




'''simple docstring'''







import argparse
import collections
import importlib.util
import os
import re


# 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
A :    Any			   ="""src/diffusers"""
A :    Any			   ="""docs/source/en"""
A :    List[Any]			   ="""."""





def 						snake_case_							(_a     :							str   ,       _a     :							Dict   ,       _a     :							List[str]					):



    with open(_a   ,       '''r'''   ,       encoding='''utf-8'''   ,       newline='''\n'''					) as f:
        UpperCAmelCase      	=		f.readlines()
    # Find the start prompt.
    UpperCAmelCase      	=		0
    while not lines[start_index].startswith(_a					):
        start_index += 1
    start_index += 1

    UpperCAmelCase      	=		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 |
A :    str			   ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A :    Tuple			   =re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A :    Dict			   =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.
A :    int			   =re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
A :    Dict			   =importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A :    Union[str, Any]			   =spec.loader.load_module()





def 						snake_case_							(_a     :							List[Any]					):
    UpperCAmelCase      	=		re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''   ,       _a					)
    return [m.group(0					) for m in matches]





def 						snake_case_							(_a     :							str   ,       _a     :							List[str]					):
    UpperCAmelCase      	=		2 if text == '''✅''' or text == '''❌''' else len(_a					)
    UpperCAmelCase      	=		(width - text_length) // 2
    UpperCAmelCase      	=		width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent





def 						snake_case_							():
    UpperCAmelCase      	=		diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    UpperCAmelCase      	=		{
        name: config_mapping_names[code]
        for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
        if code in config_mapping_names
    }
    UpperCAmelCase      	=		{name: config.replace('''ConfigMixin'''   ,       ''''''					) 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.
    UpperCAmelCase      	=		collections.defaultdict(_a					)
    UpperCAmelCase      	=		collections.defaultdict(_a					)
    UpperCAmelCase      	=		collections.defaultdict(_a					)
    UpperCAmelCase      	=		collections.defaultdict(_a					)
    UpperCAmelCase      	=		collections.defaultdict(_a					)

    # Let's lookup through all diffusers object (once).
    for attr_name in dir(_a					):
        UpperCAmelCase      	=		None
        if attr_name.endswith('''Tokenizer'''					):
            UpperCAmelCase      	=		slow_tokenizers
            UpperCAmelCase      	=		attr_name[:-9]
        elif attr_name.endswith('''TokenizerFast'''					):
            UpperCAmelCase      	=		fast_tokenizers
            UpperCAmelCase      	=		attr_name[:-1_3]
        elif _re_tf_models.match(_a					) is not None:
            UpperCAmelCase      	=		tf_models
            UpperCAmelCase      	=		_re_tf_models.match(_a					).groups()[0]
        elif _re_flax_models.match(_a					) is not None:
            UpperCAmelCase      	=		flax_models
            UpperCAmelCase      	=		_re_flax_models.match(_a					).groups()[0]
        elif _re_pt_models.match(_a					) is not None:
            UpperCAmelCase      	=		pt_models
            UpperCAmelCase      	=		_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():
                    UpperCAmelCase      	=		True
                    break
                # Try again after removing the last word in the name
                UpperCAmelCase      	=		''''''.join(camel_case_split(_a					)[:-1]					)

    # Let's build that table!
    UpperCAmelCase      	=		list(model_name_to_config.keys()					)
    model_names.sort(key=str.lower					)
    UpperCAmelCase      	=		['''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).
    UpperCAmelCase      	=		[len(_a					) + 2 for c in columns]
    UpperCAmelCase      	=		max([len(_a					) for name in model_names]					) + 2

    # Build the table per se
    UpperCAmelCase      	=		'''|''' + '''|'''.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"

    UpperCAmelCase      	=		{True: '''✅''', False: '''❌'''}
    for name in model_names:
        UpperCAmelCase      	=		model_name_to_prefix[name]
        UpperCAmelCase      	=		[
            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 						snake_case_							(_a     :							Union[str, Any]=False					):
    UpperCAmelCase	,					UpperCAmelCase	,					UpperCAmelCase	,					UpperCAmelCase      	=		_find_text_in_file(
        filename=os.path.join(_a   ,       '''index.mdx'''					)   ,       start_prompt='''<!--This table is updated automatically from the auto modules'''   ,       end_prompt='''<!-- End table-->'''   ,       )
    UpperCAmelCase      	=		get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(_a   ,       '''index.mdx'''					)   ,       '''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.mdx` has not been updated. Run `make fix-copies` to fix this.'''					)


if __name__ == "__main__":
   A :    Optional[Any]			   =argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   A :    int			   =parser.parse_args()

   check_model_table(args.fix_and_overwrite)










'''simple docstring'''







import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch


class       _a  (     unittest.TestCase							,							__a			):
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		load_tool('''speech-to-text'''    )
           self.tool.setup()
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )







       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(audio=torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )




