




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


snake_case_ : str    		=       logging.get_logger(__name__)  # pylint: disable=invalid-name







class __snake_case			(			a		):







   def __init__(      self		:						Any					,						_snake_case		:						WhisperForConditionalGeneration					,						_snake_case		:						WhisperProcessor					,						_snake_case		:						AutoencoderKL					,						_snake_case		:						CLIPTextModel					,						_snake_case		:						CLIPTokenizer					,						_snake_case		:						UNetaDConditionModel					,						_snake_case		:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,						_snake_case		:						StableDiffusionSafetyChecker					,						_snake_case		:						CLIPImageProcessor					,						):

        """simple docstring"""


        super().__init__()

        if safety_checker is None:
             logger.warning(
                 F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
                 ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                 ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                 ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                 ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                 ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''')

        self.register_modules(
            speech_model=_snake_case					,						speech_processor=_snake_case					,						vae=_snake_case					,						text_encoder=_snake_case					,						tokenizer=_snake_case					,						unet=_snake_case					,						scheduler=_snake_case					,						feature_extractor=_snake_case					,						)







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						Optional[Union[str, int]] = "auto"):

        """simple docstring"""


        if slice_size == "auto":
             UpperCAmelCase_          =							self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(_snake_case)







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        self.enable_attention_slicing(_snake_case)







   @torch.no_grad()
   def __call__(      self		:						Optional[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[Any]=16000					,						_snake_case		:						int = 512					,						_snake_case		:						int = 512					,						_snake_case		:						int = 50					,						_snake_case		:						float = 7.5					,						_snake_case		:						Optional[Union[str, List[str]]] = None					,						_snake_case		:						Optional[int] = 1					,						_snake_case		:						float = 0.0					,						_snake_case		:						Optional[torch.Generator] = None					,						_snake_case		:						Optional[torch.FloatTensor] = None					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						_snake_case		:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,						_snake_case		:						int = 1					,						**_snake_case		:						str					,						):

        """simple docstring"""


        UpperCAmelCase_          =							self.speech_processor.feature_extractor(
            _snake_case					,						return_tensors='''pt'''					,						sampling_rate=_snake_case).input_features.to(self.device)
        UpperCAmelCase_          =							self.speech_model.generate(_snake_case					,						max_length=480000)

        UpperCAmelCase_          =							self.speech_processor.tokenizer.batch_decode(_snake_case					,						skip_special_tokens=_snake_case					,						normalize=_snake_case)[
            0
        ]

        if isinstance(_snake_case					,						_snake_case):
             UpperCAmelCase_          =							1
        elif isinstance(_snake_case					,						_snake_case):
             UpperCAmelCase_          =							len(_snake_case)
        else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_snake_case)}""")

        if height % 8 != 0 or width % 8 != 0:
             raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(_snake_case					,						_snake_case) or callback_steps <= 0)
        ):
             raise ValueError(
                 F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                 F""" {type(_snake_case)}.""")

        # get prompt text embeddings
        UpperCAmelCase_          =							self.tokenizer(
            _snake_case					,						padding='''max_length'''					,						max_length=self.tokenizer.model_max_length					,						return_tensors='''pt'''					,						)
        UpperCAmelCase_          =							text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             UpperCAmelCase_          =							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
             logger.warning(
                 '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                 F""" {self.tokenizer.model_max_length} tokens: {removed_text}""")
             UpperCAmelCase_          =							text_input_ids[:, : self.tokenizer.model_max_length]
        UpperCAmelCase_          =							self.text_encoder(text_input_ids.to(self.device))[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							text_embeddings.shape
        UpperCAmelCase_          =							text_embeddings.repeat(1					,						_snake_case					,						1)
        UpperCAmelCase_          =							text_embeddings.view(bs_embed * num_images_per_prompt					,						_snake_case					,						-1)

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        UpperCAmelCase_          =							guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
             UpperCAmelCase_          =							42
             if negative_prompt is None:
                  UpperCAmelCase_          =							[''''''] * batch_size
             elif type(_snake_case) is not type(_snake_case):
                  raise TypeError(
                      F"""`negative_prompt` should be the same type to `prompt`, but got {type(_snake_case)} !="""
                      F""" {type(_snake_case)}.""")
             elif isinstance(_snake_case					,						_snake_case):
                  UpperCAmelCase_          =							[negative_prompt]
             elif batch_size != len(_snake_case):
                  raise ValueError(
                      F"""`negative_prompt`: {negative_prompt} has batch size {len(_snake_case)}, but `prompt`:"""
                      F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                      ''' the batch size of `prompt`.''')
             else:
                  UpperCAmelCase_          =							negative_prompt

             UpperCAmelCase_          =							text_input_ids.shape[-1]
             UpperCAmelCase_          =							self.tokenizer(
                 _snake_case					,						padding='''max_length'''					,						max_length=_snake_case					,						truncation=_snake_case					,						return_tensors='''pt'''					,						)
             UpperCAmelCase_          =							self.text_encoder(uncond_input.input_ids.to(self.device))[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             UpperCAmelCase_          =							uncond_embeddings.shape[1]
             UpperCAmelCase_          =							uncond_embeddings.repeat(1					,						_snake_case					,						1)
             UpperCAmelCase_          =							uncond_embeddings.view(batch_size * num_images_per_prompt					,						_snake_case					,						-1)

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             UpperCAmelCase_          =							torch.cat([uncond_embeddings, text_embeddings])

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        UpperCAmelCase_          =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        UpperCAmelCase_          =							text_embeddings.dtype
        if latents is None:
             if self.device.type == "mps":
                  # randn does not exist on mps
                  UpperCAmelCase_          =							torch.randn(_snake_case					,						generator=_snake_case					,						device='''cpu'''					,						dtype=_snake_case).to(
                      self.device)
             else:
                  UpperCAmelCase_          =							torch.randn(_snake_case					,						generator=_snake_case					,						device=self.device					,						dtype=_snake_case)
        else:
             if latents.shape != latents_shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""")
             UpperCAmelCase_          =							latents.to(self.device)

        # set timesteps
        self.scheduler.set_timesteps(_snake_case)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        UpperCAmelCase_          =							self.scheduler.timesteps.to(self.device)

        # scale the initial noise by the standard deviation required by the scheduler
        UpperCAmelCase_          =							latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        UpperCAmelCase_          =							'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
        UpperCAmelCase_          =							{}
        if accepts_eta:
             UpperCAmelCase_          =							eta

        for i, t in enumerate(self.progress_bar(_snake_case)):
             # expand the latents if we are doing classifier free guidance
             UpperCAmelCase_          =							torch.cat([latents] * 2) if do_classifier_free_guidance else latents
             UpperCAmelCase_          =							self.scheduler.scale_model_input(_snake_case					,						_snake_case)

             # predict the noise residual
             UpperCAmelCase_          =							self.unet(_snake_case					,						_snake_case					,						encoder_hidden_states=_snake_case).sample

             # perform guidance
             if do_classifier_free_guidance:
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							noise_pred.chunk(2)
                  UpperCAmelCase_          =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             UpperCAmelCase_          =							self.scheduler.step(_snake_case					,						_snake_case					,						_snake_case					,						**_snake_case).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                  callback(_snake_case					,						_snake_case					,						_snake_case)

        UpperCAmelCase_          =							1 / 0.1_8_2_1_5 * latents
        UpperCAmelCase_          =							self.vae.decode(_snake_case).sample

        UpperCAmelCase_          =							(image / 2 + 0.5).clamp(0					,						1)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1).float().numpy()

        if output_type == "pil":
             UpperCAmelCase_          =							self.numpy_to_pil(_snake_case)

        if not return_dict:
             return image

        return StableDiffusionPipelineOutput(images=_snake_case					,						nsfw_content_detected=_snake_case)












import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:







    class __snake_case			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}					,						_snake_case)
        import datasets

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							depth_estimator(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ])
        self.assertEqual(
            [
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())					,						2_9.3_0_4)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())					,						2.6_6_2)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')






