



import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
snake_case__					:     Dict    		=   False
snake_case__					:     Dict    		=   False



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	return TrainCommand(_SCREAMING_SNAKE_CASE   )

class   _A   ( _lowercase	):


						'''simple docstring'''





						@staticmethod
						def     _snake_case	(	lowerCamelCase	:							ArgumentParser	):






							'''simple docstring'''



							__lowercase		       =			parser.add_parser("train"							,      help="CLI tool to train a model on a task."	)

							train_parser.add_argument(
							    "--train_data"							,      type=lowerCamelCase							,      required=lowerCamelCase							,      help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."							,      )
							train_parser.add_argument(
							    "--column_label"							,      type=lowerCamelCase							,      default=0							,      help="Column of the dataset csv file with example labels."	)
							train_parser.add_argument(
							    "--column_text"							,      type=lowerCamelCase							,      default=1							,      help="Column of the dataset csv file with example texts."	)
							train_parser.add_argument(
							    "--column_id"							,      type=lowerCamelCase							,      default=2							,      help="Column of the dataset csv file with example ids."	)
							train_parser.add_argument(
							    "--skip_first_row"							,      action="store_true"							,      help="Skip the first row of the csv file (headers)."	)

							train_parser.add_argument("--validation_data"							,      type=lowerCamelCase							,      default=""							,      help="path to validation dataset."	)
							train_parser.add_argument(
							    "--validation_split"							,      type=lowerCamelCase							,      default=0.1							,      help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."							,      )

							train_parser.add_argument("--output"							,      type=lowerCamelCase							,      default="./"							,      help="path to saved the trained model."	)

							train_parser.add_argument(
							    "--task"							,      type=lowerCamelCase							,      default="text_classification"							,      help="Task to train the model on."	)
							train_parser.add_argument(
							    "--model"							,      type=lowerCamelCase							,      default="bert-base-uncased"							,      help="Model's name or path to stored model."	)
							train_parser.add_argument("--train_batch_size"							,      type=lowerCamelCase							,      default=32							,      help="Batch size for training."	)
							train_parser.add_argument("--valid_batch_size"							,      type=lowerCamelCase							,      default=64							,      help="Batch size for validation."	)
							train_parser.add_argument("--learning_rate"							,      type=lowerCamelCase							,      default=3e-5							,      help="Learning rate."	)
							train_parser.add_argument("--adam_epsilon"							,      type=lowerCamelCase							,      default=1e-08							,      help="Epsilon for Adam optimizer."	)
							train_parser.set_defaults(func=lowerCamelCase	)



						def __init__(	self	:							int							,      lowerCamelCase	:							Namespace	):






							'''simple docstring'''



							__lowercase		       =			logging.get_logger("transformers-cli/training"	)

							__lowercase		       =			"tf" if is_tf_available() else "torch"

							os.makedirs(args.output							,      exist_ok=lowerCamelCase	)
							__lowercase		       =			args.output

							__lowercase		       =			args.column_label
							__lowercase		       =			args.column_text
							__lowercase		       =			args.column_id

							self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""	)
							if args.task == "text_classification":
								__lowercase		       =			TextClassificationPipeline.from_pretrained(args.model	)
							elif args.task == "token_classification":
								raise NotImplementedError
							elif args.task == "question_answering":
								raise NotImplementedError

							self.logger.info(f"""Loading dataset from {args.train_data}"""	)
							__lowercase		       =			Processor.create_from_csv(
							    args.train_data							,      column_label=args.column_label							,      column_text=args.column_text							,      column_id=args.column_id							,      skip_first_row=args.skip_first_row							,      )
							__lowercase		       =			None
							if args.validation_data:
								self.logger.info(f"""Loading validation dataset from {args.validation_data}"""	)
								__lowercase		       =			Processor.create_from_csv(
								    args.validation_data							,      column_label=args.column_label							,      column_text=args.column_text							,      column_id=args.column_id							,      skip_first_row=args.skip_first_row							,      )

							__lowercase		       =			args.validation_split
							__lowercase		       =			args.train_batch_size
							__lowercase		       =			args.valid_batch_size
							__lowercase		       =			args.learning_rate
							__lowercase		       =			args.adam_epsilon



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							if self.framework == "tf":
								return self.run_tf()
							return self.run_torch()



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							raise NotImplementedError



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							self.pipeline.fit(
							    self.train_dataset							,      validation_data=self.valid_dataset							,      validation_split=self.validation_split							,      learning_rate=self.learning_rate							,      adam_epsilon=self.adam_epsilon							,      train_batch_size=self.train_batch_size							,      valid_batch_size=self.valid_batch_size							,      )

							# Save trained pipeline
							self.pipeline.save_pretrained(self.output	)








import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


snake_case__					:     List[str]    		=   logging.get_logger(__name__)


snake_case__					:     Optional[Any]    		=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
snake_case__					:     Optional[Any]    		=   {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

snake_case__					:     List[str]    		=   {
    """allenai/led-base-16384""": 1_63_84,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def    snake_case_				(    ):
	__lowercase		       =			(
	    list(range(ord("!"   )  ,	ord("~"   ) + 1   )   ) + list(range(ord("¡"   )  ,	ord("¬"   ) + 1   )   ) + list(range(ord("®"   )  ,	ord("ÿ"   ) + 1   )   )
	)
	__lowercase		       =			bs[:]
	__lowercase		       =			0
	for b in range(2**8   ):
		if b not in bs:
			bs.append(_SCREAMING_SNAKE_CASE   )
			cs.append(2**8 + n   )
			n += 1
	__lowercase		       =			[chr(_SCREAMING_SNAKE_CASE   ) for n in cs]
	return dict(zip(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			set()
	__lowercase		       =			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)   )
		__lowercase		       =			char
	return pairs

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     List[str]      							=      VOCAB_FILES_NAMES
						_snake_case			:     Union[str, Any]      							=      PRETRAINED_VOCAB_FILES_MAP
						_snake_case			:     Dict      							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_snake_case			:     Union[str, Any]      							=      ["""input_ids""", """attention_mask"""]



						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]="replace"							,      lowerCamelCase	:							Dict="<s>"							,      lowerCamelCase	:							Dict="</s>"							,      lowerCamelCase	:							Optional[Any]="</s>"							,      lowerCamelCase	:							Any="<s>"							,      lowerCamelCase	:							List[str]="<unk>"							,      lowerCamelCase	:							Union[str, Any]="<pad>"							,      lowerCamelCase	:							Any="<mask>"							,      lowerCamelCase	:							str=False							,      **lowerCamelCase	:							Optional[Any]							,      ):






							'''simple docstring'''



							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else bos_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else eos_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else sep_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else cls_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else unk_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else mask_token

							super().__init__(
							    errors=lowerCamelCase							,      bos_token=lowerCamelCase							,      eos_token=lowerCamelCase							,      unk_token=lowerCamelCase							,      sep_token=lowerCamelCase							,      cls_token=lowerCamelCase							,      pad_token=lowerCamelCase							,      mask_token=lowerCamelCase							,      add_prefix_space=lowerCamelCase							,      **lowerCamelCase							,      )

							with open(lowerCamelCase							,      encoding="utf-8"	) as vocab_handle:
								__lowercase		       =			json.load(lowerCamelCase	)
							__lowercase		       =			{v: k for k, v in self.encoder.items()}
							__lowercase		       =			errors  # how to handle errors in decoding
							__lowercase		       =			bytes_to_unicode()
							__lowercase		       =			{v: k for k, v in self.byte_encoder.items()}
							with open(lowerCamelCase							,      encoding="utf-8"	) as merges_handle:
								__lowercase		       =			merges_handle.read().split("\n"	)[1:-1]
							__lowercase		       =			[tuple(merge.split()	) for merge in bpe_merges]
							__lowercase		       =			dict(zip(lowerCamelCase							,      range(len(lowerCamelCase	)	)	)	)
							__lowercase		       =			{}
							__lowercase		       =			add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							__lowercase		       =			re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)



						@property
						# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							return len(self.encoder	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							return dict(self.encoder							,      **self.added_tokens_encoder	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							if token in self.cache:
								return self.cache[token]
							__lowercase		       =			tuple(lowerCamelCase	)
							__lowercase		       =			get_pairs(lowerCamelCase	)

							if not pairs:
								return token

							while True:
								__lowercase		       =			min(lowerCamelCase							,      key=lambda lowerCamelCase	: self.bpe_ranks.get(lowerCamelCase							,      float("inf"	)	)	)
								if bigram not in self.bpe_ranks:
									break
								__lowercase       ,    __lowercase		       =			bigram
								__lowercase		       =			[]
								__lowercase		       =			0
								while i < len(lowerCamelCase	):
									try:
										__lowercase		       =			word.index(lowerCamelCase							,      lowerCamelCase	)
									except ValueError:
										new_word.extend(word[i:]	)
										break
									else:
										new_word.extend(word[i:j]	)
										__lowercase		       =			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
								__lowercase		       =			tuple(lowerCamelCase	)
								__lowercase		       =			new_word
								if len(lowerCamelCase	) == 1:
									break
								else:
									__lowercase		       =			get_pairs(lowerCamelCase	)
							__lowercase		       =			" ".join(lowerCamelCase	)
							__lowercase		       =			word
							return word



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			[]
							for token in re.findall(self.pat							,      lowerCamelCase	):
								__lowercase		       =			"".join(
								    self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
								bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase	).split(" "	)	)
							return bpe_tokens



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							return self.encoder.get(lowerCamelCase							,      self.encoder.get(self.unk_token	)	)



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							return self.decoder.get(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			"".join(lowerCamelCase	)
							__lowercase		       =			bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"							,      errors=self.errors	)
							return text



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Optional[str] = None	):






							'''simple docstring'''



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

							__lowercase		       =			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!"	)
										__lowercase		       =			token_index
									writer.write(" ".join(lowerCamelCase	) + "\n"	)
									index += 1

							return vocab_file, merge_file



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							Optional[List[int]] = None	):






							'''simple docstring'''



							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							__lowercase		       =			[self.cls_token_id]
							__lowercase		       =			[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							Optional[List[int]] = None							,      lowerCamelCase	:							bool = False	):






							'''simple docstring'''



							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowerCamelCase							,      token_ids_a=lowerCamelCase							,      already_has_special_tokens=lowerCamelCase	)

							if token_ids_a is None:
								return [1] + ([0] * len(lowerCamelCase	)) + [1]
							return [1] + ([0] * len(lowerCamelCase	)) + [1, 1] + ([0] * len(lowerCamelCase	)) + [1]



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							Optional[List[int]] = None	):






							'''simple docstring'''



							__lowercase		       =			[self.sep_token_id]
							__lowercase		       =			[self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep	) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple=False							,      **lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			kwargs.pop("add_prefix_space"							,      self.add_prefix_space	)
							if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase	) > 0 and not text[0].isspace()):
								__lowercase		       =			" " + text
							return (text, kwargs)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Union[Dict[str, EncodedInput], BatchEncoding]							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      ):






							'''simple docstring'''



							__lowercase		       =			super()._pad(
							    encoded_inputs=lowerCamelCase							,      max_length=lowerCamelCase							,      padding_strategy=lowerCamelCase							,      pad_to_multiple_of=lowerCamelCase							,      return_attention_mask=lowerCamelCase							,      )

							# Load from model defaults
							if return_attention_mask is None:
								__lowercase		       =			"attention_mask" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
								__lowercase		       =			encoded_inputs[self.model_input_names[0]]
								# `global_attention_mask` need to have the same length as other (sequential) inputs.
								__lowercase		       =			len(encoded_inputs["global_attention_mask"]	) != len(lowerCamelCase	)

								if needs_to_be_padded:
									__lowercase		       =			len(lowerCamelCase	) - len(encoded_inputs["global_attention_mask"]	)

									if self.padding_side == "right":
										# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
										__lowercase		       =			(
										    encoded_inputs["global_attention_mask"] + [-1] * difference
										)
									elif self.padding_side == "left":
										__lowercase		       =			[-1] * difference + encoded_inputs[
										    "global_attention_mask"
										]
									else:
										raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

							return encoded_inputs



