
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


lowerCamelCase_  :	Any 				=					logging.get_logger(__name__)

lowerCamelCase_  :	Any 				=					{"""tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :	Tuple 				=					{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  str    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  Optional[Any]    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Union[str, Any]    =	["""input_ids""", """attention_mask"""]
			__UpperCamelCase :  Dict    =	None

			def __init__(    self	:    Any  ,		snake_case_	:    Optional[int]=None  ,		snake_case_	:    str=None  ,		snake_case_	:    Optional[Any]=None  ,		snake_case_	:    str="<unk>"  ,		snake_case_	:    Optional[Any]="<s>"  ,		snake_case_	:    List[Any]="</s>"  ,		snake_case_	:    Union[str, Any]="<pad>"  ,		snake_case_	:    Any=False  ,		snake_case_	:    List[Any]=False  ,		**snake_case_	:    Optional[Any]  ,		):
							super().__init__(
							    snake_case_  ,		snake_case_  ,		tokenizer_file=snake_case_  ,		unk_token=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		pad_token=snake_case_  ,		add_prefix_space=snake_case_  ,		clean_up_tokenization_spaces=snake_case_  ,		**snake_case_  ,		)
							UpperCamelCase_:							Tuple		=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
							if pre_tok_state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
											UpperCamelCase_:							Union[str, Any]		=  getattr(snake_case_  ,		pre_tok_state.pop("""type""" ) )
											UpperCamelCase_:							List[str]		=  add_prefix_space
											UpperCamelCase_:							Dict		=  pre_tok_class(**snake_case_ )

							UpperCamelCase_:							Dict		=  add_prefix_space

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		*snake_case_	:    Optional[int]  ,		**snake_case_	:    Optional[int] ):
							UpperCamelCase_:							str		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )
							if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
											    """ pretokenized inputs.""" )

							return super()._batch_encode_plus(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[Any]  ,		*snake_case_	:    List[Any]  ,		**snake_case_	:    Any ):
							UpperCamelCase_:							Union[str, Any]		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
											    """ pretokenized inputs.""" )

							return super()._encode_plus(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    str  ,		snake_case_	:    Optional[str] = None ):
							UpperCamelCase_:							Dict		=  self._tokenizer.model.save(snake_case_  ,		name=snake_case_ )
							return tuple(snake_case_ )




			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    "Conversation" ):
							UpperCamelCase_:							List[Any]		=  []
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(snake_case_  ,		add_special_tokens=snake_case_ ) + [self.eos_token_id] )

							if len(snake_case_ ) > self.model_max_length:
											UpperCamelCase_:							Tuple		=  input_ids[-self.model_max_length :]
							return input_ids








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_  :	Any 				=					5_00_00
lowerCamelCase_  :	Any 				=					50_00

lowerCamelCase_				,    lowerCamelCase_  :	Tuple 				=					os.path.split(__file__)
lowerCamelCase_  :	int 				=					os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(0       ,      len(lowerCamelCase  )       ,      lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  dataset[i : i + batch_size]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							List[str]		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(0       ,      lowerCamelCase       ,      lowerCamelCase  ):
												UpperCamelCase_:							Union[str, Any]		=  dataset[i : i + batch_size]







def     A__   (  )					->		Tuple:
				UpperCamelCase_:							int		=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:							Union[str, Any]		=  [
				    (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""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (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""": 10_00}),
				]

				UpperCamelCase_:							Tuple		=  [
				    (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""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (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""": 10_00}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print("""generating dataset"""  )
								UpperCamelCase_:							int		=  datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
								UpperCamelCase_:							Optional[int]		=  generate_example_dataset(
								    os.path.join(lowerCamelCase       ,      """dataset.arrow"""  )       ,      lowerCamelCase       ,      num_examples=lowerCamelCase       ,      seq_shapes={"""list""": (1_00,)}       ,      )
								print("""first set of iterations"""  )
								for func, kwargs in functions:
												print(func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(lowerCamelCase       ,      **lowerCamelCase  )

								print("""shuffling dataset"""  )
								UpperCamelCase_:							Dict		=  dataset.shuffle()
								print("""Second set of iterations (after shuffling"""  )
								for func, kwargs in functions_shuffled:
												print("""shuffled """       ,      func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(
												    lowerCamelCase       ,      **lowerCamelCase  )

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


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






