import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)


def  _lowerCamelCase   (				lowerCamelCase_ :							Tuple     ,					lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     if isinstance(lowerCamelCase_     ,					torch.Tensor       ):
          return image
     elif isinstance(lowerCamelCase_     ,					PIL.Image.Image       ):
          UpperCAmelCase_							:       int       =				[image]

     if isinstance(image[0]     ,					PIL.Image.Image       ):
          UpperCAmelCase_							:       Tuple       =				[np.array(i.resize((w, h)     ,					resample=PIL_INTERPOLATION['lanczos']       )       )[None, :] for i in image]
          UpperCAmelCase_							:       int       =				np.concatenate(lowerCamelCase_     ,					axis=0       )
          UpperCAmelCase_							:       List[Any]       =				np.array(lowerCamelCase_       ).astype(np.floataa       ) / 255.0
          UpperCAmelCase_							:       Dict       =				image.transpose(0     ,					3     ,					1     ,					2       )
          UpperCAmelCase_							:       Optional[Any]       =				2.0 * image - 1.0
          UpperCAmelCase_							:       Optional[int]       =				torch.from_numpy(lowerCamelCase_       )
     elif isinstance(image[0]     ,					torch.Tensor       ):
          UpperCAmelCase_							:       str       =				torch.cat(lowerCamelCase_     ,					dim=0       )
     return image


def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							Union[str, Any]=0.9995       ):



     """simple docstring"""





     if not isinstance(lowerCamelCase_     ,					np.ndarray       ):
          UpperCAmelCase_							:       Optional[int]       =				True
          UpperCAmelCase_							:       List[Any]       =				va.device
          UpperCAmelCase_							:       Optional[int]       =				va.cpu().numpy()
          UpperCAmelCase_							:       Optional[int]       =				va.cpu().numpy()

     UpperCAmelCase_							:       Optional[int]       =				np.sum(va * va / (np.linalg.norm(lowerCamelCase_       ) * np.linalg.norm(lowerCamelCase_       ))       )
     if np.abs(lowerCamelCase_       ) > DOT_THRESHOLD:
          UpperCAmelCase_							:       int       =				(1 - t) * va + t * va
     else:
          UpperCAmelCase_							:       str       =				np.arccos(lowerCamelCase_       )
          UpperCAmelCase_							:       Tuple       =				np.sin(lowerCamelCase_       )
          UpperCAmelCase_							:       int       =				theta_a * t
          UpperCAmelCase_							:       Dict       =				np.sin(lowerCamelCase_       )
          UpperCAmelCase_							:       Optional[Any]       =				np.sin(theta_a - theta_t       ) / sin_theta_a
          UpperCAmelCase_							:       Any       =				sin_theta_t / sin_theta_a
          UpperCAmelCase_							:       Any       =				sa * va + sa * va

     if inputs_are_torch:
          UpperCAmelCase_							:       Dict       =				torch.from_numpy(lowerCamelCase_       ).to(lowerCamelCase_       )

     return va


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]     ,					lowerCamelCase_ :							Optional[int]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				F.normalize(lowerCamelCase_     ,					dim=-1       )
     UpperCAmelCase_							:       int       =				F.normalize(lowerCamelCase_     ,					dim=-1       )
     return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     for param in model.parameters():
          UpperCAmelCase_							:       Dict       =				value






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		):




       '''simple docstring'''

       super().__init__()
       self.register_modules(
           vae=snake_case_				,		text_encoder=snake_case_				,		clip_model=snake_case_				,		tokenizer=snake_case_				,		unet=snake_case_				,		scheduler=snake_case_				,		feature_extractor=snake_case_				,		coca_model=snake_case_				,		coca_tokenizer=snake_case_				,		coca_transform=snake_case_				,		)
       UpperCAmelCase_							:       List[str]       =				(
           feature_extractor.size
           if isinstance(feature_extractor.size				,		snake_case_						)
           else feature_extractor.size['shortest_edge']
       )
       UpperCAmelCase_							:       Dict       =				transforms.Normalize(mean=feature_extractor.image_mean				,		std=feature_extractor.image_std						)
       set_requires_grad(self.text_encoder				,		snake_case_						)
       set_requires_grad(self.clip_model				,		snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_ = "auto"						):




       '''simple docstring'''

       if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            UpperCAmelCase_							:       List[Any]       =				self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.enable_attention_slicing(snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       set_requires_grad(self.vae				,		snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       set_requires_grad(self.vae				,		snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       set_requires_grad(self.unet				,		snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       set_requires_grad(self.unet				,		snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				min(int(num_inference_steps * strength						)				,		snake_case_						)

       UpperCAmelCase_							:       str       =				max(num_inference_steps - init_timestep				,		0						)
       UpperCAmelCase_							:       str       =				self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=None						):




       '''simple docstring'''

       if not isinstance(snake_case_				,		torch.Tensor						):
            raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(snake_case_						)}'''						)

       UpperCAmelCase_							:       Optional[int]       =				image.to(device=snake_case_				,		dtype=snake_case_						)

       if isinstance(snake_case_				,		snake_case_						):
            UpperCAmelCase_							:       Optional[int]       =				[
                self.vae.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(snake_case_						)
            ]
            UpperCAmelCase_							:       Dict       =				torch.cat(snake_case_				,		dim=0						)
       else:
            UpperCAmelCase_							:       int       =				self.vae.encode(snake_case_						).latent_dist.sample(snake_case_						)

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       UpperCAmelCase_							:       Any       =				0.1_82_15 * init_latents
       UpperCAmelCase_							:       Optional[int]       =				init_latents.repeat_interleave(snake_case_				,		dim=0						)

       UpperCAmelCase_							:       int       =				randn_tensor(init_latents.shape				,		generator=snake_case_				,		device=snake_case_				,		dtype=snake_case_						)

       # get latents
       UpperCAmelCase_							:       Dict       =				self.scheduler.add_noise(snake_case_				,		snake_case_				,		snake_case_						)
       UpperCAmelCase_							:       List[Any]       =				init_latents

       return latents


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.coca_transform(snake_case_						).unsqueeze(0						)
       with torch.no_grad(), torch.cuda.amp.autocast():
            UpperCAmelCase_							:       Dict       =				self.coca_model.generate(transformed_image.to(device=self.device				,		dtype=self.coca_model.dtype						)						)
       UpperCAmelCase_							:       str       =				self.coca_tokenizer.decode(generated[0].cpu().numpy()						)
       return generated.split('<end_of_text>'						)[0].replace('<start_of_text>'				,		''						).rstrip(' .,'						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.feature_extractor.preprocess(snake_case_						)
       UpperCAmelCase_							:       str       =				torch.from_numpy(clip_image_input['pixel_values'][0]						).unsqueeze(0						).to(self.device						).half()
       UpperCAmelCase_							:       int       =				self.clip_model.get_image_features(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				image_embeddings_clip / image_embeddings_clip.norm(p=2				,		dim=-1				,		keepdim=snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				image_embeddings_clip.repeat_interleave(snake_case_				,		dim=0						)
       return image_embeddings_clip


  @torch.enable_grad()
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				latents.detach().requires_grad_()

       UpperCAmelCase_							:       Any       =				self.scheduler.scale_model_input(snake_case_				,		snake_case_						)

       # predict the noise residual
       UpperCAmelCase_							:       Any       =				self.unet(snake_case_				,		snake_case_				,		encoder_hidden_states=snake_case_						).sample

       if isinstance(self.scheduler				,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
            UpperCAmelCase_							:       Any       =				self.scheduler.alphas_cumprod[timestep]
            UpperCAmelCase_							:       Union[str, Any]       =				1 - alpha_prod_t
            # compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            UpperCAmelCase_							:       List[Any]       =				(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

            UpperCAmelCase_							:       List[str]       =				torch.sqrt(snake_case_						)
            UpperCAmelCase_							:       Union[str, Any]       =				pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler				,		snake_case_						):
            UpperCAmelCase_							:       Union[str, Any]       =				self.scheduler.sigmas[index]
            UpperCAmelCase_							:       Optional[int]       =				latents - sigma * noise_pred
       else:
            raise ValueError(F'''scheduler type {type(self.scheduler						)} not supported'''						)

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       UpperCAmelCase_							:       str       =				1 / 0.1_82_15 * sample
       UpperCAmelCase_							:       List[Any]       =				self.vae.decode(snake_case_						).sample
       UpperCAmelCase_							:       str       =				(image / 2 + 0.5).clamp(0				,		1						)

       UpperCAmelCase_							:       Optional[Any]       =				transforms.Resize(self.feature_extractor_size						)(snake_case_						)
       UpperCAmelCase_							:       List[Any]       =				self.normalize(snake_case_						).to(latents.dtype						)

       UpperCAmelCase_							:       Tuple       =				self.clip_model.get_image_features(snake_case_						)
       UpperCAmelCase_							:       str       =				image_embeddings_clip / image_embeddings_clip.norm(p=2				,		dim=-1				,		keepdim=snake_case_						)

       UpperCAmelCase_							:       Dict       =				spherical_dist_loss(snake_case_				,		snake_case_						).mean() * clip_guidance_scale

       UpperCAmelCase_							:       Any       =				-torch.autograd.grad(snake_case_				,		snake_case_						)[0]

       if isinstance(self.scheduler				,		snake_case_						):
            UpperCAmelCase_							:       List[Any]       =				latents.detach() + grads * (sigma**2)
            UpperCAmelCase_							:       Any       =				noise_pred_original
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				noise_pred_original - torch.sqrt(snake_case_						) * grads
       return noise_pred, latents






  @torch.no_grad()
  def __call__(			self				,		snake_case_				,		snake_case_				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = 5_1_2				,		snake_case_ = 5_1_2				,		snake_case_ = 0.6				,		snake_case_ = 5_0				,		snake_case_ = 7.5				,		snake_case_ = 1				,		snake_case_ = 0.0				,		snake_case_ = 1_0_0				,		snake_case_ = None				,		snake_case_ = "pil"				,		snake_case_ = True				,		snake_case_ = 0.8				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		):




       '''simple docstring'''

       if isinstance(snake_case_				,		snake_case_						) and len(snake_case_						) != batch_size:
            raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(snake_case_						)} generators.'''						)

       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 isinstance(snake_case_				,		torch.Generator						) and batch_size > 1:
            UpperCAmelCase_							:       List[Any]       =				[generator] + [None] * (batch_size - 1)

       UpperCAmelCase_							:       Optional[int]       =				[
           ('model', self.coca_model is None),
           ('tokenizer', self.coca_tokenizer is None),
           ('transform', self.coca_transform is None),
       ]
       UpperCAmelCase_							:       List[str]       =				[x[0] for x in coca_is_none if x[1]]
       UpperCAmelCase_							:       Optional[int]       =				', '.join(snake_case_						)
       # generate prompts with coca model if prompt is None
       if content_prompt is None:
            if len(snake_case_						):
                 raise ValueError(
                     F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                     F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''						)
            UpperCAmelCase_							:       Optional[int]       =				self.get_image_description(snake_case_						)
       if style_prompt is None:
            if len(snake_case_						):
                 raise ValueError(
                     F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                     F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''						)
            UpperCAmelCase_							:       List[Any]       =				self.get_image_description(snake_case_						)

       # get prompt text embeddings for content and style
       UpperCAmelCase_							:       Dict       =				self.tokenizer(
           snake_case_				,		padding='max_length'				,		max_length=self.tokenizer.model_max_length				,		truncation=snake_case_				,		return_tensors='pt'				,		)
       UpperCAmelCase_							:       List[str]       =				self.text_encoder(content_text_input.input_ids.to(self.device						)						)[0]

       UpperCAmelCase_							:       str       =				self.tokenizer(
           snake_case_				,		padding='max_length'				,		max_length=self.tokenizer.model_max_length				,		truncation=snake_case_				,		return_tensors='pt'				,		)
       UpperCAmelCase_							:       Optional[Any]       =				self.text_encoder(style_text_input.input_ids.to(self.device						)						)[0]

       UpperCAmelCase_							:       Dict       =				slerp(snake_case_				,		snake_case_				,		snake_case_						)

       # duplicate text embeddings for each generation per prompt
       UpperCAmelCase_							:       Any       =				text_embeddings.repeat_interleave(snake_case_				,		dim=0						)

       # set timesteps
       UpperCAmelCase_							:       Tuple       =				'offset' in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
       UpperCAmelCase_							:       Union[str, Any]       =				{}
       if accepts_offset:
            UpperCAmelCase_							:       int       =				1

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

       UpperCAmelCase_							:       Any       =				self.get_timesteps(snake_case_				,		snake_case_				,		self.device						)
       UpperCAmelCase_							:       Optional[Any]       =				timesteps[:1].repeat(snake_case_						)

       # Preprocess image
       UpperCAmelCase_							:       Optional[int]       =				preprocess(snake_case_				,		snake_case_				,		snake_case_						)
       UpperCAmelCase_							:       Tuple       =				self.prepare_latents(
           snake_case_				,		snake_case_				,		snake_case_				,		text_embeddings.dtype				,		self.device				,		snake_case_						)

       UpperCAmelCase_							:       Any       =				preprocess(snake_case_				,		snake_case_				,		snake_case_						)
       UpperCAmelCase_							:       List[Any]       =				self.prepare_latents(
           snake_case_				,		snake_case_				,		snake_case_				,		text_embeddings.dtype				,		self.device				,		snake_case_						)

       UpperCAmelCase_							:       List[Any]       =				slerp(snake_case_				,		snake_case_				,		snake_case_						)

       if clip_guidance_scale > 0:
            UpperCAmelCase_							:       Optional[Any]       =				self.get_clip_image_embeddings(snake_case_				,		snake_case_						)
            UpperCAmelCase_							:       Any       =				self.get_clip_image_embeddings(snake_case_				,		snake_case_						)
            UpperCAmelCase_							:       int       =				slerp(
                snake_case_				,		snake_case_				,		snake_case_						)

       # 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_							:       Tuple       =				guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
            UpperCAmelCase_							:       int       =				content_text_input.input_ids.shape[-1]
            UpperCAmelCase_							:       Dict       =				self.tokenizer(['']				,		padding='max_length'				,		max_length=snake_case_				,		return_tensors='pt'						)
            UpperCAmelCase_							:       Union[str, Any]       =				self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
            # duplicate unconditional embeddings for each generation per prompt
            UpperCAmelCase_							:       Any       =				uncond_embeddings.repeat_interleave(snake_case_				,		dim=0						)

            # 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_							:       List[str]       =				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_							:       int       =				(batch_size, self.unet.config.in_channels, height // 8, width // 8)
       UpperCAmelCase_							:       List[str]       =				text_embeddings.dtype
       if latents is None:
            if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 UpperCAmelCase_							:       List[str]       =				torch.randn(snake_case_				,		generator=snake_case_				,		device='cpu'				,		dtype=snake_case_						).to(
                     self.device						)
            else:
                 UpperCAmelCase_							:       List[Any]       =				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_							:       Dict       =				latents.to(self.device						)

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase_							:       Dict       =				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_							:       List[str]       =				'eta' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
       UpperCAmelCase_							:       Tuple       =				{}
       if accepts_eta:
            UpperCAmelCase_							:       Tuple       =				eta

       # check if the scheduler accepts generator
       UpperCAmelCase_							:       List[str]       =				'generator' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
       if accepts_generator:
            UpperCAmelCase_							:       List[str]       =				generator

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

                 # predict the noise residual
                 UpperCAmelCase_							:       Any       =				self.unet(snake_case_				,		snake_case_				,		encoder_hidden_states=snake_case_						).sample

                 # perform classifier free guidance
                 if do_classifier_free_guidance:
                      UpperCAmelCase_							:       str       =				noise_pred.chunk(2						)
                      UpperCAmelCase_							:       Optional[int]       =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # perform clip guidance
                 if clip_guidance_scale > 0:
                      UpperCAmelCase_							:       Union[str, Any]       =				(
                          text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
                      )
                      UpperCAmelCase_							:       Union[str, Any]       =				self.cond_fn(
                          snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		)

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       UpperCAmelCase_							:       Optional[int]       =				1 / 0.1_82_15 * latents
       UpperCAmelCase_							:       Optional[int]       =				self.vae.decode(snake_case_						).sample

       UpperCAmelCase_							:       Tuple       =				(image / 2 + 0.5).clamp(0				,		1						)
       UpperCAmelCase_							:       Tuple       =				image.cpu().permute(0				,		2				,		3				,		1						).numpy()

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

       if not return_dict:
            return (image, None)

       return StableDiffusionPipelineOutput(images=snake_case_				,		nsfw_content_detected=snake_case_						)







'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





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






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				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_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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






