



"""simple docstring"""

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


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"speech_to_text_2"
						_lowerCAmelCase							=					["past_key_values"]
						_lowerCAmelCase							=					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}



						def __init__(self						,     _lowercase=10000						,     _lowercase=6						,     _lowercase=2048						,     _lowercase=4						,     _lowercase=0.0						,     _lowercase=True						,     _lowercase="relu"						,     _lowercase=256						,     _lowercase=0.1						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=2						,     _lowercase=True						,     _lowercase=1						,     _lowercase=0						,     _lowercase=2						,     _lowercase=1024						,     **_lowercase						,     ):





													'''simple docstring'''

													__a		:		Optional[int]									=       vocab_size
													__a		:		Any									=       d_model
													__a		:		List[str]									=       decoder_ffn_dim
													__a		:		str									=       decoder_layers
													__a		:		Optional[Any]									=       decoder_attention_heads
													__a		:		Optional[int]									=       dropout
													__a		:		Tuple									=       attention_dropout
													__a		:		int									=       activation_dropout
													__a		:		List[str]									=       activation_function
													__a		:		int									=       init_std
													__a		:		Dict									=       decoder_layerdrop
													__a		:		Dict									=       use_cache
													__a		:		Tuple									=       decoder_layers
													__a		:		Any									=       scale_embedding  # scale factor will be sqrt(d_model) if True
													__a		:		Optional[Any]									=       max_target_positions

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






"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

