'''simple docstring'''

from typing import Callable, Optional, Union

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


_A      :				str 				=    logging.get_logger(__name__)

_A      :				int 				=    {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	Union[str, Any]				     = """prophetnet"""
 _SCREAMING_SNAKE_CASE					:	Any				     = ["""past_key_values"""]
 _SCREAMING_SNAKE_CASE					:	Optional[Any]				     = {
     """num_attention_heads""": """num_encoder_attention_heads""",
 }
 def __init__(				self       :     int				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.1				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[str, Callable]] = "gelu"				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 3_05_22				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 10_24				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 40_96				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 12				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 16				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 40_96				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 12				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 16				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.1				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.1				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 5_12				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.0_2				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = True				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = True				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 0				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 2				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 32				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 1_28				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = False				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.0				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = True				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 0				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 1				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 2				,	**SCREAMING_SNAKE_CASE__       :     List[Any]				,	)  ->   Optional[Any]:
  __lowerCAmelCase    							=						vocab_size
  __lowerCAmelCase    							=						hidden_size
  __lowerCAmelCase    							=						encoder_ffn_dim
  __lowerCAmelCase    							=						num_encoder_layers
  __lowerCAmelCase    							=						num_encoder_attention_heads
  __lowerCAmelCase    							=						decoder_ffn_dim
  __lowerCAmelCase    							=						num_decoder_layers
  __lowerCAmelCase    							=						num_decoder_attention_heads
  __lowerCAmelCase    							=						max_position_embeddings
  __lowerCAmelCase    							=						init_std  # Normal(0, this parameter)
  __lowerCAmelCase    							=						activation_function

  # parameters for prophetnet
  __lowerCAmelCase    							=						ngram
  __lowerCAmelCase    							=						num_buckets
  __lowerCAmelCase    							=						relative_max_distance
  __lowerCAmelCase    							=						disable_ngram_loss
  __lowerCAmelCase    							=						eps

  # 3 Types of Dropout
  __lowerCAmelCase    							=						attention_dropout
  __lowerCAmelCase    							=						activation_dropout
  __lowerCAmelCase    							=						dropout

  __lowerCAmelCase    							=						use_cache

  super().__init__(
      pad_token_id=SCREAMING_SNAKE_CASE__				,	bos_token_id=SCREAMING_SNAKE_CASE__				,	eos_token_id=SCREAMING_SNAKE_CASE__				,	is_encoder_decoder=SCREAMING_SNAKE_CASE__				,	add_cross_attention=SCREAMING_SNAKE_CASE__				,	decoder_start_token_id=SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__				,	)
 @property
 def a (				self       :     Optional[Any]							)  ->   int:
  return self.num_encoder_layers + self.num_decoder_layers





 @num_hidden_layers.setter
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]							)  ->   Any:
  raise NotImplementedError(
      """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
      """ `num_decoder_layers`."""							)







'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

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


_A      :				Any 				=    False







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


 '''simple docstring'''






 def a (				self       :     Tuple							)  ->   Dict:
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a (				self       :     Optional[int]							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.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__							)

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

  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	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__							)
   __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE__							)

  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						generator.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	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 a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
  pipe.remove_unused_weights()
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						"""cyberpunk 2077"""
  __lowerCAmelCase    							=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt=SCREAMING_SNAKE_CASE__				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=50				,	output_type="""numpy"""				,	).images

  __lowerCAmelCase    							=						image[0, 2_53:2_56, 2_53:2_56, -1]

  assert image.shape == (1, 5_12, 5_12, 3)
  __lowerCAmelCase    							=						np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]							)

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






