font_size=24			)
      a		:      List[str]       =  Group(__snake_case		,    __snake_case			).arrange(__snake_case		,    buff=0.5		,    aligned_edge=__snake_case			)
      model.move_to([3, -1.0, 0]			)
      self.add(__snake_case			)

      a		:      Optional[int]       =  []
      a		:      Optional[int]       =  []

      for i, rect in enumerate(__snake_case			):
        a		:      Union[str, Any]       =  fill.copy().set_fill(__snake_case		,    opacity=0.8			)
        target.move_to(__snake_case			)
        model_arr.append(__snake_case			)

        a		:      str       =  Rectangle(height=0.46		,    width=0.46			).set_stroke(width=0.0			).set_fill(__snake_case		,    opacity=0.8			)
        cpu_target.move_to(cpu_left_col_base[i]			)
        model_cpu_arr.append(__snake_case			)

      self.add(*__snake_case		,    *__snake_case			)

      a		:      List[str]       =  [meta_mem.copy() for i in range(6			)]
      a		:      Any       =  [meta_mem.copy() for i in range(6			)]
      a		:      str       =  VGroup(*__snake_case			).arrange(__snake_case		,    buff=0			)
      a		:      Optional[Any]       =  VGroup(*__snake_case			).arrange(__snake_case		,    buff=0			)
      a		:      Any       =  VGroup(__snake_case		,    __snake_case			).arrange(__snake_case		,    buff=0			)
      a		:      Tuple       =  Text('Disk'		,    font_size=24			)
      a		:      Tuple       =  Group(__snake_case		,    __snake_case			).arrange(__snake_case		,    buff=0.5		,    aligned_edge=__snake_case			)
      disk.move_to([-4, -1.25, 0]			)
      self.add(__snake_case		,    __snake_case			)

      a		:      int       =  Square(side_length=2.2			)
      key.move_to([-5, 2, 0]			)

      a		:      List[str]       =  MarkupText(
          F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""		,    font_size=18		,    )

      key_text.move_to([-5, 2.4, 0]			)

      self.add(__snake_case		,    __snake_case			)

      a		:      List[str]       =  MarkupText(
          F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""		,    font_size=18		,    )

      blue_text.next_to(__snake_case		,    DOWN * 2.4		,    aligned_edge=key_text.get_left()			)
      self.add(__snake_case			)

      a		:      List[Any]       =  MarkupText(
          F"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""		,    font_size=24		,    )
      step_a.move_to([2, 2, 0]			)

      self.play(Write(__snake_case			)			)

      a		:      Dict       =  Square(0.3			)
      input.set_fill(__snake_case		,    opacity=1.0			)
      input.set_stroke(width=0.0			)
      input.next_to(model_base[0]		,    __snake_case		,    buff=0.5			)

      self.play(Write(__snake_case			)			)

      input.generate_target()
      input.target.next_to(model_arr[0]		,    direction=__snake_case		,    buff=0.02			)
      self.play(MoveToTarget(__snake_case			)			)

      self.play(FadeOut(__snake_case			)			)

      a		:      Dict       =  Arrow(start=__snake_case		,    end=__snake_case		,    color=__snake_case		,    buff=0.5			)
      a.next_to(model_arr[0].get_left()		,    __snake_case		,    buff=0.2			)

      model_cpu_arr[0].generate_target()
      model_cpu_arr[0].target.move_to(gpu_rect[0]			)

      a		:      Any       =  MarkupText(
          F"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""		,    font_size=24		,    )
      step_a.move_to([2, 2, 0]			)

      self.play(Write(__snake_case		,    run_time=3			)			)

      a		:      List[Any]       =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

      self.play(
          Write(__snake_case			)		,    Circumscribe(model_arr[0]		,    color=__snake_case		,    **__snake_case			)		,    Circumscribe(model_cpu_arr[0]		,    color=__snake_case		,    **__snake_case			)		,    Circumscribe(gpu_rect[0]		,    color=__snake_case		,    **__snake_case			)		,    )
      self.play(MoveToTarget(model_cpu_arr[0]			)			)

      a		:      Optional[int]       =  a.copy()
      for i in range(6			):
        a_c.next_to(model_arr[i].get_right() + 0.02		,    __snake_case		,    buff=0.2			)

        input.generate_target()
        input.target.move_to(model_arr[i].get_right() + 0.02			)

        a		:      Optional[Any]       =  AnimationGroup(
            FadeOut(__snake_case		,    run_time=0.5			)		,    MoveToTarget(__snake_case		,    run_time=0.5			)		,    FadeIn(__snake_case		,    run_time=0.5			)		,    lag_ratio=0.2			)

        self.play(__snake_case			)

        model_cpu_arr[i].generate_target()
        model_cpu_arr[i].target.move_to(cpu_left_col_base[i]			)

        if i < 5:
          model_cpu_arr[i + 1].generate_target()
          model_cpu_arr[i + 1].target.move_to(gpu_rect[0]			)
          if i >= 1:
            a		:      List[str]       =  0.7

          self.play(
              Circumscribe(model_arr[i]		,    **__snake_case			)		,    Circumscribe(cpu_left_col_base[i]		,    **__snake_case			)		,    Circumscribe(cpu_left_col_base[i + 1]		,    color=__snake_case		,    **__snake_case			)		,    Circumscribe(gpu_rect[0]		,    color=__snake_case		,    **__snake_case			)		,    Circumscribe(model_arr[i + 1]		,    color=__snake_case		,    **__snake_case			)		,    )
          if i < 1:
            self.play(
                MoveToTarget(model_cpu_arr[i]			)		,    MoveToTarget(model_cpu_arr[i + 1]			)		,    )
          else:
            self.play(
                MoveToTarget(model_cpu_arr[i]		,    run_time=0.7			)		,    MoveToTarget(model_cpu_arr[i + 1]		,    run_time=0.7			)		,    )
        else:
          model_cpu_arr[i].generate_target()
          model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]			)
          input.generate_target()
          input.target.next_to(model_arr[-1].get_right()		,    RIGHT + 0.02		,    buff=0.2			)

          self.play(
              Circumscribe(model_arr[-1]		,    color=__snake_case		,    **__snake_case			)		,    Circumscribe(cpu_left_col_base[-1]		,    color=__snake_case		,    **__snake_case			)		,    Circumscribe(gpu_rect[0]		,    color=__snake_case		,    **__snake_case			)		,    )

          self.play(MoveToTarget(model_cpu_arr[i]			)			)

        a		:      Dict       =  a_c
        a		:      Optional[Any]       =  a_c.copy()

      input.generate_target()
      input.target.next_to(model_base[-1]		,    RIGHT + 0.02		,    buff=0.5			)
      self.play(
          FadeOut(__snake_case			)		,    FadeOut(__snake_case		,    run_time=0.5			)		,    )

      a		:      List[str]       =  MarkupText(F"""Inference on a model too large for GPU memory\nis successfully completed."""		,    font_size=24			)
      step_a.move_to([2, 2, 0]			)

      self.play(Write(__snake_case		,    run_time=3			)		,    MoveToTarget(__snake_case			)			)

      self.wait()




'''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)