


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()
_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "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",
}
_SCREAMING_SNAKE_CASE      :			str      				=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



					"""simple docstring"""






					for attribute in key.split('''.'''    ):
										snake_case					=    getattr(UpperCamelCase_							,UpperCamelCase_    )

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

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



					"""simple docstring"""






					snake_case					=    []
					snake_case					=    fairseq_model.state_dict()

					snake_case					=    hf_model.feature_extractor

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

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

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

					return proj_weight
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



					"""simple docstring"""






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



					"""simple docstring"""






					snake_case			,  snake_case					=    emb.weight.shape
					snake_case					=    nn.Linear(UpperCamelCase_							,UpperCamelCase_							,bias=UpperCamelCase_    )
					snake_case					=    emb.weight.data
					return lin_layer
def       UpperCAmelCase__						(UpperCamelCase_    ):



					"""simple docstring"""






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

					snake_case					=    len(UpperCamelCase_    )

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

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



					"""simple docstring"""






					snake_case					=    WavaVecaConfig.from_pretrained(UpperCamelCase_    )
					snake_case					=    SpeechaTextaConfig.from_pretrained(
					    UpperCamelCase_							,vocab_size=UpperCamelCase_							,decoder_layers=UpperCamelCase_							,do_stable_layer_norm=UpperCamelCase_    )

					snake_case					=    WavaVecaFeatureExtractor(
					    feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)

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

					# set weights for wav2vec2 encoder
					snake_case					=    WavaVecaModel(UpperCamelCase_    )
					snake_case					=    recursively_load_weights_wavaveca(model.encoder							,UpperCamelCase_    )

					snake_case					=    SpeechaTextaForCausalLM(UpperCamelCase_    )
					snake_case			,  snake_case					=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,strict=UpperCamelCase_    )

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

					snake_case					=    SpeechEncoderDecoderModel(encoder=UpperCamelCase_							,decoder=UpperCamelCase_    )
					snake_case					=    False

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

					snake_case					=    create_vocab_dict(UpperCamelCase_    )

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

					snake_case					=    SpeechaTextaTokenizer(os.path.join(UpperCamelCase_							,'''vocab.json'''    )    )
					tokenizer.save_pretrained(UpperCamelCase_    )

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

					snake_case					=    SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_    )

					hf_wavavec.save_pretrained(UpperCamelCase_    )
					feature_extractor.save_pretrained(UpperCamelCase_    )


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE      :			Dict      				=   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_02_24, type=int, help="Vocab size of decoder")
	parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

	_SCREAMING_SNAKE_CASE      :			List[str]      				=   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 collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'efficientnet'
    def __init__(	self      , __snake_case = 3      , __snake_case = 6_0_0      , __snake_case = 2.0      , __snake_case = 3.1      , __snake_case = 8      , __snake_case = [3, 3, 5, 3, 5, 5, 3]      , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]      , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]      , __snake_case = []      , __snake_case = [1, 2, 2, 2, 1, 2, 1]      , __snake_case = [1, 2, 2, 3, 3, 4, 1]      , __snake_case = [1, 6, 6, 6, 6, 6, 6]      , __snake_case = 0.25      , __snake_case = "swish"      , __snake_case = 2_5_6_0      , __snake_case = "mean"      , __snake_case = 0.02      , __snake_case = 0.001      , __snake_case = 0.99      , __snake_case = 0.5      , __snake_case = 0.2      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    num_channels
         snake_case					=    image_size
         snake_case					=    width_coefficient
         snake_case					=    depth_coefficient
         snake_case					=    depth_divisor
         snake_case					=    kernel_sizes
         snake_case					=    in_channels
         snake_case					=    out_channels
         snake_case					=    depthwise_padding
         snake_case					=    strides
         snake_case					=    num_block_repeats
         snake_case					=    expand_ratios
         snake_case					=    squeeze_expansion_ratio
         snake_case					=    hidden_act
         snake_case					=    hidden_dim
         snake_case					=    pooling_type
         snake_case					=    initializer_range
         snake_case					=    batch_norm_eps
         snake_case					=    batch_norm_momentum
         snake_case					=    dropout_rate
         snake_case					=    drop_connect_rate
         snake_case					=    sum(__snake_case   ) * 4






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]   )




    @property
    def 		a_      (	self   ):
         return 1E-5




