


'''simple docstring'''






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			:				Any			,       __lowerCamelCase			:				Dict			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   if isinstance(__lowerCamelCase			,       torch.Tensor				):
      return image
   elif isinstance(__lowerCamelCase			,       PIL.Image.Image				):
      _UpperCAmelCase  :  List[Any]				       =[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  :  Optional[Any]				       =np.concatenate(__lowerCamelCase			,       axis=0				)
      _UpperCAmelCase  :  Optional[Any]				       =np.array(__lowerCamelCase				).astype(np.floataa				) / 2_55.0
      _UpperCAmelCase  :  int				       =image.transpose(0			,       3			,       1			,       2				)
      _UpperCAmelCase  :  int				       =2.0 * image - 1.0
      _UpperCAmelCase  :  List[str]				       =torch.from_numpy(__lowerCamelCase				)
   elif isinstance(image[0]			,       torch.Tensor				):
      _UpperCAmelCase  :  Optional[Any]				       =torch.cat(__lowerCamelCase			,       dim=0				)
   return image





def lowerCamelCase__     (		__lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				int			,       __lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				Optional[int]=0.99_95				):







   '''simple docstring'''




   if not isinstance(__lowerCamelCase			,       np.ndarray				):
      _UpperCAmelCase  :  Union[str, Any]				       =True
      _UpperCAmelCase  :  int				       =va.device
      _UpperCAmelCase  :  List[Any]				       =va.cpu().numpy()
      _UpperCAmelCase  :  List[str]				       =va.cpu().numpy()

   _UpperCAmelCase  :  List[Any]				       =np.sum(va * va / (np.linalg.norm(__lowerCamelCase				) * np.linalg.norm(__lowerCamelCase				))				)
   if np.abs(__lowerCamelCase				) > DOT_THRESHOLD:
      _UpperCAmelCase  :  Union[str, Any]				       =(1 - t) * va + t * va
   else:
      _UpperCAmelCase  :  List[Any]				       =np.arccos(__lowerCamelCase				)
      _UpperCAmelCase  :  Union[str, Any]				       =np.sin(__lowerCamelCase				)
      _UpperCAmelCase  :  Optional[int]				       =theta_a * t
      _UpperCAmelCase  :  Union[str, Any]				       =np.sin(__lowerCamelCase				)
      _UpperCAmelCase  :  List[str]				       =np.sin(theta_a - theta_t				) / sin_theta_a
      _UpperCAmelCase  :  int				       =sin_theta_t / sin_theta_a
      _UpperCAmelCase  :  int				       =sa * va + sa * va

   if inputs_are_torch:
      _UpperCAmelCase  :  Optional[Any]				       =torch.from_numpy(__lowerCamelCase				).to(__lowerCamelCase				)

   return va





def lowerCamelCase__     (		__lowerCamelCase			:				Dict			,       __lowerCamelCase			:				Tuple				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =F.normalize(__lowerCamelCase			,       dim=-1				)
   _UpperCAmelCase  :  str				       =F.normalize(__lowerCamelCase			,       dim=-1				)
   return (x - y).norm(dim=-1				).div(2				).arcsin().pow(2				).mul(2				)





def lowerCamelCase__     (		__lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				Tuple				):







   '''simple docstring'''




   for param in model.parameters():
      _UpperCAmelCase  :  str				       =value






class    __magic_name__				(						lowerCAmelCase							):






       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    ,				)		->    Optional[int]:




          '''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  :  Dict				       =(
              feature_extractor.size
              if isinstance(feature_extractor.size    ,				snake_case)
              else feature_extractor.size['shortest_edge']
          )
          _UpperCAmelCase  :  Tuple				       =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      lowerCAmelCase						(     self    ,				snake_case = "auto")		->    List[str]:




          '''simple docstring'''
          if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             _UpperCAmelCase  :  Union[str, Any]				       =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)






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          set_requires_grad(self.vae    ,				snake_case)






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




          '''simple docstring'''
          set_requires_grad(self.vae    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          set_requires_grad(self.unet    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          set_requires_grad(self.unet    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    int:




          '''simple docstring'''
          # get the original timestep using init_timestep
          _UpperCAmelCase  :  str				       =min(int(num_inference_steps * strength)    ,				snake_case)

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

          return timesteps, num_inference_steps - t_start






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case=None)		->    Union[str, Any]:




          '''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  :  int				       =image.to(device=snake_case    ,				dtype=snake_case)

          if isinstance(snake_case    ,				snake_case):
             _UpperCAmelCase  :  Dict				       =[
                 self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(snake_case)
             ]
             _UpperCAmelCase  :  int				       =torch.cat(snake_case    ,				dim=0)
          else:
             _UpperCAmelCase  :  Union[str, Any]				       =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  :  Optional[Any]				       =0.1_82_15 * init_latents
          _UpperCAmelCase  :  Tuple				       =init_latents.repeat_interleave(snake_case    ,				dim=0)

          _UpperCAmelCase  :  Any				       =randn_tensor(init_latents.shape    ,				generator=snake_case    ,				device=snake_case    ,				dtype=snake_case)

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

          return latents






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =self.coca_transform(snake_case).unsqueeze(0)
          with torch.no_grad(), torch.cuda.amp.autocast():
             _UpperCAmelCase  :  List[str]				       =self.coca_model.generate(transformed_image.to(device=self.device    ,				dtype=self.coca_model.dtype))
          _UpperCAmelCase  :  Union[str, Any]				       =self.coca_tokenizer.decode(generated[0].cpu().numpy())
          return generated.split('<end_of_text>')[0].replace('<start_of_text>'    ,				'').rstrip(' .,')






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    Union[str, Any]:




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






       @torch.enable_grad()
       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =latents.detach().requires_grad_()

          _UpperCAmelCase  :  str				       =self.scheduler.scale_model_input(snake_case    ,				snake_case)

          # predict the noise residual
          _UpperCAmelCase  :  str				       =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  :  Dict				       =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  :  Any				       =(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

             _UpperCAmelCase  :  List[str]				       =torch.sqrt(snake_case)
             _UpperCAmelCase  :  List[Any]				       =pred_original_sample * (fac) + latents * (1 - fac)
          elif isinstance(self.scheduler    ,				snake_case):
             _UpperCAmelCase  :  Union[str, Any]				       =self.scheduler.sigmas[index]
             _UpperCAmelCase  :  List[Any]				       =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[str]				       =self.vae.decode(snake_case).sample
          _UpperCAmelCase  :  Tuple				       =(image / 2 + 0.5).clamp(0    ,				1)

          _UpperCAmelCase  :  Dict				       =transforms.Resize(self.feature_extractor_size)(snake_case)
          _UpperCAmelCase  :  int				       =self.normalize(snake_case).to(latents.dtype)

          _UpperCAmelCase  :  Any				       =self.clip_model.get_image_features(snake_case)
          _UpperCAmelCase  :  int				       =image_embeddings_clip / image_embeddings_clip.norm(p=2    ,				dim=-1    ,				keepdim=snake_case)

          _UpperCAmelCase  :  int				       =spherical_dist_loss(snake_case    ,				snake_case).mean() * clip_guidance_scale

          _UpperCAmelCase  :  str				       =-torch.autograd.grad(snake_case    ,				snake_case)[0]

          if isinstance(self.scheduler    ,				snake_case):
             _UpperCAmelCase  :  Tuple				       =latents.detach() + grads * (sigma**2)
             _UpperCAmelCase  :  Union[str, Any]				       =noise_pred_original
          else:
             _UpperCAmelCase  :  int				       =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    ,				)		->    Optional[Any]:




          '''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  :  Dict				       =[generator] + [None] * (batch_size - 1)

          _UpperCAmelCase  :  Tuple				       =[
              ('model', self.coca_model is None),
              ('tokenizer', self.coca_tokenizer is None),
              ('transform', self.coca_transform is None),
          ]
          _UpperCAmelCase  :  List[Any]				       =[x[0] for x in coca_is_none if x[1]]
          _UpperCAmelCase  :  Tuple				       =', '.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  :  Dict				       =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  :  Union[str, Any]				       =self.get_image_description(snake_case)

          # get prompt text embeddings for content and style
          _UpperCAmelCase  :  Union[str, Any]				       =self.tokenizer(
              snake_case    ,				padding='max_length'    ,				max_length=self.tokenizer.model_max_length    ,				truncation=snake_case    ,				return_tensors='pt'    ,				)
          _UpperCAmelCase  :  Optional[int]				       =self.text_encoder(content_text_input.input_ids.to(self.device))[0]

          _UpperCAmelCase  :  List[Any]				       =self.tokenizer(
              snake_case    ,				padding='max_length'    ,				max_length=self.tokenizer.model_max_length    ,				truncation=snake_case    ,				return_tensors='pt'    ,				)
          _UpperCAmelCase  :  List[Any]				       =self.text_encoder(style_text_input.input_ids.to(self.device))[0]

          _UpperCAmelCase  :  Any				       =slerp(snake_case    ,				snake_case    ,				snake_case)

          # duplicate text embeddings for each generation per prompt
          _UpperCAmelCase  :  int				       =text_embeddings.repeat_interleave(snake_case    ,				dim=0)

          # set timesteps
          _UpperCAmelCase  :  Union[str, Any]				       ='offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
          _UpperCAmelCase  :  Optional[Any]				       ={}
          if accepts_offset:
             _UpperCAmelCase  :  Union[str, Any]				       =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       ,			_UpperCAmelCase  :  List[Any]				       =self.get_timesteps(snake_case    ,				snake_case    ,				self.device)
          _UpperCAmelCase  :  int				       =timesteps[:1].repeat(snake_case)

          # Preprocess image
          _UpperCAmelCase  :  Dict				       =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  :  Optional[Any]				       =preprocess(snake_case    ,				snake_case    ,				snake_case)
          _UpperCAmelCase  :  int				       =self.prepare_latents(
              snake_case    ,				snake_case    ,				snake_case    ,				text_embeddings.dtype    ,				self.device    ,				snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       =slerp(snake_case    ,				snake_case    ,				snake_case)

          if clip_guidance_scale > 0:
             _UpperCAmelCase  :  Tuple				       =self.get_clip_image_embeddings(snake_case    ,				snake_case)
             _UpperCAmelCase  :  Optional[int]				       =self.get_clip_image_embeddings(snake_case    ,				snake_case)
             _UpperCAmelCase  :  Optional[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  :  Union[str, Any]				       =guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
             _UpperCAmelCase  :  Dict				       =content_text_input.input_ids.shape[-1]
             _UpperCAmelCase  :  Optional[int]				       =self.tokenizer(['']    ,				padding='max_length'    ,				max_length=snake_case    ,				return_tensors='pt')
             _UpperCAmelCase  :  str				       =self.text_encoder(uncond_input.input_ids.to(self.device))[0]
             # duplicate unconditional embeddings for each generation per prompt
             _UpperCAmelCase  :  str				       =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[Any]				       =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  :  List[str]				       =(batch_size, self.unet.config.in_channels, height // 8, width // 8)
          _UpperCAmelCase  :  Dict				       =text_embeddings.dtype
          if latents is None:
             if self.device.type == "mps":
                # randn does not work reproducibly on mps
                _UpperCAmelCase  :  int				       =torch.randn(snake_case    ,				generator=snake_case    ,				device='cpu'    ,				dtype=snake_case).to(
                    self.device)
             else:
                _UpperCAmelCase  :  int				       =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  :  List[Any]				       =latents.to(self.device)

          # scale the initial noise by the standard deviation required by the scheduler
          _UpperCAmelCase  :  Union[str, Any]				       =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[Any]				       ='eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
          _UpperCAmelCase  :  Optional[int]				       ={}
          if accepts_eta:
             _UpperCAmelCase  :  Dict				       =eta

          # check if the scheduler accepts generator
          _UpperCAmelCase  :  List[Any]				       ='generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
          if accepts_generator:
             _UpperCAmelCase  :  List[Any]				       =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  :  List[str]				       =torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                _UpperCAmelCase  :  Optional[int]				       =self.scheduler.scale_model_input(snake_case    ,				snake_case)

                # predict the noise residual
                _UpperCAmelCase  :  Tuple				       =self.unet(snake_case    ,				snake_case    ,				encoder_hidden_states=snake_case).sample

                # perform classifier free guidance
                if do_classifier_free_guidance:
                   _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =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  :  Optional[Any]				       =(
                       text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                   )
                   _UpperCAmelCase       ,			_UpperCAmelCase  :  Tuple				       =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  :  Optional[Any]				       =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  :  str				       =1 / 0.1_82_15 * latents
          _UpperCAmelCase  :  Tuple				       =self.vae.decode(snake_case).sample

          _UpperCAmelCase  :  Union[str, Any]				       =(image / 2 + 0.5).clamp(0    ,				1)
          _UpperCAmelCase  :  Any				       =image.cpu().permute(0    ,				2    ,				3    ,				1).numpy()

          if output_type == "pil":
             _UpperCAmelCase  :  Tuple				       =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 os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase				=pytest.mark.integration





@pytest.mark.parametrize('path'			,       ['paws', 'csv']				)
def lowerCamelCase__     (		__lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				Optional[Any]				):







   '''simple docstring'''




   inspect_dataset(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =path + '.py'
   assert script_name in os.listdir(__lowerCamelCase				)
   assert "__pycache__" not in os.listdir(__lowerCamelCase				)





@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'				)
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'				)
@pytest.mark.parametrize('path'			,       ['accuracy']				)
def lowerCamelCase__     (		__lowerCamelCase			:				Dict			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   inspect_metric(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[Any]				       =path + '.py'
   assert script_name in os.listdir(__lowerCamelCase				)
   assert "__pycache__" not in os.listdir(__lowerCamelCase				)





@pytest.mark.parametrize(
    'path, config_name, expected_splits'			,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Any			,       __lowerCamelCase			:				Optional[int]			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =get_dataset_config_info(__lowerCamelCase			,       config_name=__lowerCamelCase				)
   assert info.config_name == config_name
   assert list(info.splits.keys()				) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'			,       [
        ('paws', None, ValueError),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				List[str]				):







   '''simple docstring'''




   with pytest.raises(__lowerCamelCase				):
      get_dataset_config_info(__lowerCamelCase			,       config_name=__lowerCamelCase				)





@pytest.mark.parametrize(
    'path, expected'			,       [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				List[str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =get_dataset_config_names(__lowerCamelCase				)
   assert expected in config_names





@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'			,       [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				List[Any]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[int]				       =get_dataset_infos(__lowerCamelCase				)
   assert list(infos.keys()				) == expected_configs
   _UpperCAmelCase  :  Optional[int]				       =expected_configs[0]
   assert expected_config in infos
   _UpperCAmelCase  :  Optional[int]				       =infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()				) == expected_splits_in_first_config





@pytest.mark.parametrize(
    'path, expected_config, expected_splits'			,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =get_dataset_infos(__lowerCamelCase				)
   assert expected_config in infos
   _UpperCAmelCase  :  Dict				       =infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()				) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'			,       [
        ('paws', None, ValueError),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Union[str, Any]			,       __lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				Any				):







   '''simple docstring'''




   with pytest.raises(__lowerCamelCase				):
      get_dataset_split_names(__lowerCamelCase			,       config_name=__lowerCamelCase				)

