


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
    A_      :				Tuple		     =					np.full((len(SCREAMING_SNAKE_CASE       ), sequence_length, 2) ,			SCREAMING_SNAKE_CASE       )
  else:
    A_      :				Optional[Any]		     =					np.full((len(SCREAMING_SNAKE_CASE       ), sequence_length) ,			SCREAMING_SNAKE_CASE       )

  for i, tensor in enumerate(SCREAMING_SNAKE_CASE       ):
    if padding_side == "right":
      if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
        A_      :				List[Any]		     =					tensor[:sequence_length]
      else:
        A_      :				Dict		     =					tensor[:sequence_length]
    else:
      if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
        A_      :				Dict		     =					tensor[:sequence_length]
      else:
        A_      :				Union[str, Any]		     =					tensor[:sequence_length]

  return out_tensor.tolist()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Optional[int]		     =					ord(SCREAMING_SNAKE_CASE       )
  if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
    return True
  A_      :				List[str]		     =					unicodedata.category(SCREAMING_SNAKE_CASE       )
  if cat.startswith('''P'''       ):
    return True
  return False







@dataclass
class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    42
       snake_case      =    True
       snake_case      =    None
       snake_case      =    None
       snake_case      =    -100
       snake_case      =    "pt"



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         import torch

         A_      :				Dict		     =					'''label''' if '''label''' in features[0].keys() else '''labels'''
         A_      :				int		     =					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
         A_      :				str		     =					self.tokenizer.pad(
             _SCREAMING_SNAKE_CASE				,       padding=self.padding				,       max_length=self.max_length				,       pad_to_multiple_of=self.pad_to_multiple_of				,       return_tensors='''pt''' if labels is None else None				,       )

         if labels is None:
           return batch

         A_      :				Dict		     =					torch.tensor(batch['''entity_ids''']		).shape[1]
         A_      :				List[Any]		     =					self.tokenizer.padding_side
         if padding_side == "right":
           A_      :				Optional[int]		     =					[
               list(_SCREAMING_SNAKE_CASE		) + [self.label_pad_token_id] * (sequence_length - len(_SCREAMING_SNAKE_CASE		)) for label in labels
           ]
         else:
           A_      :				Any		     =					[
               [self.label_pad_token_id] * (sequence_length - len(_SCREAMING_SNAKE_CASE		)) + list(_SCREAMING_SNAKE_CASE		) for label in labels
           ]

         A_      :				Optional[Any]		     =					[feature['''ner_tags'''] for feature in features]
         A_      :				List[str]		     =					padding_tensor(_SCREAMING_SNAKE_CASE				,       -1				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					[feature['''original_entity_spans'''] for feature in features]
         A_      :				Tuple		     =					padding_tensor(_SCREAMING_SNAKE_CASE				,       (-1, -1)				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					{k: torch.tensor(_SCREAMING_SNAKE_CASE				,       dtype=torch.intaa		) for k, v in batch.items()}

         return batch








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




