


"""simple docstring"""







import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

a__				:							Optional[Any]						=     logging.get_logger(__name__)


a__				:							Union[str, Any]						=     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a__				:							List[str]						=     {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

a__				:							List[Any]						=     {'''facebook/blenderbot-3B''': 1_2_8}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				(
					    list(range(ord("!"   )  ,						ord("~"   ) + 1   )   ) + list(range(ord("¡"   )  ,						ord("¬"   ) + 1   )   ) + list(range(ord("®"   )  ,						ord("ÿ"   ) + 1   )   )
					)
					__SCREAMING_SNAKE_CASE						   =				bs[:]
					__SCREAMING_SNAKE_CASE						   =				0
					for b in range(2**8   ):
										if b not in bs:
															bs.append(_lowerCamelCase   )
															cs.append(2**8 + n   )
															n += 1
					__SCREAMING_SNAKE_CASE						   =				[chr(_lowerCamelCase   ) for n in cs]
					return dict(zip(_lowerCamelCase  ,						_lowerCamelCase   )   )




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  str    					=			VOCAB_FILES_NAMES
	snake_case__				:  Tuple    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Optional[Any]    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  int    					=			["input_ids", "attention_mask"]




	def __init__( self			:				List[Any]			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				Tuple="replace"			,			UpperCAmelCase__			:				Union[str, Any]="<s>"			,			UpperCAmelCase__			:				Any="</s>"			,			UpperCAmelCase__			:				List[str]="</s>"			,			UpperCAmelCase__			:				Any="<s>"			,			UpperCAmelCase__			:				Union[str, Any]="<unk>"			,			UpperCAmelCase__			:				Optional[int]="<pad>"			,			UpperCAmelCase__			:				Tuple="<mask>"			,			UpperCAmelCase__			:				str=False			,			**UpperCAmelCase__			:				Any			,			)       ->	str:
						__SCREAMING_SNAKE_CASE						   =				AddedToken(__a			,			lstrip=__a			,			rstrip=__a   ) if isinstance(__a			,			__a   ) else bos_token
						__SCREAMING_SNAKE_CASE						   =				AddedToken(__a			,			lstrip=__a			,			rstrip=__a   ) if isinstance(__a			,			__a   ) else eos_token
						__SCREAMING_SNAKE_CASE						   =				AddedToken(__a			,			lstrip=__a			,			rstrip=__a   ) if isinstance(__a			,			__a   ) else sep_token
						__SCREAMING_SNAKE_CASE						   =				AddedToken(__a			,			lstrip=__a			,			rstrip=__a   ) if isinstance(__a			,			__a   ) else cls_token
						__SCREAMING_SNAKE_CASE						   =				AddedToken(__a			,			lstrip=__a			,			rstrip=__a   ) if isinstance(__a			,			__a   ) else unk_token
						__SCREAMING_SNAKE_CASE						   =				AddedToken(__a			,			lstrip=__a			,			rstrip=__a   ) if isinstance(__a			,			__a   ) else pad_token

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

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

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

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						__SCREAMING_SNAKE_CASE						   =				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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	List[str]:
						return len(self.encoder   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	List[Any]:
						return dict(self.encoder			,			**self.added_tokens_encoder   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Union[str, Any]   )       ->	Optional[int]:

						if token in self.cache:
											return self.cache[token]
						__SCREAMING_SNAKE_CASE						   =				tuple(__a   )
						__SCREAMING_SNAKE_CASE						   =				get_pairs(__a   )

						if not pairs:
											return token

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

																if word[i] == first and i < len(__a   ) - 1 and word[i + 1] == second:
																					new_word.append(first + second   )
																					i += 2
																else:
																					new_word.append(word[i]   )
																					i += 1
											__SCREAMING_SNAKE_CASE						   =				tuple(__a   )
											__SCREAMING_SNAKE_CASE						   =				new_word
											if len(__a   ) == 1:
																break
											else:
																__SCREAMING_SNAKE_CASE						   =				get_pairs(__a   )
						__SCREAMING_SNAKE_CASE						   =				" ".join(__a   )
						__SCREAMING_SNAKE_CASE						   =				word
						return word




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				List[Any]   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				[]
						for token in re.findall(self.pat			,			__a   ):
											__SCREAMING_SNAKE_CASE						   =				"".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(__a   ).split(" "   )   )
						return bpe_tokens




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Any   )       ->	List[Any]:
						return self.encoder.get(__a			,			self.encoder.get(self.unk_token   )   )




	def   UpperCAmelCase_      ( self			:				Dict			,			UpperCAmelCase__			:				Any   )       ->	Tuple:
						return self.decoder.get(__a   )




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				List[str]   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				"".join(__a   )
						__SCREAMING_SNAKE_CASE						   =				bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8"			,			errors=self.errors   )
						return text




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				Dict = None   )       ->	int:

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

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

						__SCREAMING_SNAKE_CASE						   =				0
						with open(__a			,			"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 UpperCAmelCase__   : 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!"   )
																					__SCREAMING_SNAKE_CASE						   =				token_index
																writer.write(" ".join(__a   ) + "\n"   )
																index += 1

						return vocab_file, merge_file




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				List[Any]			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[int] = False   )       ->	Dict:

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

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




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				List[str] = None   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				[self.sep_token_id]
						__SCREAMING_SNAKE_CASE						   =				[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   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				List[Any]			,			UpperCAmelCase__			:				List[Any]=False			,			**UpperCAmelCase__			:				Optional[int]   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				kwargs.pop("add_prefix_space"			,			self.add_prefix_space   )
						if (is_split_into_words or add_prefix_space) and (len(__a   ) > 0 and not text[0].isspace()):
											__SCREAMING_SNAKE_CASE						   =				" " + text
						return (text, kwargs)




	def   UpperCAmelCase_      ( self			:				Dict			,			UpperCAmelCase__			:				List[str]			,			UpperCAmelCase__			:				Any = None   )       ->	int:
						return token_ids_a + [self.eos_token_id]




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				List[Any]   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				[]
						for is_user, text in conversation.iter_texts():
											if is_user:
																# We need to space prefix as it's being done within blenderbot
																inputs.append(" " + text   )
											else:
																# Generated responses should contain them already.
																inputs.append(__a   )

						__SCREAMING_SNAKE_CASE						   =				"  ".join(__a   )
						__SCREAMING_SNAKE_CASE						   =				self.encode(__a   )
						if len(__a   ) > self.model_max_length:
											__SCREAMING_SNAKE_CASE						   =				input_ids[-self.model_max_length :]
											logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""   )
						return input_ids





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


_snake_case		=  logging.get_logger(__name__)


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						from transformers import Trainer



						class   UpperCAmelCase_    (       a):







													def __init__(   self,			__a = 101):

																				'''simple docstring'''

																				_lowerCAmelCase     :							str    =    length







													def __len__(   self):

																				'''simple docstring'''

																				return self.length







													def __getitem__(   self,			__a):

																				'''simple docstring'''

																				return i



						class   UpperCAmelCase_    :







													def __call__(   self,			__a):

																				'''simple docstring'''

																				return {"input_ids": torch.tensor(__a), "labels": torch.tensor(__a)}



						class   UpperCAmelCase_    (       nn.Module):







													def __init__(   self):

																				'''simple docstring'''

																				super().__init__()
																				# Add some (unused) params otherwise DDP will complain.
																				_lowerCAmelCase     :							str    =    nn.Linear(120,			80)







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

																				'''simple docstring'''

																				if labels is not None:
																											return torch.tensor(0.0,			device=input_ids.device), input_ids
																				else:
																											return input_ids



class   UpperCAmelCase_    (       a):







							@require_torch_neuroncore
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
														_lowerCAmelCase     :							Tuple    =    self.get_auto_remove_tmp_dir()
														_lowerCAmelCase     :							Optional[int]    =    f"--output_dir {output_dir}".split()
														_lowerCAmelCase     :							List[Any]    =    ["torchrun"] + distributed_args + args
														execute_subprocess_async(__a,			env=self.get_env())
														# successful return here == success - any errors would have caused an error in the sub-call



class   UpperCAmelCase_    (       a):







							@require_torch_multi_gpu
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
														_lowerCAmelCase     :							Any    =    self.get_auto_remove_tmp_dir()
														_lowerCAmelCase     :							Optional[int]    =    f"--output_dir {output_dir}".split()
														_lowerCAmelCase     :							Any    =    ["torchrun"] + distributed_args + args
														execute_subprocess_async(__a,			env=self.get_env())
														# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
						# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
						#
						# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

						_snake_case		=  HfArgumentParser((TrainingArguments,))
						_snake_case		=  parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
						    f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
						)

						# Essentially, what we want to verify in the distributed case is that we get all samples back,
						# in the right order. (this is crucial for prediction for instance)
						for dataset_length in [101, 40, 7]:
												_snake_case		=  DummyDataset(dataset_length)






												def 				A				(					_lowerCamelCase  ):





																			'''simple docstring'''




																			_lowerCAmelCase     :							Dict    =    list(range(len(_lowerCamelCase  )  )  )
																			_lowerCAmelCase     :							Union[str, Any]    =    p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																			if not success and training_args.local_rank == 0:
																										logger.warning(
																										    "Predictions and/or labels do not match expected results:\n  - predictions: "
																										    F"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"  )
																			return {"success": success}

												_snake_case		=  Trainer(
												    model=DummyModel(),
												    args=training_args,
												    data_collator=DummyDataCollator(),
												    eval_dataset=dataset,
												    compute_metrics=compute_metrics,
												)
												_snake_case		=  trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												_snake_case		=  trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)

												_snake_case		=  2

												_snake_case		=  trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												_snake_case		=  trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)



												_snake_case		=  None
