

"""simple docstring"""




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   :	Tuple												=						logging.get_logger(__name__)

_lowercase   :	Optional[int]												=						{
    '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   :	Tuple												=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def       lowercase__						(       snake_case_   :Tuple			,		snake_case_   :Dict			,		snake_case_   :Optional[int]			,		snake_case_   :Any			,		snake_case_   :Dict    ):
	for attribute in key.split('''.'''    ):
		__UpperCAmelCase      		= getattr(snake_case_			,		snake_case_    )

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

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





def       lowercase__						(       snake_case_   :Dict			,		snake_case_   :Dict    ):
	__UpperCAmelCase      		= []
	__UpperCAmelCase      		= fairseq_model.state_dict()

	__UpperCAmelCase      		= hf_model.feature_extractor

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

	for name, value in fairseq_dict.items():
		__UpperCAmelCase      		= False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		hf_model.config.feat_extract_norm == '''group'''			,		)
			__UpperCAmelCase      		= True
		elif name.split('''.'''    )[0] == "proj":
			__UpperCAmelCase      		= fairseq_model.proj
			__UpperCAmelCase      		= True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split('''w2v_model.'''    )[-1] == name.split('''.'''    )[0]:
					__UpperCAmelCase      		= True
					if "*" in mapped_key:
						__UpperCAmelCase      		= name.split(snake_case_    )[0].split('''.'''    )[-2]
						__UpperCAmelCase      		= mapped_key.replace('''*'''			,		snake_case_    )
					if "weight_g" in name:
						__UpperCAmelCase      		= '''weight_g'''
					elif "weight_v" in name:
						__UpperCAmelCase      		= '''weight_v'''
					elif "bias" in name:
						__UpperCAmelCase      		= '''bias'''
					elif "weight" in name:
						__UpperCAmelCase      		= '''weight'''
					else:
						__UpperCAmelCase      		= 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       lowercase__						(       snake_case_   :str			,		snake_case_   :List[Any]			,		snake_case_   :str			,		snake_case_   :List[Any]			,		snake_case_   :Dict    ):
	__UpperCAmelCase      		= full_name.split('''conv_layers.'''    )[-1]
	__UpperCAmelCase      		= name.split('''.'''    )
	__UpperCAmelCase      		= int(items[0]    )
	__UpperCAmelCase      		= 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.'''
			)
			__UpperCAmelCase      		= 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.'''
			)
			__UpperCAmelCase      		= 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."
			)
			__UpperCAmelCase      		= 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.'''
			)
			__UpperCAmelCase      		= 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       lowercase__						(       snake_case_   :Any    ):
	__UpperCAmelCase		,							__UpperCAmelCase      		= emb.weight.shape
	__UpperCAmelCase      		= nn.Linear(snake_case_			,		snake_case_			,		bias=snake_case_    )
	__UpperCAmelCase      		= emb.weight.data
	return lin_layer





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

	__UpperCAmelCase      		= len(snake_case_    )

	__UpperCAmelCase      		= {
	    '''<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       lowercase__						(       snake_case_   :int			,		snake_case_   :Any			,		snake_case_   :Optional[int]			,		snake_case_   :Tuple			,		snake_case_   :Optional[int]			,		snake_case_   :Optional[Any]			,		snake_case_   :List[Any]			,		):
	__UpperCAmelCase      		= WavaVecaConfig.from_pretrained(snake_case_    )
	__UpperCAmelCase      		= SpeechaTextaConfig.from_pretrained(
	    snake_case_			,		vocab_size=snake_case_			,		decoder_layers=snake_case_			,		do_stable_layer_norm=snake_case_    )

	__UpperCAmelCase      		= WavaVecaFeatureExtractor(
	    feature_size=1			,		sampling_rate=16_000			,		padding_value=0			,		do_normalize=snake_case_			,		return_attention_mask=snake_case_			,		)

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

	# set weights for wav2vec2 encoder
	__UpperCAmelCase      		= WavaVecaModel(snake_case_    )
	__UpperCAmelCase      		= recursively_load_weights_wavaveca(model.encoder			,		snake_case_    )

	__UpperCAmelCase      		= SpeechaTextaForCausalLM(snake_case_    )
	__UpperCAmelCase		,							__UpperCAmelCase      		= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,		strict=snake_case_    )

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

	__UpperCAmelCase      		= SpeechEncoderDecoderModel(encoder=snake_case_			,		decoder=snake_case_    )
	__UpperCAmelCase      		= False

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

	__UpperCAmelCase      		= create_vocab_dict(snake_case_    )

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

	__UpperCAmelCase      		= SpeechaTextaTokenizer(os.path.join(snake_case_			,		'''vocab.json'''    )    )
	tokenizer.save_pretrained(snake_case_    )

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

	__UpperCAmelCase      		= SpeechEncoderDecoderConfig.from_dict(snake_case_    )

	hf_wavavec.save_pretrained(snake_case_    )
	feature_extractor.save_pretrained(snake_case_    )


if __name__ == "__main__":
				_lowercase   :	Union[str, Any]												=						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')

				_lowercase   :	Dict												=						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,
				)



import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       10




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4]
											snake_case_     							=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       ''
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       (
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											snake_case_							,       snake_case_     							=       process_story(snake_case			)

											snake_case_     							=       [
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(snake_case ,  snake_case			)

											snake_case_     							=       ['It was the best of times.']
											self.assertEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  0			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4, 23, 23, 23]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  23			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([8, 2, 3, 4, 1, 1, 1]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  1			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       101
											snake_case_     							=       torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]			)
											snake_case_     							=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]			)

											snake_case_     							=       compute_token_type_ids(snake_case ,  snake_case			)
											np.testing.assert_array_equal(snake_case ,  snake_case			)




