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 			lowercase_				(			_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Optional[Any]):
    if isinstance(_lowerCamelCase		,	torch.Tensor):
        return image
    elif isinstance(_lowerCamelCase		,	PIL.Image.Image):
        lowercase__ :   List[str]    					=				[image]

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

def 			lowercase_				(			_lowerCamelCase							:       Any		,	_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       str=0.9995):
    if not isinstance(_lowerCamelCase		,	np.ndarray):
        lowercase__ :   List[str]    					=				True
        lowercase__ :   Tuple    					=				va.device
        lowercase__ :   Union[str, Any]    					=				va.cpu().numpy()
        lowercase__ :   str    					=				va.cpu().numpy()

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

    if inputs_are_torch:
        lowercase__ :   Tuple    					=				torch.from_numpy(_lowerCamelCase).to(_lowerCamelCase)

    return va

def 			lowercase_				(			_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       Union[str, Any]):
    lowercase__ :   Any    					=				F.normalize(_lowerCamelCase		,	dim=-1)
    lowercase__ :   Optional[Any]    					=				F.normalize(_lowerCamelCase		,	dim=-1)
    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)

def 			lowercase_				(			_lowerCamelCase							:       int		,	_lowerCamelCase							:       int):
    for param in model.parameters():
        lowercase__ :   str    					=				value



class   snake_case_       (		__A       ):






   def __init__(      self				:     str	,						lowercase_				:     AutoencoderKL	,						lowercase_				:     CLIPTextModel	,						lowercase_				:     CLIPModel	,						lowercase_				:     CLIPTokenizer	,						lowercase_				:     UNetaDConditionModel	,						lowercase_				:     Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]	,						lowercase_				:     CLIPFeatureExtractor	,						lowercase_				:     Dict=None	,						lowercase_				:     Tuple=None	,						lowercase_				:     Tuple=None	,						)					->						Union[str, Any]:
       super().__init__()
       self.register_modules(
           vae=lowercase_	,						text_encoder=lowercase_	,						clip_model=lowercase_	,						tokenizer=lowercase_	,						unet=lowercase_	,						scheduler=lowercase_	,						feature_extractor=lowercase_	,						coca_model=lowercase_	,						coca_tokenizer=lowercase_	,						coca_transform=lowercase_	,						)
       lowercase__ :   int    					=				(
           feature_extractor.size
           if isinstance(feature_extractor.size	,						lowercase_  )
           else feature_extractor.size["shortest_edge"]
       )
       lowercase__ :   Any    					=				transforms.Normalize(mean=feature_extractor.image_mean	,						std=feature_extractor.image_std  )
       set_requires_grad(self.text_encoder	,						lowercase_  )
       set_requires_grad(self.clip_model	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     Optional[Union[str, int]] = "auto"  )					->						int:
       if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           lowercase__ :   int    					=				self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       self.enable_attention_slicing(lowercase_  )






   def       __UpperCamelCase     (      self				:     Tuple  )					->						int:
       set_requires_grad(self.vae	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Tuple  )					->						Optional[Any]:
       set_requires_grad(self.vae	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						Optional[Any]:
       set_requires_grad(self.unet	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Optional[int]:
       set_requires_grad(self.unet	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     Any	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Dict  )					->						List[Any]:
       # get the original timestep using init_timestep
       lowercase__ :   Any    					=				min(int(num_inference_steps * strength  )	,						lowercase_  )

       lowercase__ :   Optional[int]    					=				max(num_inference_steps - init_timestep	,						0  )
       lowercase__ :   List[Any]    					=				self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start






   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     Any	,						lowercase_				:     Any	,						lowercase_				:     int	,						lowercase_				:     Optional[int]	,						lowercase_				:     str	,						lowercase_				:     Tuple=None  )					->						Union[str, Any]:
       if not isinstance(lowercase_	,						torch.Tensor  ):
           raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowercase_  )}'''  )

       lowercase__ :   int    					=				image.to(device=lowercase_	,						dtype=lowercase_  )

       if isinstance(lowercase_	,						lowercase_  ):
           lowercase__ :   Tuple    					=				[
               self.vae.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(lowercase_  )
           ]
           lowercase__ :   Any    					=				torch.cat(lowercase_	,						dim=0  )
       else:
           lowercase__ :   List[str]    					=				self.vae.encode(lowercase_  ).latent_dist.sample(lowercase_  )

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

       lowercase__ :   int    					=				randn_tensor(init_latents.shape	,						generator=lowercase_	,						device=lowercase_	,						dtype=lowercase_  )

       # get latents
       lowercase__ :   str    					=				self.scheduler.add_noise(lowercase_	,						lowercase_	,						lowercase_  )
       lowercase__ :   Optional[Any]    					=				init_latents

       return latents






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     Tuple  )					->						List[str]:
       lowercase__ :   int    					=				self.coca_transform(lowercase_  ).unsqueeze(0  )
       with torch.no_grad(), torch.cuda.amp.autocast():
           lowercase__ :   int    					=				self.coca_model.generate(transformed_image.to(device=self.device	,						dtype=self.coca_model.dtype  )  )
       lowercase__ :   List[Any]    					=				self.coca_tokenizer.decode(generated[0].cpu().numpy()  )
       return generated.split("<end_of_text>"  )[0].replace("<start_of_text>"	,						""  ).rstrip(" .,"  )






   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     List[Any]	,						lowercase_				:     Any  )					->						int:
       lowercase__ :   Optional[int]    					=				self.feature_extractor.preprocess(lowercase_  )
       lowercase__ :   List[Any]    					=				torch.from_numpy(clip_image_input["pixel_values"][0]  ).unsqueeze(0  ).to(self.device  ).half()
       lowercase__ :   int    					=				self.clip_model.get_image_features(lowercase_  )
       lowercase__ :   List[str]    					=				image_embeddings_clip / image_embeddings_clip.norm(p=2	,						dim=-1	,						keepdim=lowercase_  )
       lowercase__ :   Tuple    					=				image_embeddings_clip.repeat_interleave(lowercase_	,						dim=0  )
       return image_embeddings_clip






   @torch.enable_grad()
   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     int	,						lowercase_				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     int	,						lowercase_				:     int	,						lowercase_				:     int	,						)					->						Optional[int]:
       lowercase__ :   Dict    					=				latents.detach().requires_grad_()

       lowercase__ :   List[Any]    					=				self.scheduler.scale_model_input(lowercase_	,						lowercase_  )

       # predict the noise residual
       lowercase__ :   Optional[Any]    					=				self.unet(lowercase_	,						lowercase_	,						encoder_hidden_states=lowercase_  ).sample

       if isinstance(self.scheduler	,						(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)  ):
           lowercase__ :   Optional[int]    					=				self.scheduler.alphas_cumprod[timestep]
           lowercase__ :   Tuple    					=				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
           lowercase__ :   Union[str, Any]    					=				(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

           lowercase__ :   Optional[Any]    					=				torch.sqrt(lowercase_  )
           lowercase__ :   List[str]    					=				pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler	,						lowercase_  ):
           lowercase__ :   Tuple    					=				self.scheduler.sigmas[index]
           lowercase__ :   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
       lowercase__ :   Dict    					=				1 / 0.1_82_15 * sample
       lowercase__ :   Optional[Any]    					=				self.vae.decode(lowercase_  ).sample
       lowercase__ :   Optional[Any]    					=				(image / 2 + 0.5).clamp(0	,						1  )

       lowercase__ :   List[str]    					=				transforms.Resize(self.feature_extractor_size  )(lowercase_  )
       lowercase__ :   Optional[Any]    					=				self.normalize(lowercase_  ).to(latents.dtype  )

       lowercase__ :   List[Any]    					=				self.clip_model.get_image_features(lowercase_  )
       lowercase__ :   List[str]    					=				image_embeddings_clip / image_embeddings_clip.norm(p=2	,						dim=-1	,						keepdim=lowercase_  )

       lowercase__ :   int    					=				spherical_dist_loss(lowercase_	,						lowercase_  ).mean() * clip_guidance_scale

       lowercase__ :   int    					=				-torch.autograd.grad(lowercase_	,						lowercase_  )[0]

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







   @torch.no_grad()
   def __call__(      self				:     Tuple	,						lowercase_				:     Union[torch.FloatTensor, PIL.Image.Image]	,						lowercase_				:     Union[torch.FloatTensor, PIL.Image.Image]	,						lowercase_				:     Optional[str] = None	,						lowercase_				:     Optional[str] = None	,						lowercase_				:     Optional[int] = 5_12	,						lowercase_				:     Optional[int] = 5_12	,						lowercase_				:     float = 0.6	,						lowercase_				:     Optional[int] = 50	,						lowercase_				:     Optional[float] = 7.5	,						lowercase_				:     Optional[int] = 1	,						lowercase_				:     float = 0.0	,						lowercase_				:     Optional[float] = 1_00	,						lowercase_				:     Optional[torch.Generator] = None	,						lowercase_				:     Optional[str] = "pil"	,						lowercase_				:     bool = True	,						lowercase_				:     float = 0.8	,						lowercase_				:     float = 0.1	,						lowercase_				:     float = 0.1	,						)					->						Dict:
       if isinstance(lowercase_	,						lowercase_  ) and len(lowercase_  ) != batch_size:
           raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowercase_  )} 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(lowercase_	,						torch.Generator  ) and batch_size > 1:
           lowercase__ :   List[str]    					=				[generator] + [None] * (batch_size - 1)

       lowercase__ :   str    					=				[
           ("model", self.coca_model is None),
           ("tokenizer", self.coca_tokenizer is None),
           ("transform", self.coca_transform is None),
       ]
       lowercase__ :   Dict    					=				[x[0] for x in coca_is_none if x[1]]
       lowercase__ :   int    					=				", ".join(lowercase_  )
       # generate prompts with coca model if prompt is None
       if content_prompt is None:
           if len(lowercase_  ):
               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.'''  )
           lowercase__ :   Dict    					=				self.get_image_description(lowercase_  )
       if style_prompt is None:
           if len(lowercase_  ):
               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.'''  )
           lowercase__ :   Optional[int]    					=				self.get_image_description(lowercase_  )

       # get prompt text embeddings for content and style
       lowercase__ :   Tuple    					=				self.tokenizer(
           lowercase_	,						padding="max_length"	,						max_length=self.tokenizer.model_max_length	,						truncation=lowercase_	,						return_tensors="pt"	,						)
       lowercase__ :   int    					=				self.text_encoder(content_text_input.input_ids.to(self.device  )  )[0]

       lowercase__ :   List[str]    					=				self.tokenizer(
           lowercase_	,						padding="max_length"	,						max_length=self.tokenizer.model_max_length	,						truncation=lowercase_	,						return_tensors="pt"	,						)
       lowercase__ :   Optional[Any]    					=				self.text_encoder(style_text_input.input_ids.to(self.device  )  )[0]

       lowercase__ :   int    					=				slerp(lowercase_	,						lowercase_	,						lowercase_  )

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

       # set timesteps
       lowercase__ :   Optional[int]    					=				"offset" in set(inspect.signature(self.scheduler.set_timesteps  ).parameters.keys()  )
       lowercase__ :   Optional[Any]    					=				{}
       if accepts_offset:
           lowercase__ :   Optional[Any]    					=				1

       self.scheduler.set_timesteps(lowercase_	,						**lowercase_  )
       # 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  )

       lowercase__					,   lowercase__ :   Optional[int]    					=				self.get_timesteps(lowercase_	,						lowercase_	,						self.device  )
       lowercase__ :   str    					=				timesteps[:1].repeat(lowercase_  )

       # Preprocess image
       lowercase__ :   int    					=				preprocess(lowercase_	,						lowercase_	,						lowercase_  )
       lowercase__ :   Tuple    					=				self.prepare_latents(
           lowercase_	,						lowercase_	,						lowercase_	,						text_embeddings.dtype	,						self.device	,						lowercase_  )

       lowercase__ :   List[Any]    					=				preprocess(lowercase_	,						lowercase_	,						lowercase_  )
       lowercase__ :   Union[str, Any]    					=				self.prepare_latents(
           lowercase_	,						lowercase_	,						lowercase_	,						text_embeddings.dtype	,						self.device	,						lowercase_  )

       lowercase__ :   Any    					=				slerp(lowercase_	,						lowercase_	,						lowercase_  )

       if clip_guidance_scale > 0:
           lowercase__ :   Tuple    					=				self.get_clip_image_embeddings(lowercase_	,						lowercase_  )
           lowercase__ :   Optional[Any]    					=				self.get_clip_image_embeddings(lowercase_	,						lowercase_  )
           lowercase__ :   Union[str, Any]    					=				slerp(
               lowercase_	,						lowercase_	,						lowercase_  )

       # 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.
       lowercase__ :   Optional[int]    					=				guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
           lowercase__ :   int    					=				content_text_input.input_ids.shape[-1]
           lowercase__ :   Optional[Any]    					=				self.tokenizer([""]	,						padding="max_length"	,						max_length=lowercase_	,						return_tensors="pt"  )
           lowercase__ :   List[str]    					=				self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
           # duplicate unconditional embeddings for each generation per prompt
           lowercase__ :   Union[str, Any]    					=				uncond_embeddings.repeat_interleave(lowercase_	,						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
           lowercase__ :   Tuple    					=				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`.
       lowercase__ :   Union[str, Any]    					=				(batch_size, self.unet.config.in_channels, height // 8, width // 8)
       lowercase__ :   Union[str, Any]    					=				text_embeddings.dtype
       if latents is None:
           if self.device.type == "mps":
               # randn does not work reproducibly on mps
               lowercase__ :   str    					=				torch.randn(lowercase_	,						generator=lowercase_	,						device="cpu"	,						dtype=lowercase_  ).to(
                   self.device  )
           else:
               lowercase__ :   Tuple    					=				torch.randn(lowercase_	,						generator=lowercase_	,						device=self.device	,						dtype=lowercase_  )
       else:
           if latents.shape != latents_shape:
               raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''  )
           lowercase__ :   List[Any]    					=				latents.to(self.device  )

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

       # check if the scheduler accepts generator
       lowercase__ :   Optional[Any]    					=				"generator" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
       if accepts_generator:
           lowercase__ :   Optional[int]    					=				generator

       with self.progress_bar(total=lowercase_  ):
           for i, t in enumerate(lowercase_  ):
               # expand the latents if we are doing classifier free guidance
               lowercase__ :   Tuple    					=				torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
               lowercase__ :   List[Any]    					=				self.scheduler.scale_model_input(lowercase_	,						lowercase_  )

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

               # perform classifier free guidance
               if do_classifier_free_guidance:
                   lowercase__					,   lowercase__ :   int    					=				noise_pred.chunk(2  )
                   lowercase__ :   Dict    					=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               # perform clip guidance
               if clip_guidance_scale > 0:
                   lowercase__ :   Optional[Any]    					=				(
                       text_embeddings.chunk(2  )[1] if do_classifier_free_guidance else text_embeddings
                   )
                   lowercase__					,   lowercase__ :   Dict    					=				self.cond_fn(
                       lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						)

               # compute the previous noisy sample x_t -> x_t-1
               lowercase__ :   Tuple    					=				self.scheduler.step(lowercase_	,						lowercase_	,						lowercase_	,						**lowercase_  ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       lowercase__ :   Union[str, Any]    					=				1 / 0.1_82_15 * latents
       lowercase__ :   Optional[Any]    					=				self.vae.decode(lowercase_  ).sample

       lowercase__ :   List[Any]    					=				(image / 2 + 0.5).clamp(0	,						1  )
       lowercase__ :   Dict    					=				image.cpu().permute(0	,						2	,						3	,						1  ).numpy()

       if output_type == "pil":
           lowercase__ :   Optional[int]    					=				self.numpy_to_pil(lowercase_  )

       if not return_dict:
           return (image, None)

       return StableDiffusionPipelineOutput(images=lowercase_	,						nsfw_content_detected=lowercase_  )




"""simple docstring"""






import os
import sys
import unittest


lowerCAmelCase__  :  Tuple						     =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase__  :  Tuple						     =	os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowerCAmelCase__  :  Optional[Any]						     =	os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							get_test_to_tester_mapping(lowerCamelCase__			)
									UpperCAmelCase__             =							get_test_to_tester_mapping(lowerCamelCase__			)

									UpperCAmelCase__             =							{'BertModelTest': 'BertModelTester'}

									UpperCAmelCase__             =							{
									    'BlipModelTest': 'BlipModelTester',
									    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
									    'BlipTextModelTest': 'BlipTextModelTester',
									    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
									    'BlipVQAModelTest': 'BlipVQAModelTester',
									    'BlipVisionModelTest': 'BlipVisionModelTester',
									}

									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)
									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							get_model_to_test_mapping(lowerCamelCase__			)
									UpperCAmelCase__             =							get_model_to_test_mapping(lowerCamelCase__			)

									UpperCAmelCase__             =							{
									    'BertForMaskedLM': ['BertModelTest'],
									    'BertForMultipleChoice': ['BertModelTest'],
									    'BertForNextSentencePrediction': ['BertModelTest'],
									    'BertForPreTraining': ['BertModelTest'],
									    'BertForQuestionAnswering': ['BertModelTest'],
									    'BertForSequenceClassification': ['BertModelTest'],
									    'BertForTokenClassification': ['BertModelTest'],
									    'BertLMHeadModel': ['BertModelTest'],
									    'BertModel': ['BertModelTest'],
									}

									UpperCAmelCase__             =							{
									    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
									    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
									    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
									    'BlipModel': ['BlipModelTest'],
									    'BlipTextModel': ['BlipTextModelTest'],
									    'BlipVisionModel': ['BlipVisionModelTest'],
									}

									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)
									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							get_model_to_tester_mapping(lowerCamelCase__			)
									UpperCAmelCase__             =							get_model_to_tester_mapping(lowerCamelCase__			)

									UpperCAmelCase__             =							{
									    'BertForMaskedLM': ['BertModelTester'],
									    'BertForMultipleChoice': ['BertModelTester'],
									    'BertForNextSentencePrediction': ['BertModelTester'],
									    'BertForPreTraining': ['BertModelTester'],
									    'BertForQuestionAnswering': ['BertModelTester'],
									    'BertForSequenceClassification': ['BertModelTester'],
									    'BertForTokenClassification': ['BertModelTester'],
									    'BertLMHeadModel': ['BertModelTester'],
									    'BertModel': ['BertModelTester'],
									}

									UpperCAmelCase__             =							{
									    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
									    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
									    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
									    'BlipModel': ['BlipModelTester'],
									    'BlipTextModel': ['BlipTextModelTester'],
									    'BlipVisionModel': ['BlipVisionModelTester'],
									}

									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)
									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)



