





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


UpperCamelCase__   : Union[str, Any]					  = logging.get_logger(__name__)

UpperCamelCase__   : Dict					  = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase__   : str					  = {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase__   : List[str]					  = {
    """squeezebert/squeezebert-uncased""": 5_12,
    """squeezebert/squeezebert-mnli""": 5_12,
    """squeezebert/squeezebert-mnli-headless""": 5_12,
}


UpperCamelCase__   : Tuple					  = {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       VOCAB_FILES_NAMES
					lowerCamelCase     :     int			    =       PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase     :     Optional[int]			    =       PRETRAINED_INIT_CONFIGURATION
					lowerCamelCase     :     Tuple			    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase     :     Union[str, Any]			    =       SqueezeBertTokenizer





					def __init__(						self    :  int ,					__lowercase    :  Dict=None ,					__lowercase    :  int=None ,					__lowercase    :  List[str]=True ,					__lowercase    :  Dict="[UNK]" ,					__lowercase    :  List[str]="[SEP]" ,					__lowercase    :  Union[str, Any]="[PAD]" ,					__lowercase    :  Optional[Any]="[CLS]" ,					__lowercase    :  List[str]="[MASK]" ,					__lowercase    :  int=True ,					__lowercase    :  Optional[int]=None ,					**__lowercase    :  Tuple ,					):






						'''simple docstring'''







						super().__init__(
						    __lowercase ,					tokenizer_file=__lowercase ,					do_lower_case=__lowercase ,					unk_token=__lowercase ,					sep_token=__lowercase ,					pad_token=__lowercase ,					cls_token=__lowercase ,					mask_token=__lowercase ,					tokenize_chinese_chars=__lowercase ,					strip_accents=__lowercase ,					**__lowercase ,					)

						UpperCAmelCase_								=     json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
						if (
						    normalizer_state.get("""lowercase""" ,					__lowercase		) != do_lower_case
						    or normalizer_state.get("""strip_accents""" ,					__lowercase		) != strip_accents
						    or normalizer_state.get("""handle_chinese_chars""" ,					__lowercase		) != tokenize_chinese_chars
						):
							UpperCAmelCase_								=     getattr(__lowercase ,					normalizer_state.pop("""type"""		)		)
							UpperCAmelCase_								=     do_lower_case
							UpperCAmelCase_								=     strip_accents
							UpperCAmelCase_								=     tokenize_chinese_chars
							UpperCAmelCase_								=     normalizer_class(**__lowercase		)

						UpperCAmelCase_								=     do_lower_case





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  str ,					__lowercase    :  str=None		):






						'''simple docstring'''







						UpperCAmelCase_								=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     [self.sep_token_id]
						UpperCAmelCase_								=     [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		) * [0] + len(token_ids_a + sep		) * [1]





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  str ,					__lowercase    :  Optional[str] = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     self._tokenizer.model.save(__lowercase ,					name=__lowercase		)
						return tuple(__lowercase		)









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__   : Tuple					  = 5_00_00
UpperCamelCase__   : Any					  = 50_00

UpperCamelCase__  ,			UpperCamelCase__   : Tuple					  = os.path.split(__file__)
UpperCamelCase__   : Union[str, Any]					  = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def    A_(			A				,      A   ):
	for i in range(A   ):
		UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A   ):
	for i in range(0				,      len(A   )				,      A   ):
		UpperCAmelCase_								=     dataset[i : i + batch_size]




@get_duration
def    A_(			A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(A   ):
			UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(0				,      A				,      A   ):
			UpperCAmelCase_								=     dataset[i : i + batch_size]




def    A_(			):
	UpperCAmelCase_								=     {"""num examples""": SPEED_TEST_N_EXAMPLES}
	UpperCAmelCase_								=     [
	    (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}),
	]

	UpperCAmelCase_								=     [
	    (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"""   )
		UpperCAmelCase_								=     datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""   )   ), """numbers""": datasets.Value("""float32"""   )}   )
		UpperCAmelCase_								=     generate_example_dataset(
		    os.path.join(A				,      """dataset.arrow"""   )				,      A				,      num_examples=A				,      seq_shapes={"""list""": (100,)}				,      )
		print("""first set of iterations"""   )
		for func, kwargs in functions:
			print(func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(A				,      **A   )

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

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


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


