


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowerCAmelCase			=				{
    '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',
    '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',
}
lowerCAmelCase			=				[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		for attribute in key.split('''.'''	):
				lowercase__				= getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

		if weight_type is not None:
				lowercase__				= getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	).shape
		else:
				lowercase__				= 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":
				lowercase__				= value
		elif weight_type == "weight_g":
				lowercase__				= value
		elif weight_type == "weight_v":
				lowercase__				= value
		elif weight_type == "bias":
				lowercase__				= value
		else:
				lowercase__				= value

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


		"""simple docstring"""




		lowercase__				= []
		lowercase__				= fairseq_model.state_dict()

		lowercase__				= hf_model.feature_extractor

		# if encoder has different dim to decoder -> use proj_weight
		lowercase__				= None

		for name, value in fairseq_dict.items():
				lowercase__				= False
				if "conv_layers" in name:
						load_conv_layer(
						    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							hf_model.config.feat_extract_norm == '''group'''		,							)
						lowercase__				= True
				elif name.split('''.'''	)[0] == "proj":
						lowercase__				= fairseq_model.proj
						lowercase__				= True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
										lowercase__				= True
										if "*" in mapped_key:
												lowercase__				= name.split(SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
												lowercase__				= mapped_key.replace('''*'''		,							SCREAMING_SNAKE_CASE	)
										if "weight_g" in name:
												lowercase__				= '''weight_g'''
										elif "weight_v" in name:
												lowercase__				= '''weight_v'''
										elif "bias" in name:
												lowercase__				= '''bias'''
										elif "weight" in name:
												lowercase__				= '''weight'''
										else:
												lowercase__				= None
										set_recursively(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
								continue
				if not is_used:
						unused_weights.append(SCREAMING_SNAKE_CASE	)

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

		return proj_weight
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__	,						lowercase__				= emb.weight.shape
		lowercase__				= nn.Linear(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							bias=SCREAMING_SNAKE_CASE	)
		lowercase__				= emb.weight.data
		return lin_layer
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		with open(SCREAMING_SNAKE_CASE		,							'''r'''		,							encoding='''utf-8'''	) as f:
				lowercase__				= f.readlines()
				lowercase__				= [line.split(''' '''	)[0] for line in lines]

		lowercase__				= len(SCREAMING_SNAKE_CASE	)

		lowercase__				= {
		    '''<s>''': 0,
		    '''<pad>''': 1,
		    '''</s>''': 2,
		    '''<unk>''': 3,
		}

		vocab_dict.update(dict(zip(SCREAMING_SNAKE_CASE		,							range(4		,							num_words + 4	)	)	)	)
		return vocab_dict
@torch.no_grad()
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							):


		"""simple docstring"""




		lowercase__				= WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
		lowercase__				= SpeechaTextaConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE		,							vocab_size=SCREAMING_SNAKE_CASE		,							decoder_layers=SCREAMING_SNAKE_CASE		,							do_stable_layer_norm=SCREAMING_SNAKE_CASE	)

		lowercase__				= WavaVecaFeatureExtractor(
		    feature_size=1		,							sampling_rate=1_60_00		,							padding_value=0		,							do_normalize=SCREAMING_SNAKE_CASE		,							return_attention_mask=SCREAMING_SNAKE_CASE		,							)

		lowercase__	,						lowercase__	,						lowercase__				= fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]		,							arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
		lowercase__				= model[0].eval()

		# set weights for wav2vec2 encoder
		lowercase__				= WavaVecaModel(SCREAMING_SNAKE_CASE	)
		lowercase__				= recursively_load_weights_wavaveca(model.encoder		,							SCREAMING_SNAKE_CASE	)

		lowercase__				= SpeechaTextaForCausalLM(SCREAMING_SNAKE_CASE	)
		lowercase__	,						lowercase__				= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,							strict=SCREAMING_SNAKE_CASE	)

		# set output linear layer
		unexpected_keys.remove('''embed_out'''	)
		lowercase__				= nn.Parameter(model.decoder.embed_out.detach()	)

		# layer norm is init to identity matrix so leaving it is fine
		logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'	)
		logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'	)

		lowercase__				= SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE		,							decoder=SCREAMING_SNAKE_CASE	)
		lowercase__				= False

		# add projection layer
		lowercase__				= nn.Parameter(projection_layer.weight	)
		lowercase__				= nn.Parameter(projection_layer.bias	)

		lowercase__				= create_vocab_dict(SCREAMING_SNAKE_CASE	)

		with open(os.path.join(SCREAMING_SNAKE_CASE		,							'''vocab.json'''	)		,							'''w'''	) as fp:
				json.dump(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

		lowercase__				= SpeechaTextaTokenizer(os.path.join(SCREAMING_SNAKE_CASE		,							'''vocab.json'''	)	)
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)

		lowercase__				= hf_wavavec.config.to_dict()
		lowercase__				= tokenizer.pad_token_id
		lowercase__				= tokenizer.bos_token_id
		lowercase__				= tokenizer.eos_token_id
		lowercase__				= '''speech_to_text_2'''
		lowercase__				= '''wav2vec2'''

		lowercase__				= SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE	)

		hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE	)
		feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
			lowerCAmelCase			=				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(
			    '--encoder_config_path',
			    default='facebook/wav2vec2-large-lv60',
			    type=str,
			    help='Path to hf encoder wav2vec2 checkpoint config',
			)
			parser.add_argument(
			    '--decoder_config_path',
			    default='facebook/s2t-small-mustc-en-fr-st',
			    type=str,
			    help='Path to hf decoder s2t checkpoint config',
			)
			parser.add_argument('--vocab_size', default=1_0224, type=int, help='Vocab size of decoder')
			parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

			lowerCAmelCase			=				parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    vocab_size=args.vocab_size,
			    num_decoder_layers=args.num_decoder_layers,
			)





"""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

_UpperCamelCase		     =    logging.get_logger(__name__)






@add_end_docstrings(__magic_name__       )
class 			__a					(						__magic_name__       ):





       """simple docstring"""


       def __init__(   self			,					*snake_case			,					**snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					**snake_case   ):





        """simple docstring"""

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






       def __call__(   self			,					snake_case			,					snake_case = None			,					**snake_case   ):





        """simple docstring"""

        if isinstance(snake_case			,					(Image.Image, str)   ) and isinstance(snake_case			,					snake_case   ):
         lowerCAmelCase__   :				int 			=				{"image": image, "question": question}
        else:
         lowerCAmelCase__   :				Optional[int] 			=				image
        lowerCAmelCase__   :				Optional[Any] 			=				super().__call__(snake_case			,					**snake_case   )
        return results






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=False			,					snake_case=False   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				load_image(inputs["image"]   )
        lowerCAmelCase__   :				str 			=				self.tokenizer(
            inputs["question"]			,					return_tensors=self.framework			,					padding=snake_case			,					truncation=snake_case   )
        lowerCAmelCase__   :				int 			=				self.image_processor(images=snake_case			,					return_tensors=self.framework   )
        model_inputs.update(snake_case   )
        return model_inputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.model(**snake_case   )
        return model_outputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=5   ):





        """simple docstring"""

        if top_k > self.model.config.num_labels:
         lowerCAmelCase__   :				List[Any] 			=				self.model.config.num_labels

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

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






