




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[int]    ,				**_lowerCAmelCase				: List[Any]						):




 """simple docstring"""







 return RunBeamCommand(
     args.dataset    ,				args.name    ,				args.cache_dir    ,				args.beam_pipeline_options    ,				args.data_dir    ,				args.all_configs    ,				args.save_info or args.save_infos    ,				args.ignore_verifications    ,				args.force_redownload    ,				**lowerCAmelCase__    ,				)


class       _UpperCamelCase   (					__A		):
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :Optional[Any]   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    parser.add_parser("run_beam"			,   help="Run a Beam dataset processing pipeline"   )
       run_beam_parser.add_argument("dataset"			,   type=UpperCamelCase__			,   help="Name of the dataset to download"   )
       run_beam_parser.add_argument("--name"			,   type=UpperCamelCase__			,   default=UpperCamelCase__			,   help="Dataset config name"   )
       run_beam_parser.add_argument(
           "--cache_dir"			,   type=UpperCamelCase__			,   default=UpperCamelCase__			,   help="Cache directory where the datasets are stored"			,   )
       run_beam_parser.add_argument(
           "--beam_pipeline_options"			,   type=UpperCamelCase__			,   default=""			,   help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"			,   )
       run_beam_parser.add_argument(
           "--data_dir"			,   type=UpperCamelCase__			,   default=UpperCamelCase__			,   help="Can be used to specify a manual directory to get the files from"			,   )
       run_beam_parser.add_argument("--all_configs"			,   action="store_true"			,   help="Test all dataset configurations"   )
       run_beam_parser.add_argument("--save_info"			,   action="store_true"			,   help="Save the dataset infos file"   )
       run_beam_parser.add_argument(
           "--ignore_verifications"			,   action="store_true"			,   help="Run the test without checksums and splits checks"   )
       run_beam_parser.add_argument("--force_redownload"			,   action="store_true"			,   help="Force dataset redownload"   )
       # aliases
       run_beam_parser.add_argument("--save_infos"			,   action="store_true"			,   help="alias for save_info"   )
       run_beam_parser.set_defaults(func=UpperCamelCase__   )
      def __init__(      self :Tuple			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :int			,   lowerCamelCase :Any			,   lowerCamelCase :Any			,   lowerCamelCase :Dict			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :Any			,   lowerCamelCase :Tuple			,   **lowerCamelCase :Union[str, Any]			,   )  ->      str:
       UpperCAmelCase__							=    dataset
       UpperCAmelCase__							=    name
       UpperCAmelCase__							=    cache_dir
       UpperCAmelCase__							=    beam_pipeline_options
       UpperCAmelCase__							=    data_dir
       UpperCAmelCase__							=    all_configs
       UpperCAmelCase__							=    save_infos
       UpperCAmelCase__							=    ignore_verifications
       UpperCAmelCase__							=    force_redownload
       UpperCAmelCase__							=    config_kwargs







      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      int:
       import apache_beam as beam

       if self._name is not None and self._all_configs:
        print("Both parameters `name` and `all_configs` can\'t be used at once."   )
        exit(1   )
       UpperCAmelCase__							=    self._dataset, self._name
       UpperCAmelCase__							=    dataset_module_factory(UpperCamelCase__   )
       UpperCAmelCase__							=    import_main_class(dataset_module.module_path   )
       UpperCAmelCase__							=    []
       if self._beam_pipeline_options:
        UpperCAmelCase__							=    beam.options.pipeline_options.PipelineOptions(
            flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(","   ) if opt]   )
       else:
        UpperCAmelCase__							=    None
       if self._all_configs and len(builder_cls.BUILDER_CONFIGS   ) > 0:
        for builder_config in builder_cls.BUILDER_CONFIGS:
         builders.append(
             builder_cls(
                 config_name=builder_config.name			,   data_dir=self._data_dir			,   hash=dataset_module.hash			,   beam_options=UpperCamelCase__			,   cache_dir=self._cache_dir			,   base_path=dataset_module.builder_kwargs.get("base_path"   )			,   )   )
       else:
        builders.append(
            builder_cls(
                config_name=UpperCamelCase__			,   data_dir=self._data_dir			,   beam_options=UpperCamelCase__			,   cache_dir=self._cache_dir			,   base_path=dataset_module.builder_kwargs.get("base_path"   )			,   **self._config_kwargs			,   )   )

       for builder in builders:
        builder.download_and_prepare(
            download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
            if not self._force_redownload
            else DownloadMode.FORCE_REDOWNLOAD			,   download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH   )			,   verification_mode=VerificationMode.NO_CHECKS
            if self._ignore_verifications
            else VerificationMode.ALL_CHECKS			,   try_from_hf_gcs=UpperCamelCase__			,   )
        if self._save_infos:
         builder._save_infos()

       print("Apache beam run successful."   )

       # If save_infos=True, the dataset infos file is created next to the loaded module file.
       # Let's move it to the original directory of the dataset script, to allow the user to
       # upload them on S3 at the same time afterwards.
       if self._save_infos:
        UpperCAmelCase__							=    os.path.join(builder_cls.get_imported_module_dir()			,   config.DATASETDICT_INFOS_FILENAME   )

        UpperCAmelCase__							=    Path(UpperCamelCase__   ).name + '.py'

        UpperCAmelCase__							=    os.path.join(UpperCamelCase__			,   UpperCamelCase__   )
        if os.path.isfile(UpperCamelCase__   ):
         UpperCAmelCase__							=    os.path.dirname(UpperCamelCase__   )
        elif os.path.isfile(UpperCamelCase__   ):
         UpperCAmelCase__							=    path
        else:  # in case of a remote dataset
         print(f'''Dataset Infos file saved at {dataset_infos_path}'''   )
         exit(1   )

        # Move datasetinfo back to the user
        UpperCAmelCase__							=    os.path.join(UpperCamelCase__			,   config.DATASETDICT_INFOS_FILENAME   )
        copyfile(UpperCamelCase__			,   UpperCamelCase__   )
        print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''   )













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										=      'src/diffusers'
A										=      'docs/source/en'
A										=      '.'



def        _lowerCamelCase(      lowerCAmelCase__					:		List[Any]     ,     lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_ :						List[str]    				= start_index
				while not lines[end_index].startswith(lowerCAmelCase__   ):
								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										=      'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
A										=      re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A										=      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										=      re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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



def        _lowerCamelCase(      lowerCAmelCase__					:		Any   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,     lowerCAmelCase__   )
				return [m.group(0   ) for m in matches]



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[Any]     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Any    				= 2 if text == '✅' or text == '❌' else len(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						int    				= (width - text_length) // 2
				SCREAMING_SNAKE_CASE_ :						str    				= width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {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.
				SCREAMING_SNAKE_CASE_ :						int    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						str    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Dict    				= collections.defaultdict(lowerCAmelCase__   )

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

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

    # Let's build that table!
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= list(model_name_to_config.keys()   )
				model_names.sort(key=str.lower   )
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= ['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).
				SCREAMING_SNAKE_CASE_ :						str    				= [len(lowerCAmelCase__   ) + 2 for c in columns]
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= max([len(lowerCAmelCase__   ) for name in model_names]   ) + 2

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

				SCREAMING_SNAKE_CASE_ :						int    				= {True: '✅', False: '❌'}
				for name in model_names:
								SCREAMING_SNAKE_CASE_ :						int    				= model_name_to_prefix[name]
								SCREAMING_SNAKE_CASE_ :						int    				= [
								    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(lowerCAmelCase__     ,     lowerCAmelCase__   ) for l, w in zip(lowerCAmelCase__     ,     lowerCAmelCase__   )]   ) + "|\n"
				return table



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[int]=False   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						str    				= _find_text_in_file(
				    filename=os.path.join(lowerCAmelCase__     ,     'index.mdx'   )     ,     start_prompt='<!--This table is updated automatically from the auto modules'     ,     end_prompt='<!-- End table-->'     ,     )
				SCREAMING_SNAKE_CASE_ :						Tuple    				= get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase__     ,     '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										=      argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				A										=      parser.parse_args()

				check_model_table(args.fix_and_overwrite)