"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

SCREAMING_SNAKE_CASE									=						{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for attribute in key.split('.'			):
		_lowercase :    List[str]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			)

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

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[Any]							=							[]
	_lowercase :    int							=							fairseq_model.state_dict()

	_lowercase :    str							=							hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_lowercase :    Dict							=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,hf_model.config.feat_extract_norm == 'group'     ,)
			_lowercase :    str							=							True
		else:
			for key, mapped_key in MAPPING.items():
				_lowercase :    Optional[int]							=							'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

				if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
					_lowercase :    List[Any]							=							True
					if "*" in mapped_key:
						_lowercase :    Dict							=							name.split(__UpperCAmelCase			)[0].split('.'			)[-2]
						_lowercase :    List[Any]							=							mapped_key.replace('*'     ,__UpperCAmelCase			)
					if "weight_g" in name:
						_lowercase :    List[Any]							=							'weight_g'
					elif "weight_v" in name:
						_lowercase :    Union[str, Any]							=							'weight_v'
					elif "weight" in name:
						_lowercase :    Optional[Any]							=							'weight'
					elif "bias" in name:
						_lowercase :    str							=							'bias'
					else:
						_lowercase :    int							=							None
					set_recursively(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			)
				continue
		if not is_used:
			unused_weights.append(__UpperCAmelCase			)

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    str							=							SEWConfig()
	if is_finetuned:
		_lowercase :    Dict							=							model.wav_encoder.wav_model.cfg
	else:
		_lowercase :    Tuple							=							model.cfg

	_lowercase :    Dict							=							fs_config.conv_bias
	_lowercase :    int							=							eval(fs_config.conv_feature_layers			)
	_lowercase :    Dict							=							[x[0] for x in conv_layers]
	_lowercase :    int							=							[x[1] for x in conv_layers]
	_lowercase :    Tuple							=							[x[2] for x in conv_layers]
	_lowercase :    int							=							'gelu'
	_lowercase :    str							=							'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_lowercase :    Optional[int]							=							0.0
	_lowercase :    Dict							=							fs_config.activation_fn.name
	_lowercase :    Tuple							=							fs_config.encoder_embed_dim
	_lowercase :    Dict							=							0.0_2
	_lowercase :    Union[str, Any]							=							fs_config.encoder_ffn_embed_dim
	_lowercase :    Dict							=							1E-5
	_lowercase :    Tuple							=							fs_config.encoder_layerdrop
	_lowercase :    List[str]							=							fs_config.encoder_attention_heads
	_lowercase :    List[Any]							=							fs_config.conv_pos_groups
	_lowercase :    Dict							=							fs_config.conv_pos
	_lowercase :    Union[str, Any]							=							len(__UpperCAmelCase			)
	_lowercase :    Union[str, Any]							=							fs_config.encoder_layers
	_lowercase :    Tuple							=							fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		_lowercase :    Optional[Any]							=							model.cfg
		_lowercase :    int							=							fs_config.final_dropout
		_lowercase :    Tuple							=							fs_config.layerdrop
	_lowercase :    Optional[int]							=							fs_config.activation_dropout
	_lowercase :    List[Any]							=							fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	_lowercase :    Tuple							=							fs_config.attention_dropout
	_lowercase :    int							=							fs_config.dropout_input
	_lowercase :    Union[str, Any]							=							fs_config.dropout
	_lowercase :    Optional[int]							=							fs_config.mask_channel_length
	_lowercase :    List[str]							=							fs_config.mask_channel_prob
	_lowercase :    Optional[Any]							=							fs_config.mask_length
	_lowercase :    List[str]							=							fs_config.mask_prob

	_lowercase :    Any							=							'Wav2Vec2FeatureExtractor'
	_lowercase :    Optional[int]							=							'Wav2Vec2CTCTokenizer'

	return config


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=True			):






	"""simple docstring"""





	if is_finetuned:
		_lowercase :    Optional[Any]							=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]     ,arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
	else:
		_lowercase :    str							=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

	if config_path is not None:
		_lowercase :    Union[str, Any]							=							SEWConfig.from_pretrained(__UpperCAmelCase			)
	else:
		_lowercase :    Optional[Any]							=							convert_config(model[0]     ,__UpperCAmelCase			)
	_lowercase :    str							=							model[0].eval()

	_lowercase :    Tuple							=							True if config.feat_extract_norm == 'layer' else False
	_lowercase :    List[str]							=							WavaVecaFeatureExtractor(
	    feature_size=1     ,sampling_rate=16_000     ,padding_value=0     ,do_normalize=__UpperCAmelCase     ,return_attention_mask=__UpperCAmelCase     ,)

	if is_finetuned:
		if dict_path:
			_lowercase :    Dict							=							Dictionary.load(__UpperCAmelCase			)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_lowercase :    Tuple							=							target_dict.pad_index
			_lowercase :    str							=							target_dict.bos_index
			_lowercase :    List[str]							=							target_dict.pad_index
			_lowercase :    List[Any]							=							target_dict.bos_index
			_lowercase :    Any							=							target_dict.eos_index
			_lowercase :    int							=							len(target_dict.symbols			)
			_lowercase :    Optional[int]							=							os.path.join(__UpperCAmelCase     ,'vocab.json'			)
			if not os.path.isdir(__UpperCAmelCase			):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__UpperCAmelCase			)			)
				return
			os.makedirs(__UpperCAmelCase     ,exist_ok=__UpperCAmelCase			)
			with open(__UpperCAmelCase     ,'w'     ,encoding='utf-8'			) as vocab_handle:
				json.dump(target_dict.indices     ,__UpperCAmelCase			)
			_lowercase :    List[Any]							=							WavaVecaCTCTokenizer(
			    __UpperCAmelCase     ,unk_token=target_dict.unk_word     ,pad_token=target_dict.pad_word     ,bos_token=target_dict.bos_word     ,eos_token=target_dict.eos_word     ,word_delimiter_token='|'     ,do_lower_case=__UpperCAmelCase     ,)
			_lowercase :    List[str]							=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase     ,tokenizer=__UpperCAmelCase			)
			processor.save_pretrained(__UpperCAmelCase			)

		_lowercase :    str							=							SEWForCTC(__UpperCAmelCase			)
	else:
		_lowercase :    int							=							SEWModel(__UpperCAmelCase			)
		feature_extractor.save_pretrained(__UpperCAmelCase			)

	recursively_load_weights(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			)

	hf_model.save_pretrained(__UpperCAmelCase			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						parser.add_argument(
						    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)




"""simple docstring"""







import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
SCREAMING_SNAKE_CASE									=						pytest.mark.integration


SCREAMING_SNAKE_CASE									=						{'comet'}
SCREAMING_SNAKE_CASE									=						importlib.util.find_spec('fairseq') is not None

SCREAMING_SNAKE_CASE									=						{'code_eval'}
SCREAMING_SNAKE_CASE									=						os.name == 'nt'

SCREAMING_SNAKE_CASE									=						{'bertscore', 'frugalscore', 'perplexity'}
SCREAMING_SNAKE_CASE									=						importlib.util.find_spec('transformers') is not None


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	@wraps(__UpperCAmelCase			)
	def wrapper(self     ,__UpperCAmelCase			):
		if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
			self.skipTest('"test requires Fairseq"'			)
		else:
			test_case(self     ,__UpperCAmelCase			)

	return wrapper


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	@wraps(__UpperCAmelCase			)
	def wrapper(self     ,__UpperCAmelCase			):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest('"test requires transformers"'			)
		else:
			test_case(self     ,__UpperCAmelCase			)

	return wrapper


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	@wraps(__UpperCAmelCase			)
	def wrapper(self     ,__UpperCAmelCase			):
		if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
			self.skipTest('"test not supported on Windows"'			)
		else:
			test_case(self     ,__UpperCAmelCase			)

	return wrapper


def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    int							=							[metric_dir.split(os.sep			)[-2] for metric_dir in glob.glob('./metrics/*/'			)]
	return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished



@parameterized.named_parameters(get_local_metric_names()     )
@for_all_test_methods(
    __lowerCamelCase						,							__lowerCamelCase						,							__lowerCamelCase     )
@local
class  _lowerCamelCase			(parameterized.TestCase     ):
				_snake_case     =  {}
				_snake_case     =  None







				@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'						)
				@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'						)
				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							'[...]'
					_lowercase :    str							=							importlib.import_module(
					    datasets.load.metric_module_factory(os.path.join('metrics'			,					lowerCamelCase_						)						).module_path						)
					_lowercase :    Dict							=							datasets.load.import_main_class(metric_module.__name__			,					dataset=lowerCamelCase_						)
					# check parameters
					_lowercase :    Optional[int]							=							inspect.signature(metric._compute						).parameters
					self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()						)						)  # no **kwargs
					# run doctest
					with self.patch_intensive_calls(lowerCamelCase_			,					metric_module.__name__						):
						with self.use_local_metrics():
							try:
								_lowercase :    Optional[Any]							=							doctest.testmod(lowerCamelCase_			,					verbose=lowerCamelCase_			,					raise_on_error=lowerCamelCase_						)
							except doctest.UnexpectedException as e:
								raise e.exc_info[1]  # raise the exception that doctest caught
					self.assertEqual(results.failed			,					0						)
					self.assertGreater(results.attempted			,					1						)







				@slow
				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							'[...]'
					_lowercase :    Dict							=							importlib.import_module(
					    datasets.load.metric_module_factory(os.path.join('metrics'			,					lowerCamelCase_						)						).module_path						)
					# run doctest
					with self.use_local_metrics():
						_lowercase :    str							=							doctest.testmod(lowerCamelCase_			,					verbose=lowerCamelCase_			,					raise_on_error=lowerCamelCase_						)
					self.assertEqual(results.failed			,					0						)
					self.assertGreater(results.attempted			,					1						)







				@contextmanager
				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					if metric_name in self.INTENSIVE_CALLS_PATCHER:
						with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase_						):
							yield
					else:
						yield







				@contextmanager
				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					def load_local_metric(lowerCamelCase_    :       Optional[Any]			,					*lowerCamelCase_    :       Any			,					**lowerCamelCase_    :       Optional[int]						):
						return load_metric(os.path.join('metrics'			,					lowerCamelCase_						)			,					*lowerCamelCase_			,					**lowerCamelCase_						)

					with patch('datasets.load_metric'						) as mock_load_metric:
						_lowercase :    str							=							load_local_metric
						yield







				@classmethod
				def 							__UpperCAmelCase					( cls    :       Tuple			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					def wrapper(lowerCamelCase_    :       int						):
						_lowercase :    Any							=							contextmanager(lowerCamelCase_						)
						_lowercase :    Any							=							patcher
						return patcher

					return wrapper



@LocalMetricTest.register_intensive_calls_patcher('bleurt'			)
def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

	tf.flags.DEFINE_string('sv'     ,''     ,''			)  # handle pytest cli flags



	class  _lowerCamelCase			(__lowerCamelCase     ):







					def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       str						):

						"""simple docstring"""






						assert len(input_dict['input_ids']						) == 2
						return np.array([1.03, 1.04]						)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
	with patch('bleurt.score._create_predictor'			) as mock_create_predictor:
		_lowercase :    Dict							=							MockedPredictor()
		yield


@LocalMetricTest.register_intensive_calls_patcher('bertscore'			)
def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	import torch

	def bert_cos_score_idf(__UpperCAmelCase     ,__UpperCAmelCase     ,*__UpperCAmelCase     ,**__UpperCAmelCase			):
		return torch.tensor([[1.0, 1.0, 1.0]] * len(__UpperCAmelCase			)			)

	# mock get_model which is supposed to do download a bert model
	# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
	with patch('bert_score.scorer.get_model'			), patch(
	    'bert_score.scorer.bert_cos_score_idf'			) as mock_bert_cos_score_idf:
		_lowercase :    Tuple							=							bert_cos_score_idf
		yield


@LocalMetricTest.register_intensive_calls_patcher('comet'			)
def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	def load_from_checkpoint(__UpperCAmelCase			):



		class  _lowerCamelCase			:







						def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       str			,					*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       List[str]						):

							"""simple docstring"""






							assert len(lowerCamelCase_						) == 2
							_lowercase :    Union[str, Any]							=							[0.19, 0.92]
							return scores, sum(lowerCamelCase_						) / len(lowerCamelCase_						)



		return Model()

	# mock load_from_checkpoint which is supposed to do download a bert model
	# mock load_from_checkpoint which is supposed to do download a bert model
	with patch('comet.download_model'			) as mock_download_model:
		_lowercase :    Dict							=							None
		with patch('comet.load_from_checkpoint'			) as mock_load_from_checkpoint:
			_lowercase :    str							=							load_from_checkpoint
			yield







def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Tuple							=							load_metric(os.path.join('metrics'     ,'seqeval'			)			)
	_lowercase :    int							=							'ERROR'
	_lowercase :    Union[str, Any]							=							F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
	with pytest.raises(__UpperCAmelCase     ,match=re.escape(__UpperCAmelCase			)			):
		metric.compute(predictions=[]     ,references=[]     ,scheme=__UpperCAmelCase			)



