




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Tuple						=			{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Any							=   "speech_to_text"
			lowerCAmelCase_					:				List[str]							=   ["past_key_values"]
			lowerCAmelCase_					:				List[str]							=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__( self    ,							a__=10_000    ,							a__=12    ,							a__=2_048    ,							a__=4    ,							a__=6    ,							a__=2_048    ,							a__=4    ,							a__=0.0    ,							a__=0.0    ,							a__=True    ,							a__=True    ,							a__="relu"    ,							a__=256    ,							a__=0.1    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0_2    ,							a__=2    ,							a__=True    ,							a__=1    ,							a__=0    ,							a__=2    ,							a__=6_000    ,							a__=1_024    ,							a__=2    ,							a__=(5, 5)    ,							a__=1_024    ,							a__=80    ,							a__=1    ,							**a__    ,							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				vocab_size
										snake_case_      =				d_model
										snake_case_      =				encoder_ffn_dim
										snake_case_      =				encoder_layers
										snake_case_      =				encoder_attention_heads
										snake_case_      =				decoder_ffn_dim
										snake_case_      =				decoder_layers
										snake_case_      =				decoder_attention_heads
										snake_case_      =				dropout
										snake_case_      =				attention_dropout
										snake_case_      =				activation_dropout
										snake_case_      =				activation_function
										snake_case_      =				init_std
										snake_case_      =				encoder_layerdrop
										snake_case_      =				decoder_layerdrop
										snake_case_      =				use_cache
										snake_case_      =				encoder_layers
										snake_case_      =				scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case_      =				max_source_positions
										snake_case_      =				max_target_positions
										snake_case_      =				num_conv_layers
										snake_case_      =				list(a__							)
										snake_case_      =				conv_channels
										snake_case_      =				input_feat_per_channel
										snake_case_      =				input_channels

										if len(self.conv_kernel_sizes							) != self.num_conv_layers:
																	raise ValueError(
																	    "Configuration for convolutional module is incorrect. "
																	    "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` "
																	    F'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes							)}`, '
																	    F'`config.num_conv_layers = {self.num_conv_layers}`.'							)

										super().__init__(
										    pad_token_id=a__    ,							bos_token_id=a__    ,							eos_token_id=a__    ,							is_encoder_decoder=a__    ,							decoder_start_token_id=a__    ,							**a__    ,							)










'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




@slow
@require_torch_gpu
class 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
										snake_case_      =				torch.manual_seed(0							)
										snake_case_      =				pipe(
										    image=a__    ,							generator=a__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type="numpy"    ,							).images

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

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]							)

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




