
"""simple docstring"""






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__UpperCAmelCase       =  logging.get_logger(__name__)




class 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :List[str]				 =						None






@experimental
def 						_snake_case       (	lowercase__		:  Dict      ,     lowercase__		:  int      ,     lowercase__		:  Any      ,     lowercase__		:  Any      ,     lowercase__		:  Union[str, Any]      ,     lowercase__		:  Dict      ,     lowercase__		:  Optional[Any]		)  ->				Optional[int]:


			'''simple docstring'''





			if ParallelBackendConfig.backend_name is None:
						return _map_with_multiprocessing_pool(
						    lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)

			return _map_with_joblib(lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)






def 						_snake_case       (	lowercase__		:  Optional[int]      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  str      ,     lowercase__		:  Any      ,     lowercase__		:  str      ,     lowercase__		:  Tuple		)  ->				List[str]:


			'''simple docstring'''





			lowerCAmelCase_    :List[str]			     =  num_proc if num_proc <= len(lowercase__		) else len(lowercase__		)
			lowerCAmelCase_    :Union[str, Any]			     =  []  # We organize the splits ourselve (contiguous splits)
			for index in range(lowercase__		):
						lowerCAmelCase_    :List[str]			     =  len(lowercase__		) // num_proc
						lowerCAmelCase_    :List[Any]			     =  len(lowercase__		) % num_proc
						lowerCAmelCase_    :str			     =  div * index + min(lowercase__      ,     lowercase__		)
						lowerCAmelCase_    :List[str]			     =  start + div + (1 if index < mod else 0)
						split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)		)

			if len(lowercase__		) != sum(len(i[1]		) for i in split_kwds		):
						raise ValueError(
						    f"""Error dividing inputs iterable among processes. """
						    f"""Total number of objects {len(lowercase__		)}, """
						    f"""length: {sum(len(i[1]		) for i in split_kwds		)}"""		)

			logger.info(
			    f"""Spawning {num_proc} processes for {len(lowercase__		)} objects in slices of {[len(i[1]		) for i in split_kwds]}"""		)
			lowerCAmelCase_     ,       lowerCAmelCase_    :int			     =  None, None
			if not disable_tqdm:
						lowerCAmelCase_     ,       lowerCAmelCase_    :Dict			     =  (RLock(),), tqdm.set_lock
			with Pool(lowercase__      ,     initargs=lowercase__      ,     initializer=lowercase__		) as pool:
						lowerCAmelCase_    :Tuple			     =  pool.map(lowercase__      ,     lowercase__		)
			logger.info(f"""Finished {num_proc} processes"""		)
			lowerCAmelCase_    :str			     =  [obj for proc_res in mapped for obj in proc_res]
			logger.info(f"""Unpacked {len(lowercase__		)} objects"""		)

			return mapped






def 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  List[Any]      ,     lowercase__		:  Tuple      ,     lowercase__		:  List[Any]      ,     lowercase__		:  str      ,     lowercase__		:  str      ,     lowercase__		:  str		)  ->				Any:


			'''simple docstring'''





			import joblib

			with joblib.parallel_backend(ParallelBackendConfig.backend_name      ,     n_jobs=lowercase__		):
						return joblib.Parallel()(
						    joblib.delayed(lowercase__		)((function, obj, types, None, True, None)		) for obj in iterable		)






@experimental
@contextlib.contextmanager
def 						_snake_case       (	lowercase__		:  str		)  ->				List[str]:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  backend_name

			if backend_name == "spark":
						from joblibspark import register_spark

						register_spark()

						# TODO: call create_cache_and_write_probe if "download" in steps
						# TODO: raise NotImplementedError when Dataset.map etc is called

			try:
						yield
			finally:
						lowerCAmelCase_    :Optional[Any]			     =  None










from typing import Mapping

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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "t5"
       snake_case      =    ["past_key_values"]
       snake_case      =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3_2128				,       _SCREAMING_SNAKE_CASE=512				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=6				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=1				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					vocab_size
         A_      :				int		     =					d_model
         A_      :				Optional[Any]		     =					d_kv
         A_      :				str		     =					d_ff
         A_      :				int		     =					num_layers
         A_      :				Any		     =					(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A_      :				Optional[Any]		     =					num_heads
         A_      :				Union[str, Any]		     =					relative_attention_num_buckets
         A_      :				Dict		     =					relative_attention_max_distance
         A_      :				List[str]		     =					dropout_rate
         A_      :				Dict		     =					layer_norm_epsilon
         A_      :				str		     =					initializer_factor
         A_      :				Dict		     =					feed_forward_proj
         A_      :				int		     =					use_cache

         A_      :				Optional[int]		     =					self.feed_forward_proj.split('''-'''		)
         A_      :				Optional[Any]		     =					act_info[-1]
         A_      :				Optional[Any]		     =					act_info[0] == '''gated'''

         if len(_SCREAMING_SNAKE_CASE		) > 1 and act_info[0] != "gated" or len(_SCREAMING_SNAKE_CASE		) > 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\''''		)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
           A_      :				Tuple		     =					'''gelu_new'''

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE				,       is_encoder_decoder=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       @property
       def        _snake_case       (      self		)->Mapping[str, Mapping[int, str]]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
           A_      :				List[str]		     =					'''past_encoder_sequence + sequence'''
           A_      :				Optional[int]		     =					{0: '''batch'''}
           A_      :				str		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
           A_      :				Optional[int]		     =					{0: '''batch''', 1: '''decoder_sequence'''}
           A_      :				List[Any]		     =					{0: '''batch''', 1: '''decoder_sequence'''}

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

         return common_inputs







       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         return 13




