

from typing import Mapping

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


a_     =			logging.get_logger(__name__)

a_     =			{
    '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 _UpperCamelCase							(							_a      ):
			'''simple docstring'''







			lowerCamelCase__							="""umt5"""
			lowerCamelCase__							=["""past_key_values"""]
			def __init__(	self							:       Optional[Any]					,					a							:       int=25_0112					,					a							:       List[str]=512					,					a							:       Optional[int]=64					,					a							:       str=1024					,					a							:       Union[str, Any]=8					,					a							:       str=None					,					a							:       List[str]=6					,					a							:       str=32					,					a							:       List[Any]=128					,					a							:       int=0.1					,					a							:       str=1e-6					,					a							:       List[Any]=1.0					,					a							:       Dict="gated-gelu"					,					a							:       Optional[Any]=True					,					a							:       int=True					,					a							:       Any="T5Tokenizer"					,					a							:       Tuple=True					,					a							:       str=0					,					a							:       List[Any]=1					,					a							:       str=0					,					**a							:       Optional[Any]					,					)							->				Optional[int]:

				"""simple docstring"""

				super().__init__(
				    is_encoder_decoder=__UpperCAmelCase					,					tokenizer_class=__UpperCAmelCase					,					tie_word_embeddings=__UpperCAmelCase					,					pad_token_id=__UpperCAmelCase					,					eos_token_id=__UpperCAmelCase					,					decoder_start_token_id=__UpperCAmelCase					,					**__UpperCAmelCase					,					)
				SCREAMING_SNAKE_CASE      :					List[str]    =				vocab_size
				SCREAMING_SNAKE_CASE      :					int    =				d_model
				SCREAMING_SNAKE_CASE      :					Any    =				d_kv
				SCREAMING_SNAKE_CASE      :					List[Any]    =				d_ff
				SCREAMING_SNAKE_CASE      :					Dict    =				num_layers
				SCREAMING_SNAKE_CASE      :					Dict    =				(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				SCREAMING_SNAKE_CASE      :					Tuple    =				num_heads
				SCREAMING_SNAKE_CASE      :					Tuple    =				relative_attention_num_buckets
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				relative_attention_max_distance
				SCREAMING_SNAKE_CASE      :					List[str]    =				dropout_rate
				SCREAMING_SNAKE_CASE      :					int    =				layer_norm_epsilon
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				initializer_factor
				SCREAMING_SNAKE_CASE      :					Any    =				feed_forward_proj
				SCREAMING_SNAKE_CASE      :					int    =				use_cache

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.feed_forward_proj.split("-"		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				act_info[-1]
				SCREAMING_SNAKE_CASE      :					Tuple    =				act_info[0] == "gated"

				if len(__UpperCAmelCase		) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase		) > 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":
					SCREAMING_SNAKE_CASE      :					Tuple    =				"gelu_new"
			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Any:

				"""simple docstring"""

				return self.d_model
			@property
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[str]:

				"""simple docstring"""

				return self.num_heads






			@property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Union[str, Any]:

				"""simple docstring"""

				return self.num_layers






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







			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"past_encoder_sequence + sequence"
					SCREAMING_SNAKE_CASE      :					List[Any]    =				{0: "batch"}
					SCREAMING_SNAKE_CASE      :					List[Any]    =				{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE      :					int    =				{0: "batch", 1: "decoder_sequence"}
					SCREAMING_SNAKE_CASE      :					List[Any]    =				{0: "batch", 1: "decoder_sequence"}

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

				return common_inputs
			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				int:

				"""simple docstring"""

				return 13






			@property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				str:

				"""simple docstring"""

				return 5e-4



"""simple docstring"""







import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__lowercase    					=      datasets.utils.logging.get_logger(__name__)






@dataclass
class 						_A  (  datasets.BuilderConfig  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       1_0_0_0_0
    UpperCAmelCase       :							Optional[List[str]]	=       None
    UpperCAmelCase       :							Optional[datasets.Features]	=       None






class 						_A  (  datasets.ArrowBasedBuilder  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       ParquetConfig






    def 	__snake_case							(					self				:    Tuple):
      return datasets.DatasetInfo(features=self.config.features)






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    str):
      if not self.config.data_files:
        raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''')
      a						:		str      							=			dl_manager.download_and_extract(self.config.data_files)
      if isinstance(__UpperCAmelCase				, (str, list, tuple)):
        a						:		Dict      							=			data_files
        if isinstance(__UpperCAmelCase				, __UpperCAmelCase):
          a						:		str      							=			[files]
        # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
        a						:		List[Any]      							=			[dl_manager.iter_files(__UpperCAmelCase) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={"files": files})]
      a						:		Dict      							=			[]
      for split_name, files in data_files.items():
        if isinstance(__UpperCAmelCase				, __UpperCAmelCase):
          a						:		Optional[int]      							=			[files]
        # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
        a						:		Tuple      							=			[dl_manager.iter_files(__UpperCAmelCase) for file in files]
        # Infer features is they are stoed in the arrow schema
        if self.info.features is None:
          for file in itertools.chain.from_iterable(__UpperCAmelCase):
            with open(__UpperCAmelCase				, "rb") as f:
              a						:		Tuple      							=			datasets.Features.from_arrow_schema(pq.read_schema(__UpperCAmelCase))
            break
        splits.append(datasets.SplitGenerator(name=__UpperCAmelCase				, gen_kwargs={"files": files}))
      return splits






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    pa.Table):
      if self.info.features is not None:
        # more expensive cast to support nested features with keys in a different order
        # allows str <-> int/float or str to Audio for example
        a						:		Optional[int]      							=			table_cast(__UpperCAmelCase				, self.info.features.arrow_schema)
      return pa_table






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int):
      a						:		Tuple      							=			self.info.features.arrow_schema if self.info.features is not None else None
      if self.info.features is not None and self.config.columns is not None:
        if sorted(field.name for field in schema) != sorted(self.config.columns):
          raise ValueError(
              f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'''')
      for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase)):
        with open(__UpperCAmelCase				, "rb") as f:
          a						:		Tuple      							=			pq.ParquetFile(__UpperCAmelCase)
          try:
            for batch_idx, record_batch in enumerate(
                parquet_file.iter_batches(batch_size=self.config.batch_size				, columns=self.config.columns)):
              a						:		Optional[Any]      							=			pa.Table.from_batches([record_batch])
              # Uncomment for debugging (will print the Arrow table size and elements)
              # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
              # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
              yield f'''{file_idx}_{batch_idx}''', self._cast_table(__UpperCAmelCase)
          except ValueError as e:
            logger.error(f'''Failed to read file \'{file}\' with error {type(__UpperCAmelCase)}: {e}''')
            raise






