
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_  :       Tuple					      =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_  :       Any					      =     ["""T5Tokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_  :       int					      =     ["""T5TokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_  :       Optional[Any]					      =     [
							    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """T5EncoderModel""",
							    """T5ForConditionalGeneration""",
							    """T5Model""",
							    """T5PreTrainedModel""",
							    """load_tf_weights_in_t5""",
							    """T5ForQuestionAnswering""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_  :       Optional[Any]					      =     [
							    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFT5EncoderModel""",
							    """TFT5ForConditionalGeneration""",
							    """TFT5Model""",
							    """TFT5PreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_  :       Optional[int]					      =     [
							    """FlaxT5EncoderModel""",
							    """FlaxT5ForConditionalGeneration""",
							    """FlaxT5Model""",
							    """FlaxT5PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_  :       Optional[int]					      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import torch

from transformers import AutoTokenizer

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				List[str]  ,		snake_case_       :				Dict					):
							UpperCamelCase_:							int		=  AutoTokenizer.from_pretrained(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				Optional[Any]					):

							if isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							Tuple		=  None
											for l in x:
															UpperCamelCase_:							Optional[Any]		=  self.tokenizer(
															    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)
															UpperCamelCase_:							str		=  torch.tensor([len(snake_case_					)]					)
															if d is not None:
																			for k in d.keys():
																							UpperCamelCase_:							List[Any]		=  torch.cat((d[k], t[k])  ,		0					)
															else:
																			UpperCamelCase_:							Optional[int]		=  t

											UpperCamelCase_:							Union[str, Any]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""					)					)
											UpperCamelCase_:							List[str]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""					)					)

							elif isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							List[str]		=  self.tokenizer(
											    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)

							else:
											raise Exception(
											    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
											    """ strings` for supports are supported."""					)

							return d




			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				List[str]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				List[Any]=0.70					):
							UpperCamelCase_:							int		=  []
							for idx in range(len(W_query["""input_ids"""]					)					):
											UpperCamelCase_:							List[str]		=  range(p_start.shape[1]					)

											UpperCamelCase_:							str		=  []
											for start_id in start_indexes:
															for end_id in end_indexes:
																			if start_id < end_id:
																							output.append(
																							    (
																							        start_id,
																							        end_id,
																							        p_start[idx][start_id].item(),
																							        p_end[idx][end_id].item(),
																							    )					)

											output.sort(key=lambda snake_case_					: (tup[2] * tup[3])  ,		reverse=snake_case_					)
											UpperCamelCase_:							Optional[int]		=  []
											for k in range(len(snake_case_					)					):
															if output[k][2] * output[k][3] >= thresh:
																			UpperCamelCase_,      UpperCamelCase_:							Tuple		=  output[k][0], output[k][1]
																			UpperCamelCase_:							Any		=  self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]					)
																			temp.append((decoded, output[k][2] * output[k][3])					)

											final_outputs.append(snake_case_					)

							return final_outputs






