




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__A								=       {
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

__A								=       {
    "ctrl": 2_56,
}

__A								=       {
    "Pregnancy": 16_86_29,
    "Christianity": 76_75,
    "Explain": 10_64_23,
    "Fitness": 6_34_40,
    "Saving": 6_31_63,
    "Ask": 2_71_71,
    "Ass": 9_59_85,
    "Joke": 16_35_09,
    "Questions": 4_56_22,
    "Thoughts": 4_96_05,
    "Retail": 5_23_42,
    "Feminism": 16_43_38,
    "Writing": 1_19_92,
    "Atheism": 19_22_63,
    "Netflix": 4_86_16,
    "Computing": 3_96_39,
    "Opinion": 4_32_13,
    "Alone": 4_49_67,
    "Funny": 5_89_17,
    "Gaming": 4_03_58,
    "Human": 40_88,
    "India": 13_31,
    "Joker": 7_71_38,
    "Diet": 3_62_06,
    "Legal": 1_18_59,
    "Norman": 49_39,
    "Tip": 7_26_89,
    "Weight": 5_23_43,
    "Movies": 4_62_73,
    "Running": 2_34_25,
    "Science": 20_90,
    "Horror": 3_77_93,
    "Confession": 6_05_72,
    "Finance": 1_22_50,
    "Politics": 1_63_60,
    "Scary": 19_19_85,
    "Support": 1_26_54,
    "Technologies": 3_25_16,
    "Teenage": 6_61_60,
    "Event": 3_27_69,
    "Learned": 6_74_60,
    "Notion": 18_27_70,
    "Wikipedia": 3_75_83,
    "Books": 66_65,
    "Extract": 7_60_50,
    "Confessions": 10_27_01,
    "Conspiracy": 7_59_32,
    "Links": 6_36_74,
    "Narcissus": 15_04_25,
    "Relationship": 5_47_66,
    "Relationships": 13_47_96,
    "Reviews": 4_16_71,
    "News": 42_56,
    "Translation": 2_68_20,
    "multilingual": 12_84_06,
}







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int] )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       set()
				__lowerCamelCase									=       word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								__lowerCamelCase									=       char

				__lowerCamelCase									=       set(UpperCamelCase__ )
				return pairs







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			VOCAB_FILES_NAMES
			snake_case_  						=			PRETRAINED_VOCAB_FILES_MAP
			snake_case_  						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_  						=			CONTROL_CODES


			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__="<unk>"     ,		**lowerCamelCase__				)       -> int:



							'''simple docstring'''
							super().__init__(unk_token=lowerCamelCase__     ,		**lowerCamelCase__				)

							with open(lowerCamelCase__     ,		encoding='utf-8'				) as vocab_handle:
											__lowerCamelCase									=       json.load(lowerCamelCase__				)
							__lowerCamelCase									=       {v: k for k, v in self.encoder.items()}
							with open(lowerCamelCase__     ,		encoding='utf-8'				) as merges_handle:
											__lowerCamelCase									=       merges_handle.read().split('\n'				)[1:-1]
							__lowerCamelCase									=       [tuple(merge.split()				) for merge in merges]
							__lowerCamelCase									=       dict(zip(lowerCamelCase__     ,		range(len(lowerCamelCase__				)				)				)				)
							__lowerCamelCase									=       {}


			@property
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							return len(self.encoder				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							return dict(self.encoder     ,		**self.added_tokens_encoder				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							if token in self.cache:
											return self.cache[token]
							__lowerCamelCase									=       tuple(lowerCamelCase__				)
							__lowerCamelCase									=       tuple(list(word[:-1]				) + [word[-1] + '</w>']				)
							__lowerCamelCase									=       get_pairs(lowerCamelCase__				)

							if not pairs:
											return token

							while True:
											__lowerCamelCase									=       min(lowerCamelCase__     ,		key=lambda lowerCamelCase__				: self.bpe_ranks.get(lowerCamelCase__     ,		float('inf'				)				)				)
											if bigram not in self.bpe_ranks:
															break
											__lowerCamelCase			,		__lowerCamelCase									=       bigram
											__lowerCamelCase									=       []
											__lowerCamelCase									=       0
											while i < len(lowerCamelCase__				):
															try:
																			__lowerCamelCase									=       word.index(lowerCamelCase__     ,		lowerCamelCase__				)
															except ValueError:
																			new_word.extend(word[i:]				)
																			break
															else:
																			new_word.extend(word[i:j]				)
																			__lowerCamelCase									=       j

															if word[i] == first and i < len(lowerCamelCase__				) - 1 and word[i + 1] == second:
																			new_word.append(first + second				)
																			i += 2
															else:
																			new_word.append(word[i]				)
																			i += 1
											__lowerCamelCase									=       tuple(lowerCamelCase__				)
											__lowerCamelCase									=       new_word
											if len(lowerCamelCase__				) == 1:
															break
											else:
															__lowerCamelCase									=       get_pairs(lowerCamelCase__				)
							__lowerCamelCase									=       '@@ '.join(lowerCamelCase__				)
							__lowerCamelCase									=       word[:-4]
							__lowerCamelCase									=       word
							return word


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       []

							__lowerCamelCase									=       re.findall(R'\S+\n?'     ,		lowerCamelCase__				)

							for token in words:
											split_tokens.extend(list(self.bpe(lowerCamelCase__				).split(' '				)				)				)
							return split_tokens


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							return self.encoder.get(lowerCamelCase__     ,		self.encoder.get(self.unk_token				)				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							return self.decoder.get(lowerCamelCase__     ,		self.unk_token				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       ' '.join(lowerCamelCase__				).replace('@@ '     ,		''				).strip()
							return out_string






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Tuple[str]:



							'''simple docstring'''
							if not os.path.isdir(lowerCamelCase__				):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
											return
							__lowerCamelCase									=       os.path.join(
							    lowerCamelCase__     ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
							__lowerCamelCase									=       os.path.join(
							    lowerCamelCase__     ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']				)

							with open(lowerCamelCase__     ,		'w'     ,		encoding='utf-8'				) as f:
											f.write(json.dumps(self.encoder     ,		indent=2     ,		sort_keys=lowerCamelCase__     ,		ensure_ascii=lowerCamelCase__				) + '\n'				)

							__lowerCamelCase									=       0
							with open(lowerCamelCase__     ,		'w'     ,		encoding='utf-8'				) as writer:
											writer.write('#version: 0.2\n'				)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,		key=lambda lowerCamelCase__				: kv[1]				):
															if index != token_index:
																			logger.warning(
																			    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																			    ' Please check that the tokenizer is not corrupted!'				)
																			__lowerCamelCase									=       token_index
															writer.write(' '.join(lowerCamelCase__				) + '\n'				)
															index += 1

							return vocab_file, merge_file

			# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
			#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
			#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
			#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
			#     return ''.join(tokens_generated_so_far)








from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												return ext

				raise Exception(
				    F"""Unable to determine file format from file extension {path}. """
				    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       parser.add_parser('run'     ,		help='Run a pipeline through the CLI'				)
							run_parser.add_argument('--task'     ,		choices=get_supported_tasks()     ,		help='Task to run'				)
							run_parser.add_argument('--input'     ,		type=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		default=-1     ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,		)
							run_parser.add_argument('--overwrite'     ,		action='store_true'     ,		help='Allow overwriting the output file.'				)
							run_parser.set_defaults(func=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

        # Saving data
							if self._nlp.binary_output:
											__lowerCamelCase									=       self._reader.save_binary(lowerCamelCase__				)
											logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
											self._reader.save(lowerCamelCase__				)


