
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCamelCase      =False




class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     pass




@nightly
@require_torch_gpu
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''  )
           # remove text_unet
           pipe.remove_unused_weights()
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[Any]					=				'''A painting of a squirrel eating a burger '''
           UpperCamelCase__    :   Optional[Any]					=				torch.manual_seed(0  )
           UpperCamelCase__    :   Tuple					=				pipe(
               prompt=__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						guidance_scale=7.5						,						num_inference_steps=2						,						output_type='''numpy'''  ).images

           with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Dict					=				VersatileDiffusionTextToImagePipeline.from_pretrained(__SCREAMING_SNAKE_CASE  )
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Any					=				generator.manual_seed(0  )
           UpperCamelCase__    :   str					=				pipe(
               prompt=__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						guidance_scale=7.5						,						num_inference_steps=2						,						output_type='''numpy'''  ).images

           assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				VersatileDiffusionTextToImagePipeline.from_pretrained(
               '''shi-labs/versatile-diffusion'''						,						torch_dtype=torch.floataa  )
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				'''A painting of a squirrel eating a burger '''
           UpperCamelCase__    :   Dict					=				torch.manual_seed(0  )
           UpperCamelCase__    :   Any					=				pipe(
               prompt=__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						guidance_scale=7.5						,						num_inference_steps=5_0						,						output_type='''numpy'''  ).images

           UpperCamelCase__    :   Union[str, Any]					=				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)
           UpperCamelCase__    :   Tuple					=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2




import torch

from transformers import AutoModel




class 				_lowerCamelCase (      torch.nn.Module ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"  )  ->   Optional[Any]:




           """simple docstring"""







           super(__SCREAMING_SNAKE_CASE						,						self  ).__init__()

           UpperCamelCase__    :   Tuple					=				AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						,						return_dict=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.CosineSimilarity(3						,						1e-08  )
           UpperCamelCase__    :   List[Any]					=				torch.nn.Softmax(dim=1  )






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




           """simple docstring"""







           return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           return token_embeddings.sum(2						,						keepdim=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1  )  ->   str:




           """simple docstring"""







           return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				W_supports['''sizes'''].tolist()
           UpperCamelCase__    :   Tuple					=				W_supports['''start_token_id'''].item()
           UpperCamelCase__    :   int					=				W_supports['''end_token_id'''].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           UpperCamelCase__    :   Optional[int]					=				self.BERT(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.BERT(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				None

           UpperCamelCase__    :   Any					=				W_supports['''input_ids'''] == start_token_id
           UpperCamelCase__    :   Optional[int]					=				W_supports['''input_ids'''] == end_token_id

           for i, size in enumerate(__SCREAMING_SNAKE_CASE  ):
                 if i == 0:
                       UpperCamelCase__    :   int					=				0
                 else:
                       UpperCamelCase__    :   Optional[int]					=				support_sizes[i - 1]

                 UpperCamelCase__    :   List[Any]					=				S[s : s + size][start_token_masks[s : s + size]]
                 UpperCamelCase__    :   Optional[Any]					=				S[s : s + size][end_token_masks[s : s + size]]

                 UpperCamelCase__    :   Tuple					=				torch.matmul(q[i]						,						s_start.T  ).sum(1  ).softmax(0  )
                 UpperCamelCase__    :   Optional[Any]					=				torch.matmul(q[i]						,						s_end.T  ).sum(1  ).softmax(0  )

                 if p_starts is not None:
                       UpperCamelCase__    :   Union[str, Any]					=				torch.vstack((p_starts, p_start)  )
                       UpperCamelCase__    :   str					=				torch.vstack((p_ends, p_end)  )
                 else:
                       UpperCamelCase__    :   List[Any]					=				p_start
                       UpperCamelCase__    :   List[str]					=				p_end

           return p_starts, p_ends


