






"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

lowerCamelCase_							      = logging.get_logger(__name__)


lowerCamelCase_							      = {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase_							      = {
    '''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'''
    },
}

lowerCamelCase_							      = {'''facebook/blenderbot-3B''': 128}







class      UpperCamelCase_			(__A	):
	__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__       =							BlenderbotTokenizer





	def __init__( self   :					List[str]      ,      lowerCAmelCase_   :					List[Any]=None      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					str=None      ,      lowerCAmelCase_   :					List[Any]="replace"      ,      lowerCAmelCase_   :					List[Any]="<s>"      ,      lowerCAmelCase_   :					Tuple="</s>"      ,      lowerCAmelCase_   :					str="</s>"      ,      lowerCAmelCase_   :					List[str]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Any="<pad>"      ,      lowerCAmelCase_   :					str="<mask>"      ,      lowerCAmelCase_   :					Optional[int]=False      ,      lowerCAmelCase_   :					Dict=True      ,      **lowerCAmelCase_   :					Any      ,      )		->     Dict:
				super().__init__(
				    lowerCAmelCase_      ,      lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      errors=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      add_prefix_space=lowerCAmelCase_      ,      trim_offsets=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					Optional[int]     	=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space"      ,      lowerCAmelCase_		) != add_prefix_space:
							UpperCAmelCase_	:					str     	=   getattr(lowerCAmelCase_      ,      pre_tok_state.pop("type"		)		)
							UpperCAmelCase_	:					Optional[Any]     	=   add_prefix_space
							UpperCAmelCase_	:					Any     	=   pre_tok_class(**lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[Any]     	=   add_prefix_space

				UpperCAmelCase_	:					Union[str, Any]     	=   "post_processor"
				UpperCAmelCase_	:					Dict     	=   getattr(self.backend_tokenizer      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)
				if tokenizer_component_instance:
							UpperCAmelCase_	:					Any     	=   json.loads(tokenizer_component_instance.__getstate__()		)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
										UpperCAmelCase_	:					Optional[Any]     	=   tuple(state["sep"]		)
							if "cls" in state:
										UpperCAmelCase_	:					Union[str, Any]     	=   tuple(state["cls"]		)

							UpperCAmelCase_	:					str     	=   False

							if state.get("add_prefix_space"      ,      lowerCAmelCase_		) != add_prefix_space:
										UpperCAmelCase_	:					List[str]     	=   add_prefix_space
										UpperCAmelCase_	:					Union[str, Any]     	=   True

							if state.get("trim_offsets"      ,      lowerCAmelCase_		) != trim_offsets:
										UpperCAmelCase_	:					Any     	=   trim_offsets
										UpperCAmelCase_	:					Any     	=   True

							if changes_to_apply:
										UpperCAmelCase_	:					int     	=   getattr(lowerCAmelCase_      ,      state.pop("type"		)		)
										UpperCAmelCase_	:					Dict     	=   component_class(**lowerCAmelCase_		)
										setattr(self.backend_tokenizer      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)





	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				if self._mask_token is None:
							if self.verbose:
										logger.error("Using mask_token, but it is not set yet."		)
							return None
				return str(self._mask_token		)





	@mask_token.setter
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Tuple		)		->     Tuple:
				UpperCAmelCase_	:					Optional[Any]     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else value
				UpperCAmelCase_	:					Any     	=   value





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					Optional[Any]      ,      **lowerCAmelCase_   :					List[str]		)		->     BatchEncoding:
				UpperCAmelCase_	:					List[Any]     	=   kwargs.get("is_split_into_words"      ,      lowerCAmelCase_		)
				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(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Tuple		)		->     BatchEncoding:
				UpperCAmelCase_	:					Optional[int]     	=   kwargs.get("is_split_into_words"      ,      lowerCAmelCase_		)

				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(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   self._tokenizer.model.save(lowerCAmelCase_      ,      name=lowerCAmelCase_		)
				return tuple(lowerCAmelCase_		)





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					"Conversation"		)		->     List[int]:
				UpperCAmelCase_	:					Tuple     	=   []
				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(lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   "  ".join(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.encode(lowerCAmelCase_		)
				if len(lowerCAmelCase_		) > self.model_max_length:
							UpperCAmelCase_	:					Optional[int]     	=   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











"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase_							      = logging.getLogger()

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   argparse.ArgumentParser()
			parser.add_argument("-f"		)
			UpperCAmelCase_	:					Optional[Any]     	=   parser.parse_args()
			return args.f







class      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     None:
				UpperCAmelCase_	:					List[str]     	=   logging.StreamHandler(sys.stdout		)
				logger.addHandler(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[int]     	=   get_gpu_count()

				if n_gpu > 1:
							pass
							# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
							# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
							# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
							# cmd = [sys.executable] + distributed_args + args
							# execute_subprocess_async(cmd, env=self.get_env())
							# XXX: test the results - need to save them first into .json file
				else:
							args.insert(0      ,      "run_glue_deebert.py"		)
							with patch.object(lowerCAmelCase_      ,      "argv"      ,      lowerCAmelCase_		):
										UpperCAmelCase_	:					Dict     	=   run_glue_deebert.main()
										for value in result.values():
													self.assertGreaterEqual(lowerCAmelCase_      ,      0.6_6_6		)





	@slow
	@require_torch_non_multi_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				UpperCAmelCase_	:					Tuple     	=   "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)



