



# 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			(       A__     ):
     UpperCamelCase__		    =				'''naver-clova-ix/donut-base-finetuned-docvqa'''
     UpperCamelCase__		    =				(
         '''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.'''
     )
     UpperCamelCase__		    =				'''document_qa'''
     UpperCamelCase__		    =				AutoProcessor
     UpperCamelCase__		    =				VisionEncoderDecoderModel

     UpperCamelCase__		    =				['''image''', '''text''']
     UpperCamelCase__		    =				['''text''']




     def __init__(				self ,						*a__ ,						**a__):
         if not is_vision_available():
             raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''')

         super().__init__(*a__ ,						**a__)




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
         A__		 =					task_prompt.replace('''{user_input}''' ,						a__)
         A__		 =					self.pre_processor.tokenizer(
             a__ ,						add_special_tokens=a__ ,						return_tensors='''pt''').input_ids
         A__		 =					self.pre_processor(a__ ,						return_tensors='''pt''').pixel_values

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




     def 		snake_case_		(				self ,						a__):
         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=a__ ,						pad_token_id=self.pre_processor.tokenizer.pad_token_id ,						eos_token_id=self.pre_processor.tokenizer.eos_token_id ,						use_cache=a__ ,						num_beams=1 ,						bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] ,						return_dict_in_generate=a__ ,						).sequences




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.pre_processor.batch_decode(a__)[0]
         A__		 =					sequence.replace(self.pre_processor.tokenizer.eos_token ,						'''''')
         A__		 =					sequence.replace(self.pre_processor.tokenizer.pad_token ,						'''''')
         A__		 =					re.sub(R'''<.*?>''' ,						'''''' ,						a__ ,						count=1).strip()  # remove first task start token
         A__		 =					self.pre_processor.tokenajson(a__)

         return sequence["answer"]





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				IFPipeline
     UpperCamelCase__		    =				TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     UpperCamelCase__		    =				TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		    =				PipelineTesterMixin.required_optional_params - {'''latents'''}




     def 		snake_case_		(				self):
         return self._get_dummy_components()




     def 		snake_case_		(				self ,						a__ ,						a__=0):
         if str(a__).startswith('''mps'''):
             A__		 =					torch.manual_seed(a__)
         else:
             A__		 =					torch.Generator(device=a__).manual_seed(a__)

         A__		 =					{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs




     def 		snake_case_		(				self):
         self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != '''cuda''' ,						reason='''float16 requires CUDA''')
     def 		snake_case_		(				self):
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1)




     def 		snake_case_		(				self):
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




     def 		snake_case_		(				self):
         self._test_save_load_local()




     def 		snake_case_		(				self):
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,						)




     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available() ,						reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,						)
     def 		snake_case_		(				self):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)







@slow
@require_torch_gpu
class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(				self):
         # if

         A__		 =					IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' ,						variant='''fp16''' ,						torch_dtype=torch.floataa)

         A__		 =					IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0''' ,						variant='''fp16''' ,						torch_dtype=torch.floataa ,						text_encoder=a__ ,						tokenizer=a__)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda''')

         A__ ,	A__		 =					pipe_a.encode_prompt('''anime turtle''' ,						device='''cuda''')

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         A__		 =					None
         A__		 =					None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         A__		 =					IFImgaImgPipeline(**pipe_a.components)
         A__		 =					IFImgaImgSuperResolutionPipeline(**pipe_a.components)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if_imgaimg(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         A__		 =					IFInpaintingPipeline(**pipe_a.components)
         A__		 =					IFInpaintingSuperResolutionPipeline(**pipe_a.components)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if_inpainting(a__ ,						a__ ,						a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_3 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(1)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(1)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)







def 		lowerCAmelCase__    (					)->  List[Any]:
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()
