

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

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


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

_UpperCAmelCase		:   Union[str, Any]										=       {
    """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"""
    },
}

_UpperCAmelCase		:   int										=       {"""facebook/blenderbot-3B""": 128}





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







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






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







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE					: int       			=			PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE					: int       			=			['''input_ids''', '''attention_mask''']




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case="replace"      ,   snake_case="<s>"      ,   snake_case="</s>"      ,   snake_case="</s>"      ,   snake_case="<s>"      ,   snake_case="<unk>"      ,   snake_case="<pad>"      ,   snake_case="<mask>"      ,   snake_case=False      ,   **snake_case      ,   ):
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else bos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else eos_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else sep_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else cls_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else unk_token
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else pad_token

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

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

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

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											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     a							(					self       ):
											return len(self.encoder       )




				def     a							(					self       ):
											return dict(self.encoder      ,   **self.added_tokens_encoder       )




				def     a							(					self      ,   snake_case       ):
											if token in self.cache:
																		return self.cache[token]
											snake_case_							=    tuple(snake_case       )
											snake_case_							=    get_pairs(snake_case       )

											if not pairs:
																		return token

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

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




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    []
											for token in re.findall(self.pat      ,   snake_case       ):
																		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(snake_case       ).split(' '       )       )
											return bpe_tokens




				def     a							(					self      ,   snake_case       ):
											return self.encoder.get(snake_case      ,   self.encoder.get(self.unk_token       )       )




				def     a							(					self      ,   snake_case       ):
											return self.decoder.get(snake_case       )




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    ''.join(snake_case       )
											snake_case_							=    bytearray([self.byte_decoder[c] for c in text]       ).decode('utf-8'      ,   errors=self.errors       )
											return text




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											if not os.path.isdir(snake_case       ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
																		return
											snake_case_							=    os.path.join(
											    snake_case      ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
											snake_case_							=    os.path.join(
											    snake_case      ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

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

											snake_case_							=    0
											with open(snake_case      ,   '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 snake_case       : 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!'       )
																																snake_case_							=    token_index
																									writer.write(' '.join(snake_case       ) + '\n'       )
																									index += 1

											return vocab_file, merge_file




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = False       ):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case      ,   token_ids_a=snake_case      ,   already_has_special_tokens=snake_case       )

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




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											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     a							(					self      ,   snake_case      ,   snake_case=False      ,   **snake_case       ):
											snake_case_							=    kwargs.pop('add_prefix_space'      ,   self.add_prefix_space       )
											if (is_split_into_words or add_prefix_space) and (len(snake_case       ) > 0 and not text[0].isspace()):
																		snake_case_							=    ' ' + text
											return (text, kwargs)




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											return token_ids_a + [self.eos_token_id]




				def     a							(					self      ,   snake_case       ):
											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(snake_case       )

											snake_case_							=    '  '.join(snake_case       )
											snake_case_							=    self.encode(snake_case       )
											if len(snake_case       ) > self.model_max_length:
																		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







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase		:   Optional[int]										=       5_0000
_UpperCAmelCase		:   Dict										=       5000

_UpperCAmelCase				,						_UpperCAmelCase		:   Optional[int]										=       os.path.split(__file__)
_UpperCAmelCase		:   List[str]										=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(UpperCamelCase__      ):
														snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(0   ,							len(UpperCamelCase__      )   ,							UpperCamelCase__      ):
														snake_case_							=    dataset[i : i + batch_size]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(UpperCamelCase__      ):
																					snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(0   ,							UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    dataset[i : i + batch_size]





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    {'num examples': SPEED_TEST_N_EXAMPLES}
							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]

							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'      )
														snake_case_							=    datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
														snake_case_							=    generate_example_dataset(
														    os.path.join(UpperCamelCase__   ,							'dataset.arrow'      )   ,							UpperCamelCase__   ,							num_examples=UpperCamelCase__   ,							seq_shapes={'list': (100,)}   ,							)
														print('first set of iterations'      )
														for func, kwargs in functions:
																					print(func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(UpperCamelCase__   ,							**UpperCamelCase__      )

														print('shuffling dataset'      )
														snake_case_							=    dataset.shuffle()
														print('Second set of iterations (after shuffling'      )
														for func, kwargs in functions_shuffled:
																					print('shuffled '   ,							func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(
																					    UpperCamelCase__   ,							**UpperCamelCase__      )

							with open(UpperCamelCase__   ,							'wb'      ) as f:
														f.write(json.dumps(UpperCamelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()




