

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

a_     =			{
    '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',
}
a_     =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]

def        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a							,	_a):
	for attribute in key.split("."):
		SCREAMING_SNAKE_CASE      :					str    =				getattr(_a							,	_a)

	if weight_type is not None:
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				getattr(_a							,	_a).shape
	else:
		SCREAMING_SNAKE_CASE      :					List[Any]    =				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":
		SCREAMING_SNAKE_CASE      :					str    =				value
	elif weight_type == "weight_g":
		SCREAMING_SNAKE_CASE      :					List[Any]    =				value
	elif weight_type == "weight_v":
		SCREAMING_SNAKE_CASE      :					List[str]    =				value
	elif weight_type == "bias":
		SCREAMING_SNAKE_CASE      :					List[Any]    =				value
	else:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				value

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

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				[]
	SCREAMING_SNAKE_CASE      :					List[str]    =				fairseq_model.state_dict()

	SCREAMING_SNAKE_CASE      :					str    =				hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		SCREAMING_SNAKE_CASE      :					str    =				False
		if "conv_layers" in name:
			load_conv_layer(
			    _a							,	_a							,	_a							,	_a							,	hf_model.config.feat_extract_norm == "group"							,	)
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				True
		else:
			for key, mapped_key in MAPPING.items():
				SCREAMING_SNAKE_CASE      :					Any    =				"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
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				True
					if "*" in mapped_key:
						SCREAMING_SNAKE_CASE      :					str    =				name.split(_a)[0].split(".")[-2]
						SCREAMING_SNAKE_CASE      :					int    =				mapped_key.replace("*"							,	_a)
					if "weight_g" in name:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"weight_g"
					elif "weight_v" in name:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"weight_v"
					elif "bias" in name:
						SCREAMING_SNAKE_CASE      :					Tuple    =				"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				"weight"
					else:
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				None
					set_recursively(_a							,	_a							,	_a							,	_a							,	_a)
				continue
		if not is_used:
			unused_weights.append(_a)

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

def        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				full_name.split("conv_layers.")[-1]
	SCREAMING_SNAKE_CASE      :					List[Any]    =				name.split(".")
	SCREAMING_SNAKE_CASE      :					int    =				int(items[0])
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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.")
			SCREAMING_SNAKE_CASE      :					Any    =				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.")
			SCREAMING_SNAKE_CASE      :					List[str]    =				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.")
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				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.")
			SCREAMING_SNAKE_CASE      :					List[str]    =				value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
	else:
		unused_weights.append(_a)

@torch.no_grad()
def        lowerCamelCase__       (    _a							,	_a							,	_a=None							,	_a=None							,	_a=True):
	if config_path is not None:
		SCREAMING_SNAKE_CASE      :					str    =				UniSpeechSatConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE      :					int    =				UniSpeechSatConfig()

	SCREAMING_SNAKE_CASE      :					int    =				""

	if is_finetuned:
		SCREAMING_SNAKE_CASE      :					Any    =				UniSpeechSatForCTC(_a)
	else:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				UniSpeechSatForPreTraining(_a)

	SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					int    =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]							,	arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
	SCREAMING_SNAKE_CASE      :					List[str]    =				model[0].eval()

	recursively_load_weights(_a							,	_a)

	hf_wavavec.save_pretrained(_a)


if __name__ == "__main__":
			a_     =			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'
			)
			a_     =			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
			)




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
