


'''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()
a_						:       Any      			=				logging.get_logger(__name__)

a_						:       Tuple      			=				{
    """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""",
}
a_						:       List[str]      			=				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[int]	):
	for attribute in key.split("."	):
		lowerCamelCase_  				=						getattr(UpperCAmelCase_      ,       UpperCAmelCase_	)

	if weight_type is not None:
		lowerCamelCase_  				=						getattr(UpperCAmelCase_      ,       UpperCAmelCase_	).shape
	else:
		lowerCamelCase_  				=						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":
		lowerCamelCase_  				=						value
	elif weight_type == "weight_g":
		lowerCamelCase_  				=						value
	elif weight_type == "weight_v":
		lowerCamelCase_  				=						value
	elif weight_type == "bias":
		lowerCamelCase_  				=						value
	elif weight_type == "running_mean":
		lowerCamelCase_  				=						value
	elif weight_type == "running_var":
		lowerCamelCase_  				=						value
	elif weight_type == "num_batches_tracked":
		lowerCamelCase_  				=						value
	elif weight_type == "inv_freq":
		lowerCamelCase_  				=						value
	else:
		lowerCamelCase_  				=						value

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





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict	):
	lowerCamelCase_  				=						[]
	lowerCamelCase_  				=						fairseq_model.state_dict()

	lowerCamelCase_  				=						hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		lowerCamelCase_  				=						False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       hf_model.config.feat_extract_norm == "group"      ,       )
			lowerCamelCase_  				=						True
		else:
			for key, mapped_key in MAPPING.items():
				lowerCamelCase_  				=						"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]:
					lowerCamelCase_  				=						True
					if "*" in mapped_key:
						lowerCamelCase_  				=						name.split(UpperCAmelCase_	)[0].split("."	)[-2]
						lowerCamelCase_  				=						mapped_key.replace("*"      ,       UpperCAmelCase_	)
					if "pos_bias_u" in name:
						lowerCamelCase_  				=						None
					elif "pos_bias_v" in name:
						lowerCamelCase_  				=						None
					elif "weight_g" in name:
						lowerCamelCase_  				=						"weight_g"
					elif "weight_v" in name:
						lowerCamelCase_  				=						"weight_v"
					elif "bias" in name:
						lowerCamelCase_  				=						"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowerCamelCase_  				=						"weight"
					elif "running_mean" in name:
						lowerCamelCase_  				=						"running_mean"
					elif "inv_freq" in name:
						lowerCamelCase_  				=						"inv_freq"
					elif "running_var" in name:
						lowerCamelCase_  				=						"running_var"
					elif "num_batches_tracked" in name:
						lowerCamelCase_  				=						"num_batches_tracked"
					else:
						lowerCamelCase_  				=						None
					set_recursively(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_	)

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





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





@torch.no_grad()
def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		str=None      ,       UpperCAmelCase_		:		Any=None      ,       UpperCAmelCase_		:		Any=True	):
	if config_path is not None:
		lowerCamelCase_  				=						WavaVecaConformerConfig.from_pretrained(UpperCAmelCase_      ,       hidden_act="swish"	)
	else:
		lowerCamelCase_  				=						WavaVecaConformerConfig()

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

	if is_finetuned:
		if dict_path:
			lowerCamelCase_  				=						Dictionary.load(UpperCAmelCase_	)

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

			# fairseq has the <pad> and <s> switched
			lowerCamelCase_  				=						0
			lowerCamelCase_  				=						1
			with open(UpperCAmelCase_      ,       "w"      ,       encoding="utf-8"	) as vocab_handle:
				json.dump(UpperCAmelCase_      ,       UpperCAmelCase_	)
			lowerCamelCase_  				=						WavaVecaCTCTokenizer(
			    UpperCAmelCase_      ,       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=UpperCAmelCase_      ,       )
			lowerCamelCase_  				=						True if config.feat_extract_norm == "layer" else False
			lowerCamelCase_  				=						WavaVecaFeatureExtractor(
			    feature_size=1      ,       sampling_rate=16000      ,       padding_value=0      ,       do_normalize=UpperCAmelCase_      ,       return_attention_mask=UpperCAmelCase_      ,       )
			lowerCamelCase_  				=						WavaVecaProcessor(feature_extractor=UpperCAmelCase_      ,       tokenizer=UpperCAmelCase_	)
			processor.save_pretrained(UpperCAmelCase_	)

		lowerCamelCase_  				=						WavaVecaConformerForCTC(UpperCAmelCase_	)
	else:
		lowerCamelCase_  				=						WavaVecaConformerForPreTraining(UpperCAmelCase_	)

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

		lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,       task=UpperCAmelCase_	)

	lowerCamelCase_  				=						model[0].eval()

	recursively_load_weights(UpperCAmelCase_      ,       UpperCAmelCase_      ,       not is_finetuned	)

	hf_wavavec.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
					a_						:       int      			=				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_						:       Optional[int]      			=				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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_						:       List[Any]      			=				{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       int      			=				["""OwlViTFeatureExtractor"""]
     a_						:       Any      			=				["""OwlViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       Tuple      			=				[
         """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OwlViTModel""",
         """OwlViTPreTrainedModel""",
         """OwlViTTextModel""",
         """OwlViTVisionModel""",
         """OwlViTForObjectDetection""",
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

     a_						:       Tuple      			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



