



"""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()
__lowerCAmelCase       :					Union[str, Any]      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					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''',
}
__lowerCAmelCase       :					Optional[int]      					=					[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						for attribute in key.split("""."""    ):
												snake_case_   : Dict						=			getattr(__UpperCamelCase					, __UpperCamelCase    )

						if weight_type is not None:
												snake_case_   : Optional[Any]						=			getattr(__UpperCamelCase					, __UpperCamelCase    ).shape
						else:
												snake_case_   : Any						=			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_   : Any						=			value
						elif weight_type == "weight_g":
												snake_case_   : Optional[int]						=			value
						elif weight_type == "weight_v":
												snake_case_   : Union[str, Any]						=			value
						elif weight_type == "bias":
												snake_case_   : Optional[int]						=			value
						else:
												snake_case_   : List[Any]						=			value

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


						'''simple docstring'''






						snake_case_   : List[Any]						=			[]
						snake_case_   : Dict						=			fairseq_model.state_dict()

						snake_case_   : Any						=			hf_model.feature_extractor

						# if encoder has different dim to decoder -> use proj_weight
						snake_case_   : Tuple						=			None

						for name, value in fairseq_dict.items():
												snake_case_   : List[str]						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, hf_model.config.feat_extract_norm == """group"""					, )
																		snake_case_   : Dict						=			True
												elif name.split("""."""    )[0] == "proj":
																		snake_case_   : List[Any]						=			fairseq_model.proj
																		snake_case_   : Tuple						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
																														snake_case_   : Union[str, Any]						=			True
																														if "*" in mapped_key:
																																				snake_case_   : Union[str, Any]						=			name.split(__UpperCamelCase    )[0].split("""."""    )[-2]
																																				snake_case_   : Tuple						=			mapped_key.replace("""*"""					, __UpperCamelCase    )
																														if "weight_g" in name:
																																				snake_case_   : Dict						=			"""weight_g"""
																														elif "weight_v" in name:
																																				snake_case_   : List[str]						=			"""weight_v"""
																														elif "bias" in name:
																																				snake_case_   : List[str]						=			"""bias"""
																														elif "weight" in name:
																																				snake_case_   : List[str]						=			"""weight"""
																														else:
																																				snake_case_   : Tuple						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			str					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : Dict						=			full_name.split("""conv_layers."""    )[-1]
						snake_case_   : str						=			name.split("""."""    )
						snake_case_   : Any						=			int(items[0]    )
						snake_case_   : 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.'
																		)
																		snake_case_   : 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.'
																		)
																		snake_case_   : 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."
																		)
																		snake_case_   : 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.'
																		)
																		snake_case_   : Dict						=			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      :			str    ):


						'''simple docstring'''






						snake_case_			,		snake_case_   : Dict						=			emb.weight.shape
						snake_case_   : List[Any]						=			nn.Linear(__UpperCamelCase					, __UpperCamelCase					, bias=__UpperCamelCase    )
						snake_case_   : Dict						=			emb.weight.data
						return lin_layer
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						with open(__UpperCamelCase					, """r"""					, encoding="""utf-8"""    ) as f:
												snake_case_   : Tuple						=			f.readlines()
												snake_case_   : Optional[Any]						=			[line.split(""" """    )[0] for line in lines]

						snake_case_   : Optional[int]						=			len(__UpperCamelCase    )

						snake_case_   : Union[str, Any]						=			{
						    """<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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			str					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			Tuple					, ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			WavaVecaConfig.from_pretrained(__UpperCamelCase    )
						snake_case_   : Optional[Any]						=			SpeechaTextaConfig.from_pretrained(
						    __UpperCamelCase					, vocab_size=__UpperCamelCase					, decoder_layers=__UpperCamelCase					, do_stable_layer_norm=__UpperCamelCase    )

						snake_case_   : Optional[int]						=			WavaVecaFeatureExtractor(
						    feature_size=1					, sampling_rate=1_6_0_0_0					, padding_value=0					, do_normalize=__UpperCamelCase					, return_attention_mask=__UpperCamelCase					, )

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

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

						snake_case_   : List[str]						=			SpeechaTextaForCausalLM(__UpperCamelCase    )
						snake_case_			,		snake_case_   : Optional[int]						=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					, strict=__UpperCamelCase    )

						# set output linear layer
						unexpected_keys.remove("""embed_out"""    )
						snake_case_   : int						=			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_   : Any						=			SpeechEncoderDecoderModel(encoder=__UpperCamelCase					, decoder=__UpperCamelCase    )
						snake_case_   : Tuple						=			False

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

						snake_case_   : Dict						=			create_vocab_dict(__UpperCamelCase    )

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

						snake_case_   : str						=			SpeechaTextaTokenizer(os.path.join(__UpperCamelCase					, """vocab.json"""    )    )
						tokenizer.save_pretrained(__UpperCamelCase    )

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

						snake_case_   : Dict						=			SpeechEncoderDecoderConfig.from_dict(__UpperCamelCase    )

						hf_wavavec.save_pretrained(__UpperCamelCase    )
						feature_extractor.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
					__lowerCAmelCase       :					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_0224, type=int, help='''Vocab size of decoder''')
					parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

					__lowerCAmelCase       :					Any      					=					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,
					)






"""simple docstring"""





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=4							,    ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Tuple						=			parent
								snake_case_   : List[str]						=			batch_size
								snake_case_   : int						=			seq_length
								snake_case_   : List[Any]						=			is_training
								snake_case_   : Optional[int]						=			use_attention_mask
								snake_case_   : Optional[Any]						=			use_token_type_ids
								snake_case_   : Union[str, Any]						=			use_labels
								snake_case_   : str						=			vocab_size
								snake_case_   : List[str]						=			hidden_size
								snake_case_   : List[str]						=			num_hidden_layers
								snake_case_   : int						=			num_attention_heads
								snake_case_   : Dict						=			intermediate_size
								snake_case_   : Union[str, Any]						=			hidden_act
								snake_case_   : List[str]						=			hidden_dropout_prob
								snake_case_   : Any						=			attention_probs_dropout_prob
								snake_case_   : List[str]						=			max_position_embeddings
								snake_case_   : Union[str, Any]						=			type_vocab_size
								snake_case_   : str						=			type_sequence_label_size
								snake_case_   : Dict						=			initializer_range
								snake_case_   : str						=			num_choices





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : Tuple						=			None
								if self.use_attention_mask:
														snake_case_   : Union[str, Any]						=			random_attention_mask([self.batch_size, self.seq_length]   )

								snake_case_   : Optional[int]						=			None
								if self.use_token_type_ids:
														snake_case_   : str						=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size   )

								snake_case_   : Union[str, Any]						=			RoFormerConfig(
								    vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=_lowercase							,    initializer_range=self.initializer_range							,    )

								return config, input_ids, token_type_ids, attention_mask





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : str						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			config_and_inputs
								snake_case_   : Tuple						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict




@require_flax
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       True

		_lowerCamelCase			  =       (
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerModelTester(self   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Tuple						=			model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,    from_pt=_lowercase   )
														snake_case_   : List[str]						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(_lowercase   )




@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
								snake_case_   : Tuple						=			jnp.array([[0, 1, 2, 3, 4, 5]]   )
								snake_case_   : Dict						=			model(_lowercase   )[0]

								snake_case_   : Optional[int]						=			5_0_0_0_0

								snake_case_   : Union[str, Any]						=			(1, 6, vocab_size)
								self.assertEqual(output.shape							,    _lowercase   )

								snake_case_   : Dict						=			jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

								self.assertTrue(jnp.allclose(output[:, :3, :3]							,    _lowercase							,    atol=1E-4   )   )

