
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()
lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    "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",
}
lowercase   =	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Optional[Any]      ,					UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Union[str, Any]							)	->     Any:





							for attribute in key.split("""."""							):
														lowerCamelCase_					=       getattr(snake_case__      ,					snake_case__							)

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

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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Tuple							)	->     List[Any]:
							lowerCamelCase_					=       []
							lowerCamelCase_					=       fairseq_model.state_dict()

							lowerCamelCase_					=       hf_model.feature_extractor

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

							for name, value in fairseq_dict.items():
														lowerCamelCase_					=       False
														if "conv_layers" in name:
																					load_conv_layer(
																					    snake_case__      ,					snake_case__      ,					snake_case__      ,					snake_case__      ,					hf_model.config.feat_extract_norm == """group"""      ,					)
																					lowerCamelCase_					=       True
														elif name.split("""."""							)[0] == "proj":
																					lowerCamelCase_					=       fairseq_model.proj
																					lowerCamelCase_					=       True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
																																			lowerCamelCase_					=       True
																																			if "*" in mapped_key:
																																										lowerCamelCase_					=       name.split(snake_case__							)[0].split("""."""							)[-2]
																																										lowerCamelCase_					=       mapped_key.replace("""*"""      ,					snake_case__							)
																																			if "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:
																																										lowerCamelCase_					=       """weight"""
																																			else:
																																										lowerCamelCase_					=       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}'''							)

							return proj_weight



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Dict      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   List[str]							)	->     List[Any]:
							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:
																					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.'''
																					)
																					lowerCamelCase_					=       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.'''
																					)
																					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:
																					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."
																					)
																					lowerCamelCase_					=       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.'''
																					)
																					lowerCamelCase_					=       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   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     str:
							lowerCamelCase_	, lowerCamelCase_					=       emb.weight.shape
							lowerCamelCase_					=       nn.Linear(snake_case__      ,					snake_case__      ,					bias=snake_case__							)
							lowerCamelCase_					=       emb.weight.data
							return lin_layer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     Any:





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

							lowerCamelCase_					=       len(snake_case__							)

							lowerCamelCase_					=       {
							    """<s>""": 0,
							    """<pad>""": 1,
							    """</s>""": 2,
							    """<unk>""": 3,
							}

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



@torch.no_grad()
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[Any]      ,					UpperCAmelCase__					:   Dict      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   str      ,					)	->     Any:
							lowerCamelCase_					=       WavaVecaConfig.from_pretrained(snake_case__							)
							lowerCamelCase_					=       SpeechaTextaConfig.from_pretrained(
							    snake_case__      ,					vocab_size=snake_case__      ,					decoder_layers=snake_case__      ,					do_stable_layer_norm=snake_case__							)

							lowerCamelCase_					=       WavaVecaFeatureExtractor(
							    feature_size=1      ,					sampling_rate=1_6_0_0_0      ,					padding_value=0      ,					do_normalize=snake_case__      ,					return_attention_mask=snake_case__      ,					)

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

							# set weights for wav2vec2 encoder
							lowerCamelCase_					=       WavaVecaModel(snake_case__							)
							lowerCamelCase_					=       recursively_load_weights_wavaveca(model.encoder      ,					snake_case__							)

							lowerCamelCase_					=       SpeechaTextaForCausalLM(snake_case__							)
							lowerCamelCase_	, lowerCamelCase_					=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,					strict=snake_case__							)

							# set output linear layer
							unexpected_keys.remove("""embed_out"""							)
							lowerCamelCase_					=       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}'''							)

							lowerCamelCase_					=       SpeechEncoderDecoderModel(encoder=snake_case__      ,					decoder=snake_case__							)
							lowerCamelCase_					=       False

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

							lowerCamelCase_					=       create_vocab_dict(snake_case__							)

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

							lowerCamelCase_					=       SpeechaTextaTokenizer(os.path.join(snake_case__      ,					"""vocab.json"""							)							)
							tokenizer.save_pretrained(snake_case__							)

							lowerCamelCase_					=       hf_wavavec.config.to_dict()
							lowerCamelCase_					=       tokenizer.pad_token_id
							lowerCamelCase_					=       tokenizer.bos_token_id
							lowerCamelCase_					=       tokenizer.eos_token_id
							lowerCamelCase_					=       """speech_to_text_2"""
							lowerCamelCase_					=       """wav2vec2"""

							lowerCamelCase_					=       SpeechEncoderDecoderConfig.from_dict(snake_case__							)

							hf_wavavec.save_pretrained(snake_case__							)
							feature_extractor.save_pretrained(snake_case__							)


if __name__ == "__main__":
		lowercase   =	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_0_2_2_4, type=int, help='''Vocab size of decoder''')
		parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

		lowercase   =	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 dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  attention_head_dim
						lowerCAmelCase__		  =  num_attention_heads * attention_head_dim

						lowerCAmelCase__		  =  in_channels

						lowerCAmelCase__		  =  torch.nn.GroupNorm(num_groups=a_    ,num_channels=a_    ,eps=1e-6    ,affine=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)

						# 3. Define transformers blocks
						lowerCAmelCase__		  =  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            a_    ,a_    ,a_    ,dropout=a_    ,cross_attention_dim=a_    ,activation_fn=a_    ,attention_bias=a_    ,double_self_attention=a_    ,norm_elementwise_affine=a_    ,)
						        for d in range(a_						)
						    ]						)

						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  hidden_states.shape
						lowerCAmelCase__		  =  batch_frames // num_frames

						lowerCAmelCase__		  =  hidden_states

						lowerCAmelCase__		  =  hidden_states[None, :].reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,2    ,1    ,3    ,4						)

						lowerCAmelCase__		  =  self.norm(a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,3    ,4    ,2    ,1						).reshape(batch_size * height * width    ,a_    ,a_						)

						lowerCAmelCase__		  =  self.proj_in(a_						)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__		  =  block(
										    a_    ,encoder_hidden_states=a_    ,timestep=a_    ,cross_attention_kwargs=a_    ,class_labels=a_    ,)

						# 3. Output
						lowerCAmelCase__		  =  self.proj_out(a_						)
						lowerCAmelCase__		  =  (
						    hidden_states[None, None, :]
						    .reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						    .permute(0    ,3    ,4    ,1    ,2						)
						    .contiguous()
						)
						lowerCAmelCase__		  =  hidden_states.reshape(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=a_						)
