
import functools
import operator

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


_snake_case      :							Optional[Any]     	=    logging.get_logger(__name__)

_snake_case      :							Union[str, Any]     	=    {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[int]				   =					'''unispeech'''





						def __init__(			self							:Optional[Any]		,    lowerCAmelCase__							:Any=32		,    lowerCAmelCase__							:Any=768		,    lowerCAmelCase__							:List[Any]=12		,    lowerCAmelCase__							:Union[str, Any]=12		,    lowerCAmelCase__							:str=3072		,    lowerCAmelCase__							:int="gelu"		,    lowerCAmelCase__							:str=0.1		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:Optional[int]=0.1		,    lowerCAmelCase__							:Tuple=0.0		,    lowerCAmelCase__							:Any=0.0		,    lowerCAmelCase__							:Union[str, Any]=0.1		,    lowerCAmelCase__							:str=0.1		,    lowerCAmelCase__							:Optional[Any]=0.02		,    lowerCAmelCase__							:List[str]=1E-5		,    lowerCAmelCase__							:str="group"		,    lowerCAmelCase__							:List[str]="gelu"		,    lowerCAmelCase__							:Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)		,    lowerCAmelCase__							:Any=(5, 2, 2, 2, 2, 2, 2)		,    lowerCAmelCase__							:Tuple=(10, 3, 3, 3, 3, 2, 2)		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=128		,    lowerCAmelCase__							:str=16		,    lowerCAmelCase__							:Optional[Any]=False		,    lowerCAmelCase__							:List[str]=True		,    lowerCAmelCase__							:Union[str, Any]=0.05		,    lowerCAmelCase__							:Union[str, Any]=10		,    lowerCAmelCase__							:Tuple=2		,    lowerCAmelCase__							:Dict=0.0		,    lowerCAmelCase__							:List[Any]=10		,    lowerCAmelCase__							:Tuple=0		,    lowerCAmelCase__							:str=320		,    lowerCAmelCase__							:str=2		,    lowerCAmelCase__							:List[Any]=0.1		,    lowerCAmelCase__							:str=100		,    lowerCAmelCase__							:List[Any]=256		,    lowerCAmelCase__							:Tuple=256		,    lowerCAmelCase__							:Optional[int]=0.1		,    lowerCAmelCase__							:List[str]="mean"		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:Tuple=256		,    lowerCAmelCase__							:Optional[int]=80		,    lowerCAmelCase__							:Any=0		,    lowerCAmelCase__							:Tuple=1		,    lowerCAmelCase__							:str=2		,    lowerCAmelCase__							:Any=0.5		,    **lowerCAmelCase__							:Dict		,    )  ->Dict:
										super().__init__(**lowerCAmelCase__		,    pad_token_id=lowerCAmelCase__		,    bos_token_id=lowerCAmelCase__		,    eos_token_id=lowerCAmelCase__     )
										lowercase         =      hidden_size
										lowercase         =      feat_extract_norm
										lowercase         =      feat_extract_activation
										lowercase         =      list(lowerCAmelCase__     )
										lowercase         =      list(lowerCAmelCase__     )
										lowercase         =      list(lowerCAmelCase__     )
										lowercase         =      conv_bias
										lowercase         =      num_conv_pos_embeddings
										lowercase         =      num_conv_pos_embedding_groups
										lowercase         =      len(self.conv_dim     )
										lowercase         =      num_hidden_layers
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      num_attention_heads
										lowercase         =      hidden_dropout
										lowercase         =      attention_dropout
										lowercase         =      activation_dropout
										lowercase         =      feat_proj_dropout
										lowercase         =      final_dropout
										lowercase         =      layerdrop
										lowercase         =      layer_norm_eps
										lowercase         =      initializer_range
										lowercase         =      num_ctc_classes
										lowercase         =      vocab_size
										lowercase         =      do_stable_layer_norm
										lowercase         =      use_weighted_layer_sum
										lowercase         =      classifier_proj_size

										if (
										    (len(self.conv_stride     ) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
										    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
										):
														raise ValueError(
														    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
														    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
														    F''' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'''
														    F''' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

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

										# parameters for pretraining with codevector quantized representations
										lowercase         =      num_codevectors_per_group
										lowercase         =      num_codevector_groups
										lowercase         =      contrastive_logits_temperature
										lowercase         =      feat_quantizer_dropout
										lowercase         =      num_negatives
										lowercase         =      codevector_dim
										lowercase         =      proj_codevector_dim
										lowercase         =      diversity_loss_weight

										# ctc loss
										lowercase         =      ctc_loss_reduction
										lowercase         =      ctc_zero_infinity

										# pretraining loss
										lowercase         =      replace_prob





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Union[str, Any]:
										return functools.reduce(operator.mul		,    self.conv_stride		,    1     )


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


if __name__ == "__main__":
						_snake_case      :							Any     	=    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.")

						_snake_case      :							Tuple     	=    parser.parse_args()

						_snake_case      :							Optional[Any]     	=    {
						    "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",
						}

						_snake_case      :							Tuple     	=    {
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						_snake_case      :							Any     	=    "" 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:
												_snake_case      :							List[Any]     	=    reader.read()
												_snake_case      :							List[Any]     	=    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"):
												_snake_case      :							List[Any]     	=    UNetaDModel(**config)
						else:
												_snake_case      :							int     	=    UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												_snake_case      :							Optional[Any]     	=    class_name(**config)

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

						_snake_case      :							Any     	=    dict(model.config)

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

												_snake_case      :							List[str]     	=    [k.replace("UNetRes", "") for k in config["down_block_types"]]
												_snake_case      :							Any     	=    [k.replace("UNetRes", "") for k in config["up_block_types"]]

						if do_only_weights:
												_snake_case      :							Optional[int]     	=    torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

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

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