'''simple docstring'''




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

__SCREAMING_SNAKE_CASE       :     Tuple     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
__SCREAMING_SNAKE_CASE       :     Tuple     =  [
    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, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
    1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
    4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
    11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
    17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
    34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
__SCREAMING_SNAKE_CASE       :     List[Any]     =  [
    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, 1_350, 1_853, 1_982, 2_460, 2_627,
    3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
    7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
    14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
    22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
    42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]						=						"whisper"
      __UpperCamelCase:  int						=						["past_key_values"]
      __UpperCamelCase:  Optional[int]						=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(       self						:				Any						,					A						:				List[str]=51865						,					A						:				Dict=80						,					A						:				Any=6						,					A						:				Any=4						,					A						:				List[str]=6						,					A						:				Union[str, Any]=4						,					A						:				Optional[Any]=1536						,					A						:				Optional[int]=1536						,					A						:				Tuple=0.0						,					A						:				str=0.0						,					A						:				str=50257						,					A						:				Optional[int]=True						,					A						:				Union[str, Any]=True						,					A						:				Dict="gelu"						,					A						:				Optional[Any]=256						,					A						:				Tuple=0.0						,					A						:				List[str]=0.0						,					A						:				str=0.0						,					A						:				Optional[Any]=0.02						,					A						:				Tuple=False						,					A						:				Optional[Any]=1500						,					A						:				Any=448						,					A						:				Dict=50256						,					A						:				int=50256						,					A						:				str=50256						,					A						:				str=None						,					A						:				Dict=[220, 50256]						,					A						:				str=False						,					A						:				int=256						,					A						:				int=False						,					A						:				Optional[Any]=0.05						,					A						:				Optional[Any]=10						,					A						:				str=2						,					A						:				str=0.0						,					A						:				Any=10						,					A						:				Any=0						,					A						:				List[str]=7						,					**A						:				Union[str, Any]						,					):
             _UpperCAmelCase     :  str							      =    vocab_size
             _UpperCAmelCase     :  Union[str, Any]							      =    num_mel_bins
             _UpperCAmelCase     :  int							      =    d_model
             _UpperCAmelCase     :  Any							      =    encoder_layers
             _UpperCAmelCase     :  Union[str, Any]							      =    encoder_attention_heads
             _UpperCAmelCase     :  List[Any]							      =    decoder_layers
             _UpperCAmelCase     :  Dict							      =    decoder_attention_heads
             _UpperCAmelCase     :  List[Any]							      =    decoder_ffn_dim
             _UpperCAmelCase     :  Tuple							      =    encoder_ffn_dim
             _UpperCAmelCase     :  Tuple							      =    dropout
             _UpperCAmelCase     :  List[str]							      =    attention_dropout
             _UpperCAmelCase     :  Tuple							      =    activation_dropout
             _UpperCAmelCase     :  Dict							      =    activation_function
             _UpperCAmelCase     :  Any							      =    init_std
             _UpperCAmelCase     :  Tuple							      =    encoder_layerdrop
             _UpperCAmelCase     :  Any							      =    decoder_layerdrop
             _UpperCAmelCase     :  Any							      =    use_cache
             _UpperCAmelCase     :  Tuple							      =    encoder_layers
             _UpperCAmelCase     :  List[str]							      =    scale_embedding  # scale factor will be sqrt(d_model) if True
             _UpperCAmelCase     :  Optional[Any]							      =    max_source_positions
             _UpperCAmelCase     :  Optional[Any]							      =    max_target_positions

             # Audio Classification-specific parameters. Feel free to ignore for other classes.
             _UpperCAmelCase     :  Optional[int]							      =    classifier_proj_size
             _UpperCAmelCase     :  List[Any]							      =    use_weighted_layer_sum

             # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
             _UpperCAmelCase     :  Union[str, Any]							      =    apply_spec_augment
             _UpperCAmelCase     :  List[str]							      =    mask_time_prob
             _UpperCAmelCase     :  List[Any]							      =    mask_time_length
             _UpperCAmelCase     :  Optional[Any]							      =    mask_time_min_masks
             _UpperCAmelCase     :  Optional[Any]							      =    mask_feature_prob
             _UpperCAmelCase     :  List[str]							      =    mask_feature_length
             _UpperCAmelCase     :  str							      =    mask_feature_min_masks

             _UpperCAmelCase     :  Optional[Any]							      =    median_filter_width

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

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @property
      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    OrderedDict(
                 [
                     ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
                 ]       )
             if self.use_past:
                    _UpperCAmelCase     :  Any							      =    {0: "batch"}
             else:
                    _UpperCAmelCase     :  Tuple							      =    {0: "batch", 1: "decoder_sequence"}

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

             return common_inputs




      def      _A	(       self						:				str						,					A						:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]						,					A						:				int = -1						,					A						:				int = -1						,					A						:				bool = False						,					A						:				Optional["TensorType"] = None						,					A						:				int = 22050						,					A						:				float = 5.0						,					A						:				int = 220						,					):
             _UpperCAmelCase     :  int							      =    OrderedDict()
             _UpperCAmelCase     :  Optional[int]							      =    OnnxConfig.generate_dummy_inputs(
                 self						,					preprocessor=preprocessor.feature_extractor						,					batch_size=A						,					framework=A						,					sampling_rate=A						,					time_duration=A						,					frequency=A						,					)
             _UpperCAmelCase     :  List[str]							      =    encoder_inputs["input_features"].shape[2]
             _UpperCAmelCase     :  Optional[int]							      =    encoder_sequence_length // 2 if self.use_past else seq_length

             _UpperCAmelCase     :  Any							      =    super().generate_dummy_inputs(
                 preprocessor.tokenizer						,					A						,					A						,					A						,					A       )

             _UpperCAmelCase     :  int							      =    encoder_inputs.pop("input_features"       )
             _UpperCAmelCase     :  Any							      =    decoder_inputs.pop("decoder_input_ids"       )

             if "past_key_values" in decoder_inputs:
                    _UpperCAmelCase     :  str							      =    decoder_inputs.pop("past_key_values"       )

             return dummy_inputs





      @property
      def      _A	(       self						:				Dict       ):
             return 1E-3


'''simple docstring'''




import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Union[List[ControlNetModel], Tuple[ControlNetModel]]       ):
             super().__init__()
             _UpperCAmelCase     :  Optional[int]							      =    nn.ModuleList(A       )




      def      _A	(       self						:				Dict						,					A						:				torch.FloatTensor						,					A						:				Union[torch.Tensor, float, int]						,					A						:				torch.Tensor						,					A						:				List[torch.tensor]						,					A						:				List[float]						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[Dict[str, Any]] = None						,					A						:				bool = False						,					A						:				bool = True						,					):
             for i, (image, scale, controlnet) in enumerate(zip(A						,					A						,					self.nets       )       ):
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    controlnet(
                        A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					)

                    # merge samples
                    if i == 0:
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    down_samples, mid_sample
                    else:
                           _UpperCAmelCase     :  Optional[int]							      =    [
                               samples_prev + samples_curr
                               for samples_prev, samples_curr in zip(A						,					A       )
                           ]
                           mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample




      def      _A	(       self						:				List[str]						,					A						:				Union[str, os.PathLike]						,					A						:				bool = True						,					A						:				Callable = None						,					A						:				bool = False						,					A						:				Optional[str] = None						,					):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  str							      =    save_directory
             for controlnet in self.nets:
                    controlnet.save_pretrained(
                        A						,					is_main_process=A						,					save_function=A						,					safe_serialization=A						,					variant=A						,					)

                    idx += 1
                    _UpperCAmelCase     :  Tuple							      =    model_path_to_save + F"""_{idx}"""





      @classmethod
      def      _A	(       cls						:				int						,					A						:				Optional[Union[str, os.PathLike]]						,					**A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  int							      =    []

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             _UpperCAmelCase     :  int							      =    pretrained_model_path
             while os.path.isdir(A       ):
                    _UpperCAmelCase     :  List[str]							      =    ControlNetModel.from_pretrained(A						,					**A       )
                    controlnets.append(A       )

                    idx += 1
                    _UpperCAmelCase     :  Dict							      =    pretrained_model_path + F"""_{idx}"""

             logger.info(F"""{len(A       )} controlnets loaded from {pretrained_model_path}."""       )

             if len(A       ) == 0:
                    raise ValueError(
                        F"""No ControlNets found under {os.path.dirname(A       )}. Expected at least {pretrained_model_path + '_0'}."""       )

             return cls(A       )

