





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_SCREAMING_SNAKE_CASE							=						{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ,       __a      ):



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

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

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


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ):
	snake_case_						:	List[Any]      =		[]
	snake_case_						:	Optional[int]      =		fairseq_model.state_dict()

	snake_case_						:	Optional[Any]      =		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case_						:	Tuple      =		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_						:	str      =		True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case_						:	str      =		'''sew.''' + 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]:
					snake_case_						:	Union[str, Any]      =		True
					if "*" in mapped_key:
						snake_case_						:	List[Any]      =		name.split(snake_case__      )[0].split('.'      )[-2]
						snake_case_						:	Optional[int]      =		mapped_key.replace('*'      ,       snake_case__      )
					if "weight_g" in name:
						snake_case_						:	List[str]      =		'''weight_g'''
					elif "weight_v" in name:
						snake_case_						:	Union[str, Any]      =		'''weight_v'''
					elif "weight" in name:
						snake_case_						:	Dict      =		'''weight'''
					elif "bias" in name:
						snake_case_						:	Dict      =		'''bias'''
					else:
						snake_case_						:	Optional[Any]      =		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 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ,       __a      ):
	snake_case_						:	Union[str, Any]      =		full_name.split('conv_layers.'      )[-1]
	snake_case_						:	List[Any]      =		name.split('.'      )
	snake_case_						:	Union[str, Any]      =		int(items[0]      )
	snake_case_						:	Any      =		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."""
			)
			snake_case_						:	Optional[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."""
			)
			snake_case_						:	Optional[int]      =		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."
			)
			snake_case_						:	int      =		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."""
			)
			snake_case_						:	int      =		value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
	else:
		unused_weights.append(snake_case__      )


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ):
	snake_case_						:	Dict      =		SEWConfig()
	if is_finetuned:
		snake_case_						:	List[Any]      =		model.wav_encoder.wav_model.cfg
	else:
		snake_case_						:	str      =		model.cfg

	snake_case_						:	int      =		fs_config.conv_bias
	snake_case_						:	Optional[int]      =		eval(fs_config.conv_feature_layers      )
	snake_case_						:	Union[str, Any]      =		[x[0] for x in conv_layers]
	snake_case_						:	Dict      =		[x[1] for x in conv_layers]
	snake_case_						:	Dict      =		[x[2] for x in conv_layers]
	snake_case_						:	str      =		'''gelu'''
	snake_case_						:	Tuple      =		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
	snake_case_						:	Optional[Any]      =		0.0
	snake_case_						:	List[str]      =		fs_config.activation_fn.name
	snake_case_						:	List[str]      =		fs_config.encoder_embed_dim
	snake_case_						:	Optional[Any]      =		0.02
	snake_case_						:	Any      =		fs_config.encoder_ffn_embed_dim
	snake_case_						:	str      =		1E-5
	snake_case_						:	str      =		fs_config.encoder_layerdrop
	snake_case_						:	List[str]      =		fs_config.encoder_attention_heads
	snake_case_						:	List[str]      =		fs_config.conv_pos_groups
	snake_case_						:	str      =		fs_config.conv_pos
	snake_case_						:	Union[str, Any]      =		len(snake_case__      )
	snake_case_						:	str      =		fs_config.encoder_layers
	snake_case_						:	List[Any]      =		fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		snake_case_						:	Optional[int]      =		model.cfg
		snake_case_						:	Union[str, Any]      =		fs_config.final_dropout
		snake_case_						:	Optional[int]      =		fs_config.layerdrop
	snake_case_						:	str      =		fs_config.activation_dropout
	snake_case_						:	int      =		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	snake_case_						:	Dict      =		fs_config.attention_dropout
	snake_case_						:	List[str]      =		fs_config.dropout_input
	snake_case_						:	Union[str, Any]      =		fs_config.dropout
	snake_case_						:	int      =		fs_config.mask_channel_length
	snake_case_						:	str      =		fs_config.mask_channel_prob
	snake_case_						:	List[str]      =		fs_config.mask_length
	snake_case_						:	Tuple      =		fs_config.mask_prob

	snake_case_						:	Optional[int]      =		'''Wav2Vec2FeatureExtractor'''
	snake_case_						:	Union[str, Any]      =		'''Wav2Vec2CTCTokenizer'''

	return config


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a=None      ,       __a=None      ,       __a=True      ):



	if is_finetuned:
		snake_case_						:	Any      =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]      ,       arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
	else:
		snake_case_						:	int      =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

	if config_path is not None:
		snake_case_						:	List[str]      =		SEWConfig.from_pretrained(snake_case__      )
	else:
		snake_case_						:	Dict      =		convert_config(model[0]      ,       snake_case__      )
	snake_case_						:	str      =		model[0].eval()

	snake_case_						:	Union[str, Any]      =		True if config.feat_extract_norm == '''layer''' else False
	snake_case_						:	Optional[int]      =		WavaVecaFeatureExtractor(
	    feature_size=1      ,       sampling_rate=1_60_00      ,       padding_value=0      ,       do_normalize=snake_case__      ,       return_attention_mask=snake_case__      ,       )

	if is_finetuned:
		if dict_path:
			snake_case_						:	Optional[Any]      =		Dictionary.load(snake_case__      )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			snake_case_						:	Optional[Any]      =		target_dict.pad_index
			snake_case_						:	Optional[Any]      =		target_dict.bos_index
			snake_case_						:	Union[str, Any]      =		target_dict.pad_index
			snake_case_						:	Union[str, Any]      =		target_dict.bos_index
			snake_case_						:	int      =		target_dict.eos_index
			snake_case_						:	Tuple      =		len(target_dict.symbols      )
			snake_case_						:	List[str]      =		os.path.join(snake_case__      ,       'vocab.json'      )
			if not os.path.isdir(snake_case__      ):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case__      )      )
				return
			os.makedirs(snake_case__      ,       exist_ok=snake_case__      )
			with open(snake_case__      ,       'w'      ,       encoding='utf-8'      ) as vocab_handle:
				json.dump(target_dict.indices      ,       snake_case__      )
			snake_case_						:	str      =		WavaVecaCTCTokenizer(
			    snake_case__      ,       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=snake_case__      ,       )
			snake_case_						:	List[str]      =		WavaVecaProcessor(feature_extractor=snake_case__      ,       tokenizer=snake_case__      )
			processor.save_pretrained(snake_case__      )

		snake_case_						:	Tuple      =		SEWForCTC(snake_case__      )
	else:
		snake_case_						:	Dict      =		SEWModel(snake_case__      )
		feature_extractor.save_pretrained(snake_case__      )

	recursively_load_weights(snake_case__      ,       snake_case__      ,       snake_case__      )

	hf_model.save_pretrained(snake_case__      )


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(
						    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
						)
						_SCREAMING_SNAKE_CASE							=						parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)



'''simple docstring'''




from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

lowercase			:				str    		=			logging.get_logger(__name__)





@add_end_docstrings(__snake_case						)
class  A				(							__snake_case						):





     def __init__(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         self.check_model_type(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A,  A       :   Dict					=						{}, {}
         if padding is not None:
             A       :   List[str]					=						padding
         if truncation is not None:
             A       :   Dict					=						truncation
         if top_k is not None:
             A       :   Optional[Any]					=						top_k
         return preprocess_params, {}, postprocess_params





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         if isinstance(SCREAMING_SNAKE_CASE						,			(Image.Image, str)	) and isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   int					=						{'''image''': image, '''question''': question}
         else:
             A       :   Any					=						image
         A       :   Any					=						super().__call__(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         return results





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False	) ->				Any:






         """simple docstring"""






         A       :   Union[str, Any]					=						load_image(inputs['''image''']	)
         A       :   Optional[Any]					=						self.tokenizer(
             inputs['''question''']						,			return_tensors=self.framework						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						self.image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors=self.framework	)
         model_inputs.update(SCREAMING_SNAKE_CASE	)
         return model_inputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   List[Any]					=						self.model(**SCREAMING_SNAKE_CASE	)
         return model_outputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=5	) ->				int:






         """simple docstring"""






         if top_k > self.model.config.num_labels:
             A       :   Dict					=						self.model.config.num_labels

         if self.framework == "pt":
             A       :   Optional[int]					=						model_outputs.logits.sigmoid()[0]
             A,  A       :   int					=						probs.topk(SCREAMING_SNAKE_CASE	)
         else:
             raise ValueError(F'Unsupported framework: {self.framework}'	)

         A       :   int					=						scores.tolist()
         A       :   List[str]					=						ids.tolist()
         return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)]




