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()
_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    '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',
}
_UpperCAmelCase            =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			List[Any]						,__lowercase						:			List[str]						,__lowercase						:			Optional[int]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		for attribute in key.split('.'     ):
				A_		:    Dict         =				getattr(lowerCamelCase_						,lowerCamelCase_     )

		if weight_type is not None:
				A_		:    Tuple         =				getattr(lowerCamelCase_						,lowerCamelCase_     ).shape
		else:
				A_		:    Tuple         =				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":
				A_		:    List[str]         =				value
		elif weight_type == "weight_g":
				A_		:    List[str]         =				value
		elif weight_type == "weight_v":
				A_		:    List[str]         =				value
		elif weight_type == "bias":
				A_		:    Any         =				value
		else:
				A_		:    Any         =				value

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




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		A_		:    Dict         =				[]
		A_		:    Any         =				fairseq_model.state_dict()

		A_		:    Tuple         =				hf_model.feature_extractor

		# if encoder has different dim to decoder -> use proj_weight
		A_		:    Any         =				None

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

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

		return proj_weight




def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			Union[str, Any]						,__lowercase						:			Any						,__lowercase						:			Tuple						,__lowercase						:			Optional[Any]     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				emb.weight.shape
		A_		:    Dict         =				nn.Linear(lowerCamelCase_						,lowerCamelCase_						,bias=lowerCamelCase_     )
		A_		:    Dict         =				emb.weight.data
		return lin_layer




def     UpperCamelCase						(	__lowercase						:			List[str]     ):
		'''simple docstring'''




		with open(lowerCamelCase_						,'r'						,encoding='utf-8'     ) as f:
				A_		:    Optional[int]         =				f.readlines()
				A_		:    str         =				[line.split(' '     )[0] for line in lines]

		A_		:    List[Any]         =				len(lowerCamelCase_     )

		A_		:    Optional[Any]         =				{
		    '''<s>''': 0,
		    '''<pad>''': 1,
		    '''</s>''': 2,
		    '''<unk>''': 3,
		}

		vocab_dict.update(dict(zip(lowerCamelCase_						,range(4						,num_words + 4     )     )     )     )
		return vocab_dict




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			List[Any]						,__lowercase						:			int						,__lowercase						:			Optional[int]						,__lowercase						:			int						,__lowercase						:			Any						,__lowercase						:			Optional[Any]						,):
		'''simple docstring'''




		A_		:    Optional[Any]         =				WavaVecaConfig.from_pretrained(lowerCamelCase_     )
		A_		:    Any         =				SpeechaTextaConfig.from_pretrained(
		    lowerCamelCase_						,vocab_size=lowerCamelCase_						,decoder_layers=lowerCamelCase_						,do_stable_layer_norm=lowerCamelCase_     )

		A_		:    List[Any]         =				WavaVecaFeatureExtractor(
		    feature_size=1						,sampling_rate=1_60_00						,padding_value=0						,do_normalize=lowerCamelCase_						,return_attention_mask=lowerCamelCase_						,)

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

		# set weights for wav2vec2 encoder
		A_		:    Any         =				WavaVecaModel(lowerCamelCase_     )
		A_		:    List[str]         =				recursively_load_weights_wavaveca(model.encoder						,lowerCamelCase_     )

		A_		:    Any         =				SpeechaTextaForCausalLM(lowerCamelCase_     )
		A_		:    Dict         =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,strict=lowerCamelCase_     )

		# set output linear layer
		unexpected_keys.remove('embed_out'     )
		A_		:    Dict         =				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}'''     )

		A_		:    Tuple         =				SpeechEncoderDecoderModel(encoder=lowerCamelCase_						,decoder=lowerCamelCase_     )
		A_		:    List[str]         =				False

		# add projection layer
		A_		:    List[Any]         =				nn.Parameter(projection_layer.weight     )
		A_		:    Union[str, Any]         =				nn.Parameter(projection_layer.bias     )

		A_		:    Any         =				create_vocab_dict(lowerCamelCase_     )

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

		A_		:    Union[str, Any]         =				SpeechaTextaTokenizer(os.path.join(lowerCamelCase_						,'vocab.json'     )     )
		tokenizer.save_pretrained(lowerCamelCase_     )

		A_		:    Optional[Any]         =				hf_wavavec.config.to_dict()
		A_		:    List[str]         =				tokenizer.pad_token_id
		A_		:    Dict         =				tokenizer.bos_token_id
		A_		:    Optional[Any]         =				tokenizer.eos_token_id
		A_		:    Union[str, Any]         =				'''speech_to_text_2'''
		A_		:    Optional[Any]         =				'''wav2vec2'''

		A_		:    str         =				SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_     )

		hf_wavavec.save_pretrained(lowerCamelCase_     )
		feature_extractor.save_pretrained(lowerCamelCase_     )


if __name__ == "__main__":
	_UpperCAmelCase            =		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=10224, type=int, help="""Vocab size of decoder""")
	parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

	_UpperCAmelCase            =		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,
	)


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
	from .pipeline_unclip import UnCLIPPipeline
	from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
	from .text_proj import UnCLIPTextProjModel






