



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
				from ...feature_extraction_utils import FeatureExtractionMixin
				from ...tokenization_utils_base import PreTrainedTokenizerBase
				from ...utils import TensorType

_lowercase: str      			=		logging.get_logger(__name__)

_lowercase: List[str]      			=		{
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
_lowercase: Optional[int]      			=		[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
_lowercase: Tuple      			=		[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"whisper"
	__A							=							["past_key_values"]
	__A							=							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




	def __init__(self		,       lowerCamelCase_=51865		,       lowerCamelCase_=80		,       lowerCamelCase_=6		,       lowerCamelCase_=4		,       lowerCamelCase_=6		,       lowerCamelCase_=4		,       lowerCamelCase_=1536		,       lowerCamelCase_=1536		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=50257		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=256		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.02		,       lowerCamelCase_=False		,       lowerCamelCase_=1500		,       lowerCamelCase_=448		,       lowerCamelCase_=50256		,       lowerCamelCase_=50256		,       lowerCamelCase_=50256		,       lowerCamelCase_=None		,       lowerCamelCase_=[220, 50256]		,       lowerCamelCase_=False		,       lowerCamelCase_=256		,       lowerCamelCase_=False		,       lowerCamelCase_=0.05		,       lowerCamelCase_=10		,       lowerCamelCase_=2		,       lowerCamelCase_=0.0		,       lowerCamelCase_=10		,       lowerCamelCase_=0		,       lowerCamelCase_=7		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   vocab_size
						a					     =   num_mel_bins
						a					     =   d_model
						a					     =   encoder_layers
						a					     =   encoder_attention_heads
						a					     =   decoder_layers
						a					     =   decoder_attention_heads
						a					     =   decoder_ffn_dim
						a					     =   encoder_ffn_dim
						a					     =   dropout
						a					     =   attention_dropout
						a					     =   activation_dropout
						a					     =   activation_function
						a					     =   init_std
						a					     =   encoder_layerdrop
						a					     =   decoder_layerdrop
						a					     =   use_cache
						a					     =   encoder_layers
						a					     =   scale_embedding  # scale factor will be sqrt(d_model) if True
						a					     =   max_source_positions
						a					     =   max_target_positions

						# Audio Classification-specific parameters. Feel free to ignore for other classes.
						a					     =   classifier_proj_size
						a					     =   use_weighted_layer_sum

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						a					     =   apply_spec_augment
						a					     =   mask_time_prob
						a					     =   mask_time_length
						a					     =   mask_time_min_masks
						a					     =   mask_feature_prob
						a					     =   mask_feature_length
						a					     =   mask_feature_min_masks

						a					     =   median_filter_width

						super().__init__(
						    pad_token_id=lowerCamelCase_		,       bos_token_id=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		,       is_encoder_decoder=lowerCamelCase_		,       decoder_start_token_id=lowerCamelCase_		,       suppress_tokens=lowerCamelCase_		,       begin_suppress_tokens=lowerCamelCase_		,       **lowerCamelCase_		,       )




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   OrderedDict(
						    [
						        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
						    ]		)
						if self.use_past:
											a					     =   {0: "batch"}
						else:
											a					     =   {0: "batch", 1: "decoder_sequence"}

						if self.use_past:
											self.fill_with_past_key_values_(lowerCamelCase_		,       direction="inputs"		)

						return common_inputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = -1		,       lowerCamelCase_ = -1		,       lowerCamelCase_ = False		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 22050		,       lowerCamelCase_ = 5.0		,       lowerCamelCase_ = 220		,       ):
						"""simple docstring"""

						a					     =   OrderedDict()
						a					     =   OnnxConfig.generate_dummy_inputs(
						    self		,       preprocessor=preprocessor.feature_extractor		,       batch_size=lowerCamelCase_		,       framework=lowerCamelCase_		,       sampling_rate=lowerCamelCase_		,       time_duration=lowerCamelCase_		,       frequency=lowerCamelCase_		,       )
						a					     =   encoder_inputs["input_features"].shape[2]
						a					     =   encoder_sequence_length // 2 if self.use_past else seq_length

						a					     =   super().generate_dummy_inputs(
						    preprocessor.tokenizer		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   encoder_inputs.pop("input_features"		)
						a					     =   decoder_inputs.pop("decoder_input_ids"		)

						if "past_key_values" in decoder_inputs:
											a					     =   decoder_inputs.pop("past_key_values"		)

						return dummy_inputs




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 1E-3





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()
						self.register_modules(unet=lowerCamelCase_		,       scheduler=lowerCamelCase_		)




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,       generator=lowerCamelCase_		,       )
						a					     =   image.to(self.device		)

						# set step values
						self.scheduler.set_timesteps(lowerCamelCase_		)

						for t in self.progress_bar(self.scheduler.timesteps		):
											# 1. predict noise model_output
											a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

						a					     =   (image / 2 + 0.5).clamp(0		,       1		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		).numpy()
						if output_type == "pil":
											a					     =   self.numpy_to_pil(lowerCamelCase_		)

						if not return_dict:
											return (image,), "This is a local test"

						return ImagePipelineOutput(images=lowerCamelCase_		), "This is a local test"
