




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


a_   		=     False




class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      pass







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

      """simple docstring"""

      def       snake_case	(     self					: Optional[Any]				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







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

          __UpperCamelCase  		=						load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

          __UpperCamelCase  		=						torch.manual_seed(0				)
          __UpperCamelCase  		=						pipe.dual_guided(
              prompt='''first prompt''' ,       image=snake_case ,       text_to_image_strength=0.75 ,       generator=snake_case ,       guidance_scale=7.5 ,       num_inference_steps=2 ,       output_type='''numpy''' ,       ).images

          with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(snake_case				)
              __UpperCamelCase  		=						VersatileDiffusionPipeline.from_pretrained(snake_case ,       torch_dtype=torch.floataa				)
          pipe.to(snake_case				)
          pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						generator.manual_seed(0				)
          __UpperCamelCase  		=						pipe.dual_guided(
              prompt='''first prompt''' ,       image=snake_case ,       text_to_image_strength=0.75 ,       generator=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       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' ,       torch_dtype=torch.floataa				)
          pipe.to(snake_case				)
          pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						'''cyberpunk 2077'''
          __UpperCamelCase  		=						load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
          __UpperCamelCase  		=						torch.manual_seed(0				)
          __UpperCamelCase  		=						pipe.dual_guided(
              prompt=snake_case ,       image=snake_case ,       text_to_image_strength=0.75 ,       generator=snake_case ,       guidance_scale=7.5 ,       num_inference_steps=50 ,       output_type='''numpy''' ,       ).images

          __UpperCamelCase  		=						image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]				)

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

          __UpperCamelCase  		=						'''A painting of a squirrel eating a burger '''
          __UpperCamelCase  		=						torch.manual_seed(0				)
          __UpperCamelCase  		=						pipe.text_to_image(
              prompt=snake_case ,       generator=snake_case ,       guidance_scale=7.5 ,       num_inference_steps=50 ,       output_type='''numpy'''				).images

          __UpperCamelCase  		=						image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]				)

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

          __UpperCamelCase  		=						pipe.image_variation(snake_case ,       generator=snake_case ,       output_type='''numpy'''				).images

          __UpperCamelCase  		=						image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]				)

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












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					"big_bird"







      def __init__(     self					: str ,       snake_case					: Optional[Any]=50358 ,       snake_case					: Union[str, Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: List[Any]=12 ,       snake_case					: Tuple=3072 ,       snake_case					: Tuple="gelu_new" ,       snake_case					: Dict=0.1 ,       snake_case					: Any=0.1 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: Union[str, Any]=0.02 ,       snake_case					: Union[str, Any]=1E-12 ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Any=66 ,       snake_case					: str="block_sparse" ,       snake_case					: Any=True ,       snake_case					: Tuple=False ,       snake_case					: List[Any]=64 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=None ,       **snake_case					: Any ,       ):
          super().__init__(
              pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       sep_token_id=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						rescale_embeddings
          __UpperCamelCase  		=						attention_type
          __UpperCamelCase  		=						use_bias
          __UpperCamelCase  		=						block_size
          __UpperCamelCase  		=						num_random_blocks
          __UpperCamelCase  		=						classifier_dropout







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @property
      def       snake_case	(     self					: int				):
          if self.task == "multiple-choice":
              __UpperCamelCase  		=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              __UpperCamelCase  		=						{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]				)






