"""simple docstring"""






# 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}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					Union[str, Any]						      =							"""google/flan-t5-base"""
	lowerCAmelCase__    :					Dict						      =							(
	    """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."""
	)
	lowerCAmelCase__    :					str						      =							"""text_qa"""
	lowerCAmelCase__    :					int						      =							AutoTokenizer
	lowerCAmelCase__    :					List[str]						      =							AutoModelForSeqaSeqLM

	lowerCAmelCase__    :					Optional[Any]						      =							["""text""", """text"""]
	lowerCAmelCase__    :					Any						      =							["""text"""]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Dict:
							a_							:			Tuple     		=					QA_PROMPT.format(text=_SCREAMING_SNAKE_CASE       ,     question=_SCREAMING_SNAKE_CASE )
							return self.pre_processor(_SCREAMING_SNAKE_CASE       ,     return_tensors="pt" )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Any:
							a_							:			List[str]     		=					self.model.generate(**_SCREAMING_SNAKE_CASE )

							a_		,					a_							:			Optional[int]     		=					inputs["input_ids"].shape
							a_							:			Dict     		=					output_ids.shape[0]

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







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	List[Any]:
							return self.pre_processor.decode(_SCREAMING_SNAKE_CASE       ,     skip_special_tokens=_SCREAMING_SNAKE_CASE       ,     clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE )





"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




