"""simple docstring"""
from typing import Mapping

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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""umt5"""
				_lowerCAmelCase     :    List[Any]			  =						["""past_key_values"""]

				def __init__(				self		,    lowerCAmelCase=25_01_12		,    lowerCAmelCase=5_12		,    lowerCAmelCase=64		,    lowerCAmelCase=10_24		,    lowerCAmelCase=8		,    lowerCAmelCase=None		,    lowerCAmelCase=6		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=0.1		,    lowerCAmelCase=1E-6		,    lowerCAmelCase=1.0		,    lowerCAmelCase="gated-gelu"		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase="T5Tokenizer"		,    lowerCAmelCase=True		,    lowerCAmelCase=0		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    is_encoder_decoder=lowerCAmelCase		,    tokenizer_class=lowerCAmelCase		,    tie_word_embeddings=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    decoder_start_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			vocab_size
										snake_case								=			d_model
										snake_case								=			d_kv
										snake_case								=			d_ff
										snake_case								=			num_layers
										snake_case								=			(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										snake_case								=			num_heads
										snake_case								=			relative_attention_num_buckets
										snake_case								=			relative_attention_max_distance
										snake_case								=			dropout_rate
										snake_case								=			layer_norm_epsilon
										snake_case								=			initializer_factor
										snake_case								=			feed_forward_proj
										snake_case								=			use_cache

										snake_case								=			self.feed_forward_proj.split('-'    )
										snake_case								=			act_info[-1]
										snake_case								=			act_info[0] == 'gated'

										if len(lowerCAmelCase    ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase    ) > 2:
																raise ValueError(
																    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
																    '\'gated-gelu\' or \'relu\''    )

										if feed_forward_proj == "gated-gelu":
																snake_case								=			'gelu_new'

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.d_model

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.num_heads

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.num_layers





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			{
										    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
										    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
										}
										if self.use_past:
																snake_case								=			'past_encoder_sequence + sequence'
																snake_case								=			{0: 'batch'}
																snake_case								=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
										else:
																snake_case								=			{0: 'batch', 1: 'decoder_sequence'}
																snake_case								=			{0: 'batch', 1: 'decoder_sequence'}

										if self.use_past:
																self.fill_with_past_key_values_(lowerCAmelCase		,    direction='inputs'    )

										return common_inputs

				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 13

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 5E-4



"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE__      						=  get_logger(__name__)



class   lowerCAmelCase_    (			enum.Enum					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""all_checks"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""basic_checks"""
				_lowerCAmelCase     :    Optional[int]			  =						"""no_checks"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict ,			_UpperCamelCase       :				str=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					)					)					)
						snake_case								=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						snake_case								=			' 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   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict					)					->				Tuple:





						"""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					)					)					)
						snake_case								=			[
						    {'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    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				bool = True					)					->				dict:





						"""simple docstring"""






						if record_checksum:
												snake_case								=			shaaaa()
												with open(_UpperCamelCase ,			'rb'					) as f:
																		for chunk in iter(lambda: f.read(1 << 2_0					) ,			B''					):
																								m.update(_UpperCamelCase					)
																		snake_case								=			m.hexdigest()
						else:
												snake_case								=			None
						return {"num_bytes": os.path.getsize(_UpperCamelCase					), "checksum": checksum}





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int]					)					->				int:





						"""simple docstring"""






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


