
"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    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.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """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""",
    """mask_emb""": """masked_spec_embed""",
}
_SCREAMING_SNAKE_CASE	       =      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							for attribute in key.split("." ):
														__snake_case     			=       getattr(lowercase__							,    lowercase__ )

							if weight_type is not None:
														__snake_case     			=       getattr(lowercase__							,    lowercase__ ).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
							elif weight_type == "running_mean":
														__snake_case     			=       value
							elif weight_type == "running_var":
														__snake_case     			=       value
							elif weight_type == "num_batches_tracked":
														__snake_case     			=       value
							elif weight_type == "inv_freq":
														__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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       []
							__snake_case     			=       fairseq_model.state_dict()

							__snake_case     			=       hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														__snake_case     			=       False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    hf_model.config.feat_extract_norm == "group"							,    )
																					__snake_case     			=       True
														else:
																					for key, mapped_key in MAPPING.items():
																												__snake_case     			=       "wav2vec2_conformer." + 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]:
																																			__snake_case     			=       True
																																			if "*" in mapped_key:
																																										__snake_case     			=       name.split(lowercase__ )[0].split("." )[-2]
																																										__snake_case     			=       mapped_key.replace("*"							,    lowercase__ )
																																			if "pos_bias_u" in name:
																																										__snake_case     			=       None
																																			elif "pos_bias_v" in name:
																																										__snake_case     			=       None
																																			elif "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"
																																			elif "running_mean" in name:
																																										__snake_case     			=       "running_mean"
																																			elif "inv_freq" in name:
																																										__snake_case     			=       "inv_freq"
																																			elif "running_var" in name:
																																										__snake_case     			=       "running_var"
																																			elif "num_batches_tracked" in name:
																																										__snake_case     			=       "num_batches_tracked"
																																			else:
																																										__snake_case     			=       None
																																			set_recursively(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ )
																												continue
														if not is_used:
																					unused_weights.append(lowercase__ )

							logger.warning(F'''Unused weights: {unused_weights}''' )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""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.conv_layers[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.conv_layers[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(lowercase__ )
@torch.no_grad()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=True )  ->     Optional[Any]:







							"""simple docstring"""


							if config_path is not None:
														__snake_case     			=       WavaVecaConformerConfig.from_pretrained(lowercase__							,    hidden_act="swish" )
							else:
														__snake_case     			=       WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														__snake_case     			=       "rotary"

							if is_finetuned:
														if dict_path:
																					__snake_case     			=       Dictionary.load(lowercase__ )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					__snake_case     			=       target_dict.pad_index
																					__snake_case     			=       target_dict.bos_index
																					__snake_case     			=       target_dict.eos_index
																					__snake_case     			=       len(target_dict.symbols )
																					__snake_case     			=       os.path.join(lowercase__							,    "vocab.json" )
																					if not os.path.isdir(lowercase__ ):
																												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowercase__ ) )
																												return
																					os.makedirs(lowercase__							,    exist_ok=lowercase__ )
																					__snake_case     			=       target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					__snake_case     			=       0
																					__snake_case     			=       1
																					with open(lowercase__							,    "w"							,    encoding="utf-8" ) as vocab_handle:
																												json.dump(lowercase__							,    lowercase__ )
																					__snake_case     			=       WavaVecaCTCTokenizer(
																					    lowercase__							,    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=lowercase__							,    )
																					__snake_case     			=       True if config.feat_extract_norm == "layer" else False
																					__snake_case     			=       WavaVecaFeatureExtractor(
																					    feature_size=1							,    sampling_rate=1_60_00							,    padding_value=0							,    do_normalize=lowercase__							,    return_attention_mask=lowercase__							,    )
																					__snake_case     			=       WavaVecaProcessor(feature_extractor=lowercase__							,    tokenizer=lowercase__ )
																					processor.save_pretrained(lowercase__ )

														__snake_case     			=       WavaVecaConformerForCTC(lowercase__ )
							else:
														__snake_case     			=       WavaVecaConformerForPreTraining(lowercase__ )

							if is_finetuned:
														__snake_case			,   __snake_case			,   __snake_case     			=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]							,    arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
							else:
														__snake_case     			=       argparse.Namespace(task="audio_pretraining" )
														__snake_case     			=       fairseq.tasks.setup_task(lowercase__ )

														__snake_case			,   __snake_case			,   __snake_case     			=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,    task=lowercase__ )

							__snake_case     			=       model[0].eval()

							recursively_load_weights(lowercase__							,    lowercase__							,    not is_finetuned )

							hf_wavavec.save_pretrained(lowercase__ )


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_wavaveca_conformer_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_tf_available,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTFeatureExtractor"""]
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MobileViTForImageClassification""",
		    """MobileViTForSemanticSegmentation""",
		    """MobileViTModel""",
		    """MobileViTPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFMobileViTForImageClassification""",
		    """TFMobileViTForSemanticSegmentation""",
		    """TFMobileViTModel""",
		    """TFMobileViTPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
