


import argparse
from collections import defaultdict

import yaml


lowercase_    		=     """docs/source/en/_toctree.yml"""


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase					)     ->	str:
						_a     			=      defaultdict(_A					)
						_a     			=      []
						_a     			=      []
						for doc in doc_list:
												if "local" in doc:
																		counts[doc["local"]] += 1

												if doc["title"].lower() == "overview":
																		overview_doc.append({'local': doc['local'], 'title': doc['title']}					)
												else:
																		new_doc_list.append(_A					)

						_a     			=      new_doc_list
						_a     			=      [key for key, value in counts.items() if value > 1]

						_a     			=      []
						for duplicate_key in duplicates:
												_a     			=      list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}					)
												if len(_A					) > 1:
																		raise ValueError(
																		    f"""{duplicate_key} is present several times in the documentation table of content at """
																		    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
																		    'others.'					)
												# Only add this once
												new_doc.append({'local': duplicate_key, 'title': titles[0]}					)

						# Add none duplicate-keys
						new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]					)
						_a     			=      sorted(_A				,  key=lambda _UpperCAmelCase					: s["title"].lower()					)

						# "overview" gets special treatment and is always first
						if len(_A					) > 1:
												raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'					)

						overview_doc.extend(_A					)

						# Sort
						return overview_doc


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase=False					)     ->	Optional[int]:







						with open(_A				,  encoding='utf-8'					) as f:
												_a     			=      yaml.safe_load(f.read()					)

						# Get to the API doc
						_a     			=      0
						while content[api_idx]["title"] != "API":
												api_idx += 1
						_a     			=      content[api_idx]['sections']

						# Then to the model doc
						_a     			=      0
						while api_doc[scheduler_idx]["title"] != "Schedulers":
												scheduler_idx += 1

						_a     			=      api_doc[scheduler_idx]['sections']
						_a     			=      clean_doc_toc(_A					)

						_a     			=      False
						if new_scheduler_doc != scheduler_doc:
												_a     			=      True
												if overwrite:
																		_a     			=      new_scheduler_doc

						if diff:
												if overwrite:
																		_a     			=      api_doc
																		with open(_A				,  'w'				,  encoding='utf-8'					) as f:
																								f.write(yaml.dump(_A				,  allow_unicode=_A					)					)
												else:
																		raise ValueError(
																		    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'					)


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase=False					)     ->	Tuple:







						with open(_A				,  encoding='utf-8'					) as f:
												_a     			=      yaml.safe_load(f.read()					)

						# Get to the API doc
						_a     			=      0
						while content[api_idx]["title"] != "API":
												api_idx += 1
						_a     			=      content[api_idx]['sections']

						# Then to the model doc
						_a     			=      0
						while api_doc[pipeline_idx]["title"] != "Pipelines":
												pipeline_idx += 1

						_a     			=      False
						_a     			=      api_doc[pipeline_idx]['sections']
						_a     			=      []

						# sort sub pipeline docs
						for pipeline_doc in pipeline_docs:
												if "section" in pipeline_doc:
																		_a     			=      pipeline_doc['section']
																		_a     			=      clean_doc_toc(_A					)
																		if overwrite:
																								_a     			=      new_sub_pipeline_doc
												new_pipeline_docs.append(_A					)

						# sort overall pipeline doc
						_a     			=      clean_doc_toc(_A					)

						if new_pipeline_docs != pipeline_docs:
												_a     			=      True
												if overwrite:
																		_a     			=      new_pipeline_docs

						if diff:
												if overwrite:
																		_a     			=      api_doc
																		with open(_A				,  'w'				,  encoding='utf-8'					) as f:
																								f.write(yaml.dump(_A				,  allow_unicode=_A					)					)
												else:
																		raise ValueError(
																		    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'					)


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

		check_scheduler_doc(args.fix_and_overwrite)
		check_pipeline_doc(args.fix_and_overwrite)






'''simple docstring'''







import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCAmelCase     :  Optional[int]											=    logging.get_logger(__name__)

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  Optional[Any]											=    {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase     :  List[str]											=    {
    """Salesforce/codegen-350M-mono""": 20_48,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     CodeGenTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            if kwargs.pop('add_bos_token'   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							kwargs.pop('name_or_path'   ,						'' )
                   raise ValueError(
                       'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
                       'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
                       F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
                       F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
                       'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
                       ' so that the fast tokenizer works correctly.' )

            _lowerCAmelCase     :				List[str]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				List[Any]       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				Dict       =							add_prefix_space
                   _lowerCAmelCase     :				List[Any]       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Tuple       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							super().decode(
                token_ids=snake_case__   ,						skip_special_tokens=snake_case__   ,						clean_up_tokenization_spaces=snake_case__   ,						**snake_case__   ,						)

            if truncate_before_pattern is not None and len(snake_case__ ) > 0:
                   _lowerCAmelCase     :				Optional[int]       =							self.truncate(snake_case__   ,						snake_case__ )

            return decoded_text







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            def find_re(snake_case__   ,						snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				List[Any]       =							pattern.search(snake_case__   ,						snake_case__ )
                   return m.start() if m else -1

            _lowerCAmelCase     :				Any       =							[re.compile(snake_case__   ,						re.MULTILINE ) for pattern in truncate_before_pattern]

            _lowerCAmelCase     :				Dict       =							list(re.finditer('^print'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[int]       =							completion[: prints[1].start()]

            _lowerCAmelCase     :				Optional[int]       =							list(re.finditer('^def'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[Any]       =							completion[: defs[1].start()]

            _lowerCAmelCase     :				int       =							0

            _lowerCAmelCase     :				Tuple       =							[
                pos for pos in [find_re(snake_case__   ,						snake_case__   ,						snake_case__ ) for terminal in terminals] if pos != -1
            ]

            if len(snake_case__ ) > 0:
                   return completion[: min(snake_case__ )]
            else:
                   return completion

