
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

lowerCamelCase__     :		int        = logging.get_logger(__name__)

lowerCamelCase__     :		int        = {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
lowerCamelCase__     :		Dict        = [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
lowerCamelCase__     :		str        = [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[str]	   =      'whisper'
 __lowerCAmelCase	:  str	   =      ['past_key_values']
 __lowerCAmelCase	:  Optional[int]	   =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=5_18_65       ,	SCREAMING_SNAKE_CASE_=80       ,	SCREAMING_SNAKE_CASE_=6       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=6       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=15_36       ,	SCREAMING_SNAKE_CASE_=15_36       ,	SCREAMING_SNAKE_CASE_=0.0       ,	SCREAMING_SNAKE_CASE_=0.0       ,	SCREAMING_SNAKE_CASE_=5_02_57       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=2_56       ,	SCREAMING_SNAKE_CASE_=0.0       ,	SCREAMING_SNAKE_CASE_=0.0       ,	SCREAMING_SNAKE_CASE_=0.0       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=15_00       ,	SCREAMING_SNAKE_CASE_=4_48       ,	SCREAMING_SNAKE_CASE_=5_02_56       ,	SCREAMING_SNAKE_CASE_=5_02_56       ,	SCREAMING_SNAKE_CASE_=5_02_56       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=[2_20, 5_02_56]       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=2_56       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=0.0_5       ,	SCREAMING_SNAKE_CASE_=10       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=0.0       ,	SCREAMING_SNAKE_CASE_=10       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=7       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




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

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

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

   lowercase__	: Dict     		=   median_filter_width

   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_       ,	decoder_start_token_id=SCREAMING_SNAKE_CASE_       ,	suppress_tokens=SCREAMING_SNAKE_CASE_       ,	begin_suppress_tokens=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)



class    _snake_case (						UpperCAmelCase_				):


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




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

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

   return common_inputs


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = -1       ,	SCREAMING_SNAKE_CASE_ = -1       ,	SCREAMING_SNAKE_CASE_ = False       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 2_20_50       ,	SCREAMING_SNAKE_CASE_ = 5.0       ,	SCREAMING_SNAKE_CASE_ = 2_20       ,	):


   '''simple docstring'''




   lowercase__	: str     		=   OrderedDict()
   lowercase__	: List[Any]     		=   OnnxConfig.generate_dummy_inputs(
       self       ,	preprocessor=preprocessor.feature_extractor       ,	batch_size=SCREAMING_SNAKE_CASE_       ,	framework=SCREAMING_SNAKE_CASE_       ,	sampling_rate=SCREAMING_SNAKE_CASE_       ,	time_duration=SCREAMING_SNAKE_CASE_       ,	frequency=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: Dict     		=   encoder_inputs["""input_features"""].shape[2]
   lowercase__	: List[Any]     		=   encoder_sequence_length // 2 if self.use_past else seq_length

   lowercase__	: Optional[Any]     		=   super().generate_dummy_inputs(
       preprocessor.tokenizer       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   encoder_inputs.pop("""input_features""")
   lowercase__	: Optional[Any]     		=   decoder_inputs.pop("""decoder_input_ids""")

   if "past_key_values" in decoder_inputs:
     lowercase__	: Dict     		=   decoder_inputs.pop("""past_key_values""")

   return dummy_inputs



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return 1E-3









import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_   							=  False
lowercase_   							=  True
lowercase_   							=  False


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()

  parser.add_argument(
      '--repo_path',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

  lowercase_   							=  parser.parse_args()

  lowercase_   							=  {
      'image_size': 'sample_size',
      'num_res_blocks': 'layers_per_block',
      'block_channels': 'block_out_channels',
      'down_blocks': 'down_block_types',
      'up_blocks': 'up_block_types',
      'downscale_freq_shift': 'freq_shift',
      'resnet_num_groups': 'norm_num_groups',
      'resnet_act_fn': 'act_fn',
      'resnet_eps': 'norm_eps',
      'num_head_channels': 'attention_head_dim',
  }

  lowercase_   							=  {
      'time_steps': 'time_proj',
      'mid': 'mid_block',
      'downsample_blocks': 'down_blocks',
      'upsample_blocks': 'up_blocks',
  }

  lowercase_   							=  '' if has_file(args.repo_path, 'config.json') else 'unet'

  with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
    lowercase_   							=  reader.read()
    lowercase_   							=  json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, 'config.json'):
    lowercase_   							=  UNetaDModel(**config)
  else:
    lowercase_   							=  UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
    lowercase_   							=  class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowercase_   							=  dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowercase_   							=  config[key]
        del config[key]

    lowercase_   							=  [k.replace('UNetRes', '') for k in config['down_block_types']]
    lowercase_   							=  [k.replace('UNetRes', '') for k in config['up_block_types']]

  if do_only_weights:
    lowercase_   							=  torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

    lowercase_   							=  {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
        continue
      lowercase_   							=  False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split('.')[0] == key:
          lowercase_   							=  param_value
          lowercase_   							=  True
      if not has_changed:
        lowercase_   							=  param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))


