

"""simple docstring"""





from typing import Mapping

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


_a					 =				logging.get_logger(__name__)




class    _lowerCAmelCase    (      _a  ):







 """simple docstring"""







 __UpperCAmelCase      :   str  						=      "mt5"
 __UpperCAmelCase      :   Dict  						=      ["past_key_values"]


 def __init__(     self				:     Any,   UpperCAmelCase__				:     List[str]=2_5_0_1_1_2,   UpperCAmelCase__				:     Optional[int]=5_1_2,   UpperCAmelCase__				:     Optional[Any]=6_4,   UpperCAmelCase__				:     Optional[int]=1_0_2_4,   UpperCAmelCase__				:     Optional[int]=8,   UpperCAmelCase__				:     Union[str, Any]=None,   UpperCAmelCase__				:     List[Any]=6,   UpperCAmelCase__				:     Tuple=3_2,   UpperCAmelCase__				:     Union[str, Any]=1_2_8,   UpperCAmelCase__				:     List[Any]=0.1,   UpperCAmelCase__				:     Union[str, Any]=1E-6,   UpperCAmelCase__				:     Dict=1.0,   UpperCAmelCase__				:     Dict="gated-gelu",   UpperCAmelCase__				:     Union[str, Any]=True,   UpperCAmelCase__				:     List[Any]=True,   UpperCAmelCase__				:     Any="T5Tokenizer",   UpperCAmelCase__				:     List[Any]=False,   UpperCAmelCase__				:     List[str]=0,   UpperCAmelCase__				:     Tuple=1,   UpperCAmelCase__				:     Optional[Any]=0,   **UpperCAmelCase__				:     List[Any],   ):
    super().__init__(
        is_encoder_decoder=__SCREAMING_SNAKE_CASE,   tokenizer_class=__SCREAMING_SNAKE_CASE,   tie_word_embeddings=__SCREAMING_SNAKE_CASE,   pad_token_id=__SCREAMING_SNAKE_CASE,   eos_token_id=__SCREAMING_SNAKE_CASE,   decoder_start_token_id=__SCREAMING_SNAKE_CASE,   **__SCREAMING_SNAKE_CASE,   )
    __lowercase								=		vocab_size
    __lowercase								=		d_model
    __lowercase								=		d_kv
    __lowercase								=		d_ff
    __lowercase								=		num_layers
    __lowercase								=		(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __lowercase								=		num_heads
    __lowercase								=		relative_attention_num_buckets
    __lowercase								=		relative_attention_max_distance
    __lowercase								=		dropout_rate
    __lowercase								=		layer_norm_epsilon
    __lowercase								=		initializer_factor
    __lowercase								=		feed_forward_proj
    __lowercase								=		use_cache

    __lowercase								=		self.feed_forward_proj.split("-"      )
    __lowercase								=		act_info[-1]
    __lowercase								=		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":
       __lowercase								=		'''gelu_new'''


 @property
 def    _lowercase					(     self				:     Dict      ):
    return self.d_model


 @property
 def    _lowercase					(     self				:     List[str]      ):
    return self.num_heads



 @property
 def    _lowercase					(     self				:     Any      ):
    return self.num_layers







class    _lowerCAmelCase    (      _a  ):







 """simple docstring"""







 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    _lowercase					(     self				:     str      ):
    __lowercase								=		{
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
       __lowercase								=		'''past_encoder_sequence + sequence'''
       __lowercase								=		{0: '''batch'''}
       __lowercase								=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
       __lowercase								=		{0: '''batch''', 1: '''decoder_sequence'''}
       __lowercase								=		{0: '''batch''', 1: '''decoder_sequence'''}

    if self.use_past:
       self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE,   direction="inputs"      )

    return common_inputs


 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    _lowercase					(     self				:     Optional[int]      ):
    return 1_3



 @property
 def    _lowercase					(     self				:     List[Any]      ):
    return 5E-4








import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__UpperCAmelCase			:			Optional[Any]			      =					2 << 20  # 2mb
__UpperCAmelCase			:			List[Any]			      =					get_logger(__name__)



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self._options.get_all_options().get('''runner'''    )
              return runner in [None, "DirectRunner", "PortableRunner"]




def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		FileSystems
       if fs.exists(SCREAMING_SNAKE_CASE_							):
              if force_upload:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.create(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''rb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		local_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            remote_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						Any 						=		local_file.read(SCREAMING_SNAKE_CASE_							)


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]=False							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		FileSystems
       if os.path.exists(SCREAMING_SNAKE_CASE_							):
              if force_download:
                     logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.open(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''wb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            local_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						str 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)

