


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						ShapEPipeline
						UpperCAmelCase             =						["prompt"]
						UpperCAmelCase             =						["prompt"]
						UpperCAmelCase             =						[
						    "num_images_per_prompt",
						    "num_inference_steps",
						    "generator",
						    "latents",
						    "guidance_scale",
						    "frame_size",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase             =						False




						@property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     str:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Union[str, Any]:


										"""simple docstring"""





										return self.time_input_dim * 4




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Tuple:


										"""simple docstring"""





										return 8




						@property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
										return tokenizer




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Union[str, Any]:


										"""simple docstring"""





										torch.manual_seed(0  )
										_SCREAMING_SNAKE_CASE			  =CLIPTextConfig(
										    bos_token_id=0				,    eos_token_id=2				,    hidden_size=self.text_embedder_hidden_size				,    projection_dim=self.text_embedder_hidden_size				,    intermediate_size=37				,    layer_norm_eps=1E-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1000				,    )
										return CLIPTextModelWithProjection(_a  )




						@property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     List[str]:


										"""simple docstring"""





										torch.manual_seed(0  )

										_SCREAMING_SNAKE_CASE			  ={
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 16,
										    '''embedding_dim''': self.time_input_dim,
										    '''num_embeddings''': 32,
										    '''embedding_proj_dim''': self.text_embedder_hidden_size,
										    '''time_embed_dim''': self.time_embed_dim,
										    '''num_layers''': 1,
										    '''clip_embed_dim''': self.time_input_dim * 2,
										    '''additional_embeddings''': 0,
										    '''time_embed_act_fn''': '''gelu''',
										    '''norm_in_type''': '''layer''',
										    '''encoder_hid_proj_type''': None,
										    '''added_emb_type''': None,
										}

										_SCREAMING_SNAKE_CASE			  =PriorTransformer(**_a  )
										return model




						@property
						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     Optional[int]:


										"""simple docstring"""





										torch.manual_seed(0  )

										_SCREAMING_SNAKE_CASE			  ={
										    '''param_shapes''': (
										        (self.renderer_dim, 93),
										        (self.renderer_dim, 8),
										        (self.renderer_dim, 8),
										        (self.renderer_dim, 8),
										    ),
										    '''d_latent''': self.time_input_dim,
										    '''d_hidden''': self.renderer_dim,
										    '''n_output''': 12,
										    '''background''': (
										        0.1,
										        0.1,
										        0.1,
										    ),
										}
										_SCREAMING_SNAKE_CASE			  =ShapERenderer(**_a  )
										return model




						def 							__UpperCamelCase				(       self					:      Any  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.dummy_prior
										_SCREAMING_SNAKE_CASE			  =self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE			  =self.dummy_tokenizer
										_SCREAMING_SNAKE_CASE			  =self.dummy_renderer

										_SCREAMING_SNAKE_CASE			  =HeunDiscreteScheduler(
										    beta_schedule='''exp'''				,    num_train_timesteps=1024				,    prediction_type='''sample'''				,    use_karras_sigmas=_a				,    clip_sample=_a				,    clip_sample_range=1.0				,    )
										_SCREAMING_SNAKE_CASE			  ={
										    '''prior''': prior,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''renderer''': renderer,
										    '''scheduler''': scheduler,
										}

										return components




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      List[Any]				,    _a					:      Optional[int]=0  )							->     Optional[Any]:


										"""simple docstring"""





										if str(_a  ).startswith('''mps'''  ):
														_SCREAMING_SNAKE_CASE			  =torch.manual_seed(_a  )
										else:
														_SCREAMING_SNAKE_CASE			  =torch.Generator(device=_a  ).manual_seed(_a  )
										_SCREAMING_SNAKE_CASE			  ={
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''num_inference_steps''': 1,
										    '''frame_size''': 32,
										    '''output_type''': '''np''',
										}
										return inputs




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''cpu'''

										_SCREAMING_SNAKE_CASE			  =self.get_dummy_components()

										_SCREAMING_SNAKE_CASE			  =self.pipeline_class(**_a  )
										_SCREAMING_SNAKE_CASE			  =pipe.to(_a  )

										pipe.set_progress_bar_config(disable=_a  )

										_SCREAMING_SNAKE_CASE			  =pipe(**self.get_dummy_inputs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =output.images[0]
										_SCREAMING_SNAKE_CASE			  =image[0, -3:, -3:, -1]

										assert image.shape == (20, 32, 32, 3)

										_SCREAMING_SNAKE_CASE			  =np.array(
										    [
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										    ]  )

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Dict:


										"""simple docstring"""





										self._test_inference_batch_consistent(batch_sizes=[1, 2]  )




						def 							__UpperCamelCase				(       self					:      str  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE			  =True

										self._test_inference_batch_single_identical(
										    batch_size=2				,    test_max_difference=_a				,    relax_max_difference=_a				,    )




						def 							__UpperCamelCase				(       self					:      str  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.get_dummy_components()
										_SCREAMING_SNAKE_CASE			  =self.pipeline_class(**_a  )
										_SCREAMING_SNAKE_CASE			  =pipe.to(_a  )
										pipe.set_progress_bar_config(disable=_a  )

										_SCREAMING_SNAKE_CASE			  =1
										_SCREAMING_SNAKE_CASE			  =2

										_SCREAMING_SNAKE_CASE			  =self.get_dummy_inputs(_a  )

										for key in inputs.keys():
														if key in self.batch_params:
																		_SCREAMING_SNAKE_CASE			  =batch_size * [inputs[key]]

										_SCREAMING_SNAKE_CASE			  =pipe(**_a				,    num_images_per_prompt=_a  )[0]

										assert images.shape[0] == batch_size * num_images_per_prompt

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




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     List[str]:


										"""simple docstring"""





										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/shap_e/test_shap_e_np_out.npy'''  )
										_SCREAMING_SNAKE_CASE			  =ShapEPipeline.from_pretrained('''openai/shap-e'''  )
										_SCREAMING_SNAKE_CASE			  =pipe.to(_a  )
										pipe.set_progress_bar_config(disable=_a  )

										_SCREAMING_SNAKE_CASE			  =torch.Generator(device=_a  ).manual_seed(0  )

										_SCREAMING_SNAKE_CASE			  =pipe(
										    '''a shark'''				,    generator=_a				,    guidance_scale=15.0				,    num_inference_steps=64				,    frame_size=64				,    output_type='''np'''				,    ).images[0]

										assert images.shape == (20, 64, 64, 3)

										assert_mean_pixel_difference(_a				,    _a  )



# 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 						A__							(		UpperCamelCase__  ):
						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					:      Any				,    *_a					:      int				,    **_a					:      Dict  )							->     int:


										"""simple docstring"""





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

										super().__init__(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      "Image"				,    _a					:      str  )							->     Tuple:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Optional[Any]  )							->     int:


										"""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=_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 							__UpperCamelCase				(       self					:      Any				,    _a					:      int  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.pre_processor.batch_decode(_a  )[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'''<.*?>'''				,    ''''''				,    _a				,    count=1  ).strip()  # remove first task start token
										_SCREAMING_SNAKE_CASE			  =self.pre_processor.tokenajson(_a  )

										return sequence["answer"]