
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Tuple		       = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_UpperCAmelCase	:     List[str]		       = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							for attribute in key.split('.'   ):
														snake_case_           =					getattr(__snake_case     ,							__snake_case   )

							if weight_type is not None:
														snake_case_           =					getattr(__snake_case     ,							__snake_case   ).shape
							else:
														snake_case_           =					hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
														    F''' {value.shape} for {full_name}'''   )

							if weight_type == "weight":
														snake_case_           =					value
							elif weight_type == "weight_g":
														snake_case_           =					value
							elif weight_type == "weight_v":
														snake_case_           =					value
							elif weight_type == "bias":
														snake_case_           =					value
							else:
														snake_case_           =					value

							logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]
							snake_case_           =					fairseq_model.state_dict()

							snake_case_           =					hf_model.unispeech_sat.feature_extractor

							for name, value in fairseq_dict.items():
														snake_case_           =					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __snake_case     ,							__snake_case     ,							__snake_case     ,							__snake_case     ,							hf_model.config.feat_extract_norm == 'group'     ,							)
																					snake_case_           =					True
														else:
																					for key, mapped_key in MAPPING.items():
																												snake_case_           =					"unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
																																			if "layer_norm_for_extract" in name and (".".join(name.split('.'   )[:-1]   ) != key):
																																										# special case since naming is very similar
																																										continue
																																			snake_case_           =					True
																																			if "*" in mapped_key:
																																										snake_case_           =					name.split(__snake_case   )[0].split('.'   )[-2]
																																										snake_case_           =					mapped_key.replace('*'     ,							__snake_case   )
																																			if "weight_g" in name:
																																										snake_case_           =					"weight_g"
																																			elif "weight_v" in name:
																																										snake_case_           =					"weight_v"
																																			elif "bias" in name:
																																										snake_case_           =					"bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										snake_case_           =					"weight"
																																			else:
																																										snake_case_           =					None
																																			set_recursively(__snake_case     ,							__snake_case     ,							__snake_case     ,							__snake_case     ,							__snake_case   )
																												continue
														if not is_used:
																					unused_weights.append(__snake_case   )

							logger.warning(F'''Unused weights: {unused_weights}'''   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					full_name.split('conv_layers.'   )[-1]
							snake_case_           =					name.split('.'   )
							snake_case_           =					int(items[0]   )
							snake_case_           =					int(items[1]   )

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''   )
																					snake_case_           =					value
																					logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''   )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''   )
																					snake_case_           =					value
																					logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''   )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''   )
																					snake_case_           =					value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''   )
																					snake_case_           =					value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
							else:
														unused_weights.append(__snake_case   )





@torch.no_grad()
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None     ,							UpperCamelCase__=None     ,							UpperCamelCase__=True   ):







							'''simple docstring'''






							if config_path is not None:
														snake_case_           =					UniSpeechSatConfig.from_pretrained(__snake_case   )
							else:
														snake_case_           =					UniSpeechSatConfig()

							snake_case_           =					""

							if is_finetuned:
														snake_case_           =					UniSpeechSatForCTC(__snake_case   )
							else:
														snake_case_           =					UniSpeechSatForPreTraining(__snake_case   )

							snake_case_           =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]     ,							arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
							snake_case_           =					model[0].eval()

							recursively_load_weights(__snake_case     ,							__snake_case   )

							hf_wavavec.save_pretrained(__snake_case   )


if __name__ == "__main__":
					_UpperCAmelCase	:     Optional[Any]		       = argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					_UpperCAmelCase	:     Optional[Any]		       = parser.parse_args()
					convert_unispeech_sat_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







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

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)







@dataclass
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_if import IFPipeline
					from .pipeline_if_imgaimg import IFImgaImgPipeline
					from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
					from .pipeline_if_inpainting import IFInpaintingPipeline
					from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
					from .pipeline_if_superresolution import IFSuperResolutionPipeline
					from .safety_checker import IFSafetyChecker
					from .watermark import IFWatermarker




