



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


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

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

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: List[Any]             =   """google/flan-t5-base"""
							UpperCamelCase__			: Optional[Any]             =   (
							    """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."""
							)
							UpperCamelCase__			: Any             =   """text_qa"""
							UpperCamelCase__			: str             =   AutoTokenizer
							UpperCamelCase__			: Union[str, Any]             =   AutoModelForSeqaSeqLM

							UpperCamelCase__			: Union[str, Any]             =   ["""text""", """text"""]
							UpperCamelCase__			: int             =   ["""text"""]


							def _a (							self					,					a_					,					a_						):
											a__			 =						QA_PROMPT.format(text=a_					,					question=a_						)
											return self.pre_processor(a_					,					return_tensors="""pt"""						)


							def _a (							self					,					a_						):
											a__			 =						self.model.generate(**a_						)

											a__ ,     a__			 =						inputs["""input_ids"""].shape
											a__			 =						output_ids.shape[0]

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







							def _a (							self					,					a_						):
											return self.pre_processor.decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

