"""simple docstring"""





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Optional[int]   =  ["vqvae"]


      def __init__(   self		,			a		,			a		,			a		,			a		,			)       ->					Optional[int]:
             super().__init__()
             self.register_modules(unet=a		,			scheduler=a		,			mel=a		,			vqvae=a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             return 5_0 if isinstance(self.scheduler		,			a							) else 1_0_0_0


      @torch.no_grad()
      def __call__(   self		,			a = 1		,			a = None		,			a = None		,			a = 0		,			a = 0		,			a = None		,			a = None		,			a = 0		,			a = 0		,			a = None		,			a = 0		,			a = None		,			a = None		,			a=True		,			)       ->					Union[
          Union[AudioPipelineOutput, ImagePipelineOutput],
          Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
      ]:
             lowercase__			:		Optional[Any]       =	steps or self.get_default_steps()
             self.scheduler.set_timesteps(a							)
             lowercase__			:		Dict       =	step_generator or generator
             # For backwards compatibility
             if type(self.unet.config.sample_size							) == int:
                    lowercase__			:		str       =	(self.unet.config.sample_size, self.unet.config.sample_size)
             if noise is None:
                    lowercase__			:		Union[str, Any]       =	randn_tensor(
                        (
                            batch_size,
                            self.unet.config.in_channels,
                            self.unet.config.sample_size[0],
                            self.unet.config.sample_size[1],
                        )		,			generator=a		,			device=self.device		,			)
             lowercase__			:		Dict       =	noise
             lowercase__			:		str       =	None

             if audio_file is not None or raw_audio is not None:
                    self.mel.load_audio(a		,			a							)
                    lowercase__			:		str       =	self.mel.audio_slice_to_image(a							)
                    lowercase__			:		Tuple       =	np.frombuffer(input_image.tobytes()		,			dtype='uint8'							).reshape(
                        (input_image.height, input_image.width)							)
                    lowercase__			:		List[str]       =	(input_image / 2_5_5) * 2 - 1
                    lowercase__			:		Optional[Any]       =	torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float							).to(self.device							)

                    if self.vqvae is not None:
                           lowercase__			:		List[str]       =	self.vqvae.encode(torch.unsqueeze(a		,			0							)							).latent_dist.sample(
                               generator=a							)[0]
                           lowercase__			:		Tuple       =	self.vqvae.config.scaling_factor * input_images

                    if start_step > 0:
                           lowercase__			:		Union[str, Any]       =	self.scheduler.add_noise(a		,			a		,			self.scheduler.timesteps[start_step - 1]							)

                    lowercase__			:		Any       =	(
                        self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                    )
                    lowercase__			:		Optional[int]       =	int(mask_start_secs * pixels_per_second							)
                    lowercase__			:		Optional[int]       =	int(mask_end_secs * pixels_per_second							)
                    lowercase__			:		Optional[Any]       =	self.scheduler.add_noise(a		,			a		,			torch.tensor(self.scheduler.timesteps[start_step:]							)							)

             for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
                    if isinstance(self.unet		,			a							):
                           lowercase__			:		Any       =	self.unet(a		,			a		,			a							)['sample']
                    else:
                           lowercase__			:		Optional[int]       =	self.unet(a		,			a							)['sample']

                    if isinstance(self.scheduler		,			a							):
                           lowercase__			:		Union[str, Any]       =	self.scheduler.step(
                               model_output=a		,			timestep=a		,			sample=a		,			eta=a		,			generator=a		,			)['prev_sample']
                    else:
                           lowercase__			:		int       =	self.scheduler.step(
                               model_output=a		,			timestep=a		,			sample=a		,			generator=a		,			)['prev_sample']

                    if mask is not None:
                           if mask_start > 0:
                                  lowercase__			:		int       =	mask[:, step, :, :mask_start]
                           if mask_end > 0:
                                  lowercase__			:		Optional[int]       =	mask[:, step, :, -mask_end:]

             if self.vqvae is not None:
                    # 0.18215 was scaling factor used in training to ensure unit variance
                    lowercase__			:		Optional[Any]       =	1 / self.vqvae.config.scaling_factor * images
                    lowercase__			:		Dict       =	self.vqvae.decode(a							)['sample']

             lowercase__			:		Any       =	(images / 2 + 0.5).clamp(0		,			1							)
             lowercase__			:		List[str]       =	images.cpu().permute(0		,			2		,			3		,			1							).numpy()
             lowercase__			:		Optional[int]       =	(images * 2_5_5).round().astype('uint8'							)
             lowercase__			:		List[str]       =	list(
                 (Image.fromarray(_[:, :, 0]							) for _ in images)
                 if images.shape[3] == 1
                 else (Image.fromarray(a		,			mode='RGB'							).convert('L'							) for _ in images)							)

             lowercase__			:		int       =	[self.mel.image_to_audio(a							) for _ in images]
             if not return_dict:
                    return images, (self.mel.get_sample_rate(), audios)

             return BaseOutput(**AudioPipelineOutput(np.array(a							)[:, np.newaxis, :]							)		,			**ImagePipelineOutput(a							)							)


      @torch.no_grad()
      def 		_UpperCAmelCase       (   self		,			a		,			a = 5_0							)       ->					np.ndarray:
             assert isinstance(self.scheduler		,			a							)
             self.scheduler.set_timesteps(a							)
             lowercase__			:		Optional[Any]       =	np.array(
                 [np.frombuffer(image.tobytes()		,			dtype='uint8'							).reshape((1, image.height, image.width)							) for image in images]							)
             lowercase__			:		Any       =	(sample / 2_5_5) * 2 - 1
             lowercase__			:		Optional[int]       =	torch.Tensor(a							).to(self.device							)

             for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,			(0,)							)							):
                    lowercase__			:		Any       =	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                    lowercase__			:		List[Any]       =	self.scheduler.alphas_cumprod[t]
                    lowercase__			:		Tuple       =	(
                        self.scheduler.alphas_cumprod[prev_timestep]
                        if prev_timestep >= 0
                        else self.scheduler.final_alpha_cumprod
                    )
                    lowercase__			:		int       =	1 - alpha_prod_t
                    lowercase__			:		int       =	self.unet(a		,			a							)['sample']
                    lowercase__			:		Optional[int]       =	(1 - alpha_prod_t_prev) ** 0.5 * model_output
                    lowercase__			:		Optional[Any]       =	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                    lowercase__			:		Union[str, Any]       =	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

             return sample


      @staticmethod
      def 		_UpperCAmelCase       (   a		,			a		,			a							)       ->					torch.Tensor:
             lowercase__			:		List[Any]       =	acos(torch.dot(torch.flatten(a							)		,			torch.flatten(a							)							) / torch.norm(a							) / torch.norm(a							)							)
             return sin((1 - alpha) * theta							) * xa / sin(a							) + sin(alpha * theta							) * xa / sin(a							)


"""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.
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							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[Any]							=		"naver-clova-ix/donut-base-finetuned-docvqa"
     _UpperCamelCase							:			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."
     )
     _UpperCamelCase							:			Optional[int]							=		"document_qa"
     _UpperCamelCase							:			Any							=		AutoProcessor
     _UpperCamelCase							:			Union[str, Any]							=		VisionEncoderDecoderModel

     _UpperCamelCase							:			Union[str, Any]							=		["image", "text"]
     _UpperCamelCase							:			List[str]							=		["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     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
       _lowerCAmelCase						:       Dict  			=     task_prompt.replace("""{user_input}"""				,     a__  )
       _lowerCAmelCase						:       str  			=     self.pre_processor.tokenizer(
           a__				,     add_special_tokens=a__				,     return_tensors="""pt"""  ).input_ids
       _lowerCAmelCase						:       Dict  			=     self.pre_processor(a__				,     return_tensors="""pt"""  ).pixel_values

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






     def     __A  (		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     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     self.pre_processor.batch_decode(a__  )[0]
       _lowerCAmelCase						:       int  			=     sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     re.sub(r"""<.*?>"""				,     """"""				,     a__				,     count=1  ).strip()  # remove first task start token
       _lowerCAmelCase						:       List[str]  			=     self.pre_processor.tokenajson(a__  )

       return sequence["answer"]




