



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {
    """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""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			for attribute in key.split("."    ):
						A							:					List[Any]              =						getattr(_lowerCamelCase ,    _lowerCamelCase    )

			if weight_type is not None:
						A							:					str              =						getattr(_lowerCamelCase ,    _lowerCamelCase    ).shape
			else:
						A							:					Optional[int]              =						hf_pointer.shape

			assert hf_shape == value.shape, (
			    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":
						A							:					str              =						value
			elif weight_type == "weight_g":
						A							:					Dict              =						value
			elif weight_type == "weight_v":
						A							:					List[str]              =						value
			elif weight_type == "bias":
						A							:					str              =						value
			else:
						A							:					Union[str, Any]              =						value

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[Any]              =						[]
			A							:					Optional[int]              =						fairseq_model.state_dict()

			A							:					Any              =						hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						A							:					int              =						False
						if "conv_layers" in name:
									load_conv_layer(
									    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    hf_model.config.feat_extract_norm == "group" ,    )
									A							:					Optional[Any]              =						True
						else:
									for key, mapped_key in MAPPING.items():
												A							:					Dict              =						"hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

												if key in name or (key.split("w2v_model."    )[-1] == name.split("."    )[0] and not is_finetuned):
															A							:					str              =						True
															if "*" in mapped_key:
																		A							:					str              =						name.split(_lowerCamelCase    )[0].split("."    )[-2]
																		A							:					Tuple              =						mapped_key.replace("*" ,    _lowerCamelCase    )
															if "weight_g" in name:
																		A							:					List[str]              =						"weight_g"
															elif "weight_v" in name:
																		A							:					int              =						"weight_v"
															elif "weight" in name:
																		A							:					Tuple              =						"weight"
															elif "bias" in name:
																		A							:					int              =						"bias"
															else:
																		A							:					Dict              =						None
															set_recursively(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
												continue
						if not is_used:
									unused_weights.append(_lowerCamelCase    )

			logger.warning(f"""Unused weights: {unused_weights}"""    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						full_name.split("conv_layers."    )[-1]
			A							:					Union[str, Any]              =						name.split("."    )
			A							:					List[Any]              =						int(items[0]    )
			A							:					str              =						int(items[1]    )

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f"""{full_name} has size {value.shape}, but"""
									    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
									)
									A							:					Any              =						value
									logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f"""{full_name} has size {value.shape}, but"""
									    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
									)
									A							:					Optional[Any]              =						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:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
									    " found."
									)
									A							:					Optional[Any]              =						value
									logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f"""{full_name} has size {value.shape}, but"""
									    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
									)
									A							:					List[Any]              =						value
									logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
			else:
						unused_weights.append(_lowerCamelCase    )




@torch.no_grad()
def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase=None ,    _lowerCamelCase=None ,    _lowerCamelCase=True    ):
			if config_path is not None:
						A							:					List[Any]              =						HubertConfig.from_pretrained(_lowerCamelCase    )
			else:
						A							:					List[Any]              =						HubertConfig()

			if is_finetuned:
						if dict_path:
									A							:					Optional[int]              =						Dictionary.load(_lowerCamelCase    )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									A							:					Dict              =						target_dict.pad_index
									A							:					Any              =						target_dict.bos_index
									A							:					Tuple              =						target_dict.eos_index
									A							:					int              =						len(target_dict.symbols    )
									A							:					Tuple              =						os.path.join(_lowerCamelCase ,    "vocab.json"    )
									if not os.path.isdir(_lowerCamelCase    ):
												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCamelCase    )    )
												return
									os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
									with open(_lowerCamelCase ,    "w" ,    encoding="utf-8"    ) as vocab_handle:
												json.dump(target_dict.indices ,    _lowerCamelCase    )
									A							:					Optional[Any]              =						WavaVecaCTCTokenizer(
									    _lowerCamelCase ,    unk_token=target_dict.unk_word ,    pad_token=target_dict.pad_word ,    bos_token=target_dict.bos_word ,    eos_token=target_dict.eos_word ,    word_delimiter_token="|" ,    do_lower_case=_lowerCamelCase ,    )
									A							:					Dict              =						True if config.feat_extract_norm == "layer" else False
									A							:					Tuple              =						WavaVecaFeatureExtractor(
									    feature_size=1 ,    sampling_rate=1_6000 ,    padding_value=0 ,    do_normalize=_lowerCamelCase ,    return_attention_mask=_lowerCamelCase ,    )
									A							:					int              =						WavaVecaProcessor(feature_extractor=_lowerCamelCase ,    tokenizer=_lowerCamelCase    )
									processor.save_pretrained(_lowerCamelCase    )

						A							:					str              =						HubertForCTC(_lowerCamelCase    )
			else:
						A							:					int              =						HubertModel(_lowerCamelCase    )

			if is_finetuned:
						A							:					Optional[Any]              =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path] ,    arg_overrides={"data": "/".join(dict_path.split("/"    )[:-1]    )}    )
			else:
						A							:					List[Any]              =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

			A							:					Optional[Any]              =						model[0].eval()

			recursively_load_weights(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

			hf_wavavec.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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"""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_hubert_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)




import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


__SCREAMING_SNAKE_CASE           =  (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)  # pylint: disable=invalid-name




def 	UpperCAmelCase   (						):
			A							:					Union[str, Any]              =						"https://pypi.org/pypi/diffusers/json"
			A							:					List[Any]              =						json.loads(request.urlopen(_lowerCamelCase    ).read()    )["releases"].keys()
			return sorted(_lowerCamelCase ,    key=lambda _lowerCamelCase    : version.Version(_lowerCamelCase    )    )




def 	UpperCAmelCase   (						):
			# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
			if HF_MODULES_CACHE in sys.path:
						return

			sys.path.append(_lowerCamelCase    )
			os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
			A							:					List[Any]              =						Path(_lowerCamelCase    ) / "__init__.py"
			if not init_path.exists():
						init_path.touch()




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			init_hf_modules()
			A							:					Tuple              =						Path(_lowerCamelCase    ) / name
			# If the parent module does not exist yet, recursively create it.
			if not dynamic_module_path.parent.exists():
						create_dynamic_module(dynamic_module_path.parent    )
			os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
			A							:					Optional[int]              =						dynamic_module_path / "__init__.py"
			if not init_path.exists():
						init_path.touch()




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "r" ,    encoding="utf-8"    ) as f:
						A							:					Union[str, Any]              =						f.read()

			# Imports of the form `import .xxx`
			A							:					Union[str, Any]              =						re.findall("^\s*import\s+\.(\S+)\s*$" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Imports of the form `from .xxx import yyy`
			relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Unique-ify
			return list(set(_lowerCamelCase    )    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Optional[int]              =						False
			A							:					Tuple              =						[module_file]
			A							:					Optional[int]              =						[]

			# Let's recurse through all relative imports
			while not no_change:
						A							:					Optional[Any]              =						[]
						for f in files_to_check:
									new_imports.extend(get_relative_imports(_lowerCamelCase    )    )

						A							:					Optional[Any]              =						Path(_lowerCamelCase    ).parent
						A							:					List[str]              =						[str(module_path / m    ) for m in new_imports]
						A							:					Optional[Any]              =						[f for f in new_import_files if f not in all_relative_imports]
						A							:					Union[str, Any]              =						[f"""{f}.py""" for f in new_import_files]

						A							:					Tuple              =						len(_lowerCamelCase    ) == 0
						all_relative_imports.extend(_lowerCamelCase    )

			return all_relative_imports




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "r" ,    encoding="utf-8"    ) as f:
						A							:					Dict              =						f.read()

			# Imports of the form `import xxx`
			A							:					List[str]              =						re.findall("^\s*import\s+(\S+)\s*$" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Imports of the form `from xxx import yyy`
			imports += re.findall("^\s*from\s+(\S+)\s+import" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Only keep the top-level module
			A							:					Optional[int]              =						[imp.split("."    )[0] for imp in imports if not imp.startswith("."    )]

			# Unique-ify and test we got them all
			A							:					Any              =						list(set(_lowerCamelCase    )    )
			A							:					Tuple              =						[]
			for imp in imports:
						try:
									importlib.import_module(_lowerCamelCase    )
						except ImportError:
									missing_packages.append(_lowerCamelCase    )

			if len(_lowerCamelCase    ) > 0:
						raise ImportError(
						    "This modeling file requires the following packages that were not found in your environment: "
						    f"""{", ".join(_lowerCamelCase    )}. Run `pip install {" ".join(_lowerCamelCase    )}`"""    )

			return get_relative_imports(_lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					int              =						module_path.replace(os.path.sep ,    "."    )
			A							:					Optional[Any]              =						importlib.import_module(_lowerCamelCase    )

			if class_name is None:
						return find_pipeline_class(_lowerCamelCase    )
			return getattr(_lowerCamelCase ,    _lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			from ..pipelines import DiffusionPipeline

			A							:					int              =						dict(inspect.getmembers(_lowerCamelCase ,    inspect.isclass    )    )

			A							:					Union[str, Any]              =						None
			for cls_name, cls in cls_members.items():
						if (
						    cls_name != DiffusionPipeline.__name__
						    and issubclass(cls ,    _lowerCamelCase    )
						    and cls.__module__.split("."    )[0] != "diffusers"
						):
									if pipeline_class is not None:
												raise ValueError(
												    f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
												    f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
												    f""" {loaded_module}."""    )
									A							:					Any              =						cls

			return pipeline_class




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    _lowerCamelCase = False ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    ):
			A							:					List[Any]              =						str(_lowerCamelCase    )

			A							:					Any              =						os.path.join(_lowerCamelCase ,    _lowerCamelCase    )

			if os.path.isfile(_lowerCamelCase    ):
						A							:					Union[str, Any]              =						module_file_or_url
						A							:					Any              =						"local"
			elif pretrained_model_name_or_path.count("/"    ) == 0:
						A							:					Optional[Any]              =						get_diffusers_versions()
						# cut ".dev0"
						A							:					Union[str, Any]              =						"v" + ".".join(__version__.split("."    )[:3]    )

						# retrieve github version that matches
						if revision is None:
									A							:					List[Any]              =						latest_version if latest_version[1:] in available_versions else "main"
									logger.info(f"""Defaulting to latest_version: {revision}."""    )
						elif revision in available_versions:
									A							:					Optional[Any]              =						f"""v{revision}"""
						elif revision == "main":
									A							:					Dict              =						revision
						else:
									raise ValueError(
									    f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
									    f""" {", ".join(available_versions + ["main"]    )}."""    )

						# community pipeline on GitHub
						A							:					Dict              =						COMMUNITY_PIPELINES_URL.format(revision=_lowerCamelCase ,    pipeline=_lowerCamelCase    )
						try:
									A							:					Optional[int]              =						cached_download(
									    _lowerCamelCase ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    )
									A							:					Optional[Any]              =						"git"
									A							:					Any              =						pretrained_model_name_or_path + ".py"
						except EnvironmentError:
									logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""    )
									raise
			else:
						try:
									# Load from URL or cache if already cached
									A							:					Any              =						hf_hub_download(
									    _lowerCamelCase ,    _lowerCamelCase ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    )
									A							:					Optional[Any]              =						os.path.join("local" ,    "--".join(pretrained_model_name_or_path.split("/"    )    )    )
						except EnvironmentError:
									logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""    )
									raise

    # Check we have all the requirements in our environment
			A							:					List[str]              =						check_imports(_lowerCamelCase    )

			# Now we move the module inside our cached dynamic modules.
			A							:					List[str]              =						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
			create_dynamic_module(_lowerCamelCase    )
			A							:					Optional[int]              =						Path(_lowerCamelCase    ) / full_submodule
			if submodule == "local" or submodule == "git":
						# We always copy local files (we could hash the file to see if there was a change, and give them the name of
						# that hash, to only copy when there is a modification but it seems overkill for now).
						# The only reason we do the copy is to avoid putting too many folders in sys.path.
						shutil.copy(_lowerCamelCase ,    submodule_path / module_file    )
						for module_needed in modules_needed:
									A							:					int              =						f"""{module_needed}.py"""
									shutil.copy(os.path.join(_lowerCamelCase ,    _lowerCamelCase    ) ,    submodule_path / module_needed    )
			else:
						# Get the commit hash
						# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
						if isinstance(_lowerCamelCase ,    _lowerCamelCase    ):
									A							:					Optional[Any]              =						use_auth_token
						elif use_auth_token is True:
									A							:					Dict              =						HfFolder.get_token()
						else:
									A							:					Tuple              =						None

						A							:					List[str]              =						model_info(_lowerCamelCase ,    revision=_lowerCamelCase ,    token=_lowerCamelCase    ).sha

						# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
						# benefit of versioning.
						A							:					str              =						submodule_path / commit_hash
						A							:					List[str]              =						full_submodule + os.path.sep + commit_hash
						create_dynamic_module(_lowerCamelCase    )

						if not (submodule_path / module_file).exists():
									shutil.copy(_lowerCamelCase ,    submodule_path / module_file    )
						# Make sure we also have every file with relative
						for module_needed in modules_needed:
									if not (submodule_path / module_needed).exists():
												get_cached_module_file(
												    _lowerCamelCase ,    f"""{module_needed}.py""" ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    revision=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    )
			return os.path.join(_lowerCamelCase ,    _lowerCamelCase    )






def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    _lowerCamelCase = False ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    **_lowerCamelCase ,    ):
			A							:					int              =						get_cached_module_file(
			    _lowerCamelCase ,    _lowerCamelCase ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    revision=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    )
			return get_class_in_module(_lowerCamelCase ,    final_module.replace(".py" ,    ""    )    )