

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase				:   Dict					   =							False





class 					__UpperCAmelCase     (       unittest.TestCase						):
   pass






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



   def        __magic_name__    (							self							:     Dict						):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



   def        __magic_name__    (							self							:     Any						):
     UpperCAmelCase       :							Union[str, Any]  = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''',		torch_dtype=torch.floataa						)
     pipe.to(__UpperCAmelCase						)
     pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

     UpperCAmelCase       :							Optional[Any]  = load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)

     UpperCAmelCase       :							Optional[Any]  = torch.manual_seed(0						)
     UpperCAmelCase       :							Union[str, Any]  = pipe.dual_guided(
         prompt='''first prompt''',		image=__UpperCAmelCase,		text_to_image_strength=0.7_5,		generator=__UpperCAmelCase,		guidance_scale=7.5,		num_inference_steps=2,		output_type='''numpy''',		).images

     with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(__UpperCAmelCase						)
       UpperCAmelCase       :							Optional[Any]  = VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase,		torch_dtype=torch.floataa						)
     pipe.to(__UpperCAmelCase						)
     pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

     UpperCAmelCase       :							int  = generator.manual_seed(0						)
     UpperCAmelCase       :							Any  = pipe.dual_guided(
         prompt='''first prompt''',		image=__UpperCAmelCase,		text_to_image_strength=0.7_5,		generator=__UpperCAmelCase,		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        __magic_name__    (							self							:     List[str]						):
     UpperCAmelCase       :							str  = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''',		torch_dtype=torch.floataa						)
     pipe.to(__UpperCAmelCase						)
     pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

     UpperCAmelCase       :							Any  = "cyberpunk 2077"
     UpperCAmelCase       :							str  = load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
     UpperCAmelCase       :							List[str]  = torch.manual_seed(0						)
     UpperCAmelCase       :							Optional[int]  = pipe.dual_guided(
         prompt=__UpperCAmelCase,		image=__UpperCAmelCase,		text_to_image_strength=0.7_5,		generator=__UpperCAmelCase,		guidance_scale=7.5,		num_inference_steps=5_0,		output_type='''numpy''',		).images

     UpperCAmelCase       :							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       :							str  = np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]						)

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

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

     UpperCAmelCase       :							List[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       :							str  = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

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

     UpperCAmelCase       :							Dict  = pipe.image_variation(__UpperCAmelCase,		generator=__UpperCAmelCase,		output_type='''numpy'''						).images

     UpperCAmelCase       :							Dict  = 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       :							Any  = np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]						)

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









"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       """dpr"""






    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    int=30522				, __UpperCAmelCase				:    Union[str, Any]=768				, __UpperCAmelCase				:    Dict=12				, __UpperCAmelCase				:    List[str]=12				, __UpperCAmelCase				:    Any=3072				, __UpperCAmelCase				:    Optional[int]="gelu"				, __UpperCAmelCase				:    Any=0.1				, __UpperCAmelCase				:    Union[str, Any]=0.1				, __UpperCAmelCase				:    str=512				, __UpperCAmelCase				:    List[str]=2				, __UpperCAmelCase				:    Tuple=0.02				, __UpperCAmelCase				:    List[str]=1e-12				, __UpperCAmelCase				:    List[str]=0				, __UpperCAmelCase				:    str="absolute"				, __UpperCAmelCase				:    int = 0				, **__UpperCAmelCase				:    Tuple				, ):
      super().__init__(pad_token_id=__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		List[Any]      							=			vocab_size
      a						:		Optional[Any]      							=			hidden_size
      a						:		Union[str, Any]      							=			num_hidden_layers
      a						:		Dict      							=			num_attention_heads
      a						:		int      							=			hidden_act
      a						:		Any      							=			intermediate_size
      a						:		Any      							=			hidden_dropout_prob
      a						:		Dict      							=			attention_probs_dropout_prob
      a						:		Any      							=			max_position_embeddings
      a						:		Union[str, Any]      							=			type_vocab_size
      a						:		Optional[Any]      							=			initializer_range
      a						:		Dict      							=			layer_norm_eps
      a						:		int      							=			projection_dim
      a						:		str      							=			position_embedding_type






