

from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowerCAmelCase__             =		"""Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		a__       							=	'''google/flan-t5-base'''
		a__       							=	(
		    '''This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '''
		    '''text where to find the answer, and `question`, which is the question, and returns the answer to the question.'''
		)
		a__       							=	'''text_qa'''
		a__       							=	AutoTokenizer
		a__       							=	AutoModelForSeqaSeqLM

		a__       							=	['''text''', '''text''']
		a__       							=	['''text''']


		def 		A__       (					self						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    QA_PROMPT.format(text=lowerCamelCase__						,							question=lowerCamelCase__)
			return self.pre_processor(lowerCamelCase__						,							return_tensors='pt')


		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		Tuple								=    self.model.generate(**lowerCamelCase__)

			_UpperCamelCase :		Dict								=    inputs["input_ids"].shape
			_UpperCamelCase :		List[Any]								=    output_ids.shape[0]

			return output_ids.reshape(lowerCamelCase__						,							out_b // in_b						,							*output_ids.shape[1:])[0][0]






		def 		A__       (					self						,							__snake_case):
			return self.pre_processor.decode(lowerCamelCase__						,							skip_special_tokens=lowerCamelCase__						,							clean_up_tokenization_spaces=lowerCamelCase__)








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaubertForMultipleChoice""",
					    """FlaubertForQuestionAnswering""",
					    """FlaubertForQuestionAnsweringSimple""",
					    """FlaubertForSequenceClassification""",
					    """FlaubertForTokenClassification""",
					    """FlaubertModel""",
					    """FlaubertWithLMHeadModel""",
					    """FlaubertPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFFlaubertForMultipleChoice""",
					    """TFFlaubertForQuestionAnsweringSimple""",
					    """TFFlaubertForSequenceClassification""",
					    """TFFlaubertForTokenClassification""",
					    """TFFlaubertModel""",
					    """TFFlaubertPreTrainedModel""",
					    """TFFlaubertWithLMHeadModel""",
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





