






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 				__UpperCAmelCase		(unittest.TestCase   ):







			@property
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  UNetaDModel(
									    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=3				,     out_channels=3				,     down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")				,     up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")				,     )
									return model







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.dummy_uncond_unet
									_SCREAMING_SNAKE_CASE     				=  KarrasVeScheduler()

									_SCREAMING_SNAKE_CASE     				=  KarrasVePipeline(unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_					)
									pipe.to(UpperCAmelCase_					)
									pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(num_inference_steps=2				,     generator=UpperCAmelCase_				,     output_type="""numpy"""					).images

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(num_inference_steps=2				,     generator=UpperCAmelCase_				,     output_type="""numpy"""				,     return_dict=UpperCAmelCase_					)[0]

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2



@slow
@require_torch
class 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """google/ncsnpp-celebahq-256"""
									_SCREAMING_SNAKE_CASE     				=  UNetaDModel.from_pretrained(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  KarrasVeScheduler()

									_SCREAMING_SNAKE_CASE     				=  KarrasVePipeline(unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_					)
									pipe.to(UpperCAmelCase_					)
									pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(num_inference_steps=20				,     generator=UpperCAmelCase_				,     output_type="""numpy"""					).images

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 256, 256, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2









# 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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
		from PIL import Image



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict									=							"naver-clova-ix/donut-base-finetuned-docvqa"
			__snake_case    :				Dict									=							(
			    "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
			    "should be the document containing the information, as well as a `question` that is the question about the "
			    "document. It returns a text that contains the answer to the question."
			)
			__snake_case    :				Dict									=							"document_qa"
			__snake_case    :				Any									=							AutoProcessor
			__snake_case    :				int									=							VisionEncoderDecoderModel

			__snake_case    :				Union[str, Any]									=							["image", "text"]
			__snake_case    :				Optional[int]									=							["text"]







			def __init__(     self:  List[Any]				,     *UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									if not is_vision_available():
															raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""					)

									super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  "Image"				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
									_SCREAMING_SNAKE_CASE     				=  task_prompt.replace("""{user_input}"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenizer(
									    UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_tensors="""pt"""					).input_ids
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values

									return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									return self.model.generate(
									    inputs["""pixel_values"""].to(self.device					)				,     decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device					)				,     max_length=self.model.decoder.config.max_position_embeddings				,     early_stopping=UpperCAmelCase_				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=UpperCAmelCase_				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=UpperCAmelCase_				,     ).sequences







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.batch_decode(UpperCAmelCase_					)[0]
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  re.sub(R"""<.*?>"""				,     """"""				,     UpperCAmelCase_				,     count=1					).strip()  # remove first task start token
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenajson(UpperCAmelCase_					)

									return sequence["answer"]

