





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__A				:    List[str]       =   get_logger(__name__)

class 	A_       (enum.Enum			):
	UpperCAmelCase__					=			'''all_checks'''
	UpperCAmelCase__					=			'''basic_checks'''
	UpperCAmelCase__					=			'''no_checks'''

class 	A_       (a_			):
	pass

class 	A_       (a_			):
	pass

class 	A_       (a_			):
	pass

class 	A_       (a_			):
	pass





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None )  ->				Dict:







			'''simple docstring'''

			if expected_checksums is None:
						logger.info('''Unable to verify checksums.''' )
						return
			if len(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) )
			if len(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) > 0:
						raise UnexpectedDownloadedFile(str(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) )
			UpperCAmelCase   		=				[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			UpperCAmelCase   		=				''' for ''' + verification_name if verification_name is not None else ''''''
			if len(UpperCamelCase__ ) > 0:
						raise NonMatchingChecksumError(
						    F"""Checksums didn't match{for_verification_name}:\n"""
						    F"""{bad_urls}\n"""
						    '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error''' )
			logger.info('''All the checksums matched successfully''' + for_verification_name )

class 	A_       (a_			):
	pass

class 	A_       (a_			):
	pass

class 	A_       (a_			):
	pass

class 	A_       (a_			):
	pass





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Any:







			'''simple docstring'''

			if expected_splits is None:
						logger.info('''Unable to verify splits sizes.''' )
						return
			if len(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) > 0:
						raise ExpectedMoreSplits(str(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) )
			if len(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) > 0:
						raise UnexpectedSplits(str(set(UpperCamelCase__ ) - set(UpperCamelCase__ ) ) )
			UpperCAmelCase   		=				[
			    {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(UpperCamelCase__ ) > 0:
						raise NonMatchingSplitsSizesError(str(UpperCamelCase__ ) )
			logger.info('''All the splits matched successfully.''' )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ = True )  ->				dict:







			'''simple docstring'''

			if record_checksum:
						UpperCAmelCase   		=				shaaaa()
						with open(UpperCamelCase__			,			'''rb''' ) as f:
									for chunk in iter(lambda: f.read(1 << 20 )			,			B'''''' ):
												m.update(UpperCamelCase__ )
									UpperCAmelCase   		=				m.hexdigest()
			else:
						UpperCAmelCase   		=				None
			return {"num_bytes": os.path.getsize(UpperCamelCase__ ), "checksum": checksum}






def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Optional[int]:







			'''simple docstring'''

			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A				:    int       =   logging.get_logger(__name__)

__A				:    Tuple       =   {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''big_bird'''
	def __init__(    self							,					_A=5_0_3_5_8							,					_A=7_6_8							,					_A=1_2							,					_A=1_2							,					_A=3_0_7_2							,					_A="gelu_new"							,					_A=0.1							,					_A=0.1							,					_A=4_0_9_6							,					_A=2							,					_A=0.02							,					_A=1E-12							,					_A=True							,					_A=0							,					_A=1							,					_A=2							,					_A=6_6							,					_A="block_sparse"							,					_A=True							,					_A=False							,					_A=6_4							,					_A=3							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    pad_token_id=_A							,					bos_token_id=_A							,					eos_token_id=_A							,					sep_token_id=_A							,					**_A							,					)

				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				layer_norm_eps
				UpperCAmelCase   		=				use_cache

				UpperCAmelCase   		=				rescale_embeddings
				UpperCAmelCase   		=				attention_type
				UpperCAmelCase   		=				use_bias
				UpperCAmelCase   		=				block_size
				UpperCAmelCase   		=				num_random_blocks
				UpperCAmelCase   		=				classifier_dropout

class 	A_       (a_			):
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				if self.task == "multiple-choice":
							UpperCAmelCase   		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]		)



