import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

UpperCAmelCase__     = {
    '''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''',
}
UpperCAmelCase__     = [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     List[str]					)       ->				List[Any]:







			'''simple docstring'''

			for attribute in key.split('.'					):
						_UpperCAmelCase	 =     getattr(_A							,     _A					)

			if weight_type is not None:
						_UpperCAmelCase	 =     getattr(_A							,     _A					).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 							A			(  _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     []
			_UpperCAmelCase	 =     fairseq_model.state_dict()

			_UpperCAmelCase	 =     hf_model.feature_extractor
			_UpperCAmelCase	 =     hf_model.adapter

			for name, value in fairseq_dict.items():
						_UpperCAmelCase	 =     False
						if "conv_layers" in name:
									load_conv_layer(
									    _A							,     _A							,     _A							,     _A							,     hf_model.config.feat_extract_norm == 'group'							,     )
									_UpperCAmelCase	 =     True
						elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']					):
									load_adapter(_A							,     _A							,     _A							,     _A					)
									_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(_A					)[0].split('.'					)[-2]
																		_UpperCAmelCase	 =     mapped_key.replace('*'							,     _A					)
															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(_A							,     _A							,     _A							,     _A							,     _A					)
												continue
						if not is_used:
									unused_weights.append(_A					)

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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Dict					)       ->				Union[str, Any]:







			'''simple docstring'''
			_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(_A					)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     full_name.split('adaptor.'					)[-1]
			_UpperCAmelCase	 =     name.split('.'					)

			if items[1].isdigit():
						_UpperCAmelCase	 =     int(items[1]					)
			else:
						_UpperCAmelCase	 =     None

			if "adaptor" not in full_name:
						if "proj_ln" in full_name:
									# has to be layer norm
									if "bias" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.bias.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
												_UpperCAmelCase	 =     value
												logger.info(F"Adapter proj layer norm bias was initialized from {full_name}."					)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.weight.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
												_UpperCAmelCase	 =     value
						else:
									# has to be projection layer
									if "bias" in name:
												assert (
												    value.shape == adapter.proj.bias.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
												_UpperCAmelCase	 =     value
												logger.info(F"Adapter proj layer bias was initialized from {full_name}."					)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj.weight.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
												_UpperCAmelCase	 =     value
												logger.info(F"Adapter proj layer weight was initialized from {full_name}."					)
			elif isinstance(_A							,     _A					):
						if "bias" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.bias.data.shape
									), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
									_UpperCAmelCase	 =     value
									logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."					)
						elif "weight" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.weight.data.shape
									), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
									_UpperCAmelCase	 =     value
									logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."					)
			else:
						unused_weights.append(_A					)





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase ,  _UpperCAmelCase	 =     emb.weight.shape
			_UpperCAmelCase	 =     nn.Linear(_A							,     _A							,     bias=_A					)
			_UpperCAmelCase	 =     emb.weight.data
			return lin_layer





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     int							,     )       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     WavaVecaConfig.from_pretrained(
			    _A							,     add_adapter=_A							,     adapter_stride=_A							,     adapter_kernel_size=_A							,     use_auth_token=_A							,     output_hidden_size=_A							,     )
			_UpperCAmelCase	 =     MBartConfig.from_pretrained(_A					)

			# load model
			_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]							,     arg_overrides={
			        'config_yaml': config_yaml_path,
			        'data': '/'.join(dict_path.split('/'					)[:-1]					),
			        'w2v_path': checkpoint_path,
			        'load_pretrained_decoder_from': None,
			    }							,     )
			_UpperCAmelCase	 =     model[0].eval()

			# load feature extractor
			_UpperCAmelCase	 =     WavaVecaFeatureExtractor.from_pretrained(_A							,     use_auth_token=_A					)

			# set weights for wav2vec2 encoder
			_UpperCAmelCase	 =     WavaVecaModel(_A					)

			recursively_load_weights_wavaveca(model.encoder							,     _A					)

			# load decoder weights
			_UpperCAmelCase	 =     MBartForCausalLM(_A					)
			_UpperCAmelCase ,  _UpperCAmelCase	 =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,     strict=_A					)
			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=_A							,     decoder=_A					)
			_UpperCAmelCase	 =     False

			_UpperCAmelCase	 =     MBartaaTokenizer(_A					)
			tokenizer.save_pretrained(_A					)

			_UpperCAmelCase	 =     hf_wavavec.config.to_dict()
			_UpperCAmelCase	 =     tokenizer.pad_token_id
			_UpperCAmelCase	 =     tokenizer.bos_token_id
			_UpperCAmelCase	 =     tokenizer.eos_token_id
			_UpperCAmelCase	 =     'mbart50'
			_UpperCAmelCase	 =     'wav2vec2'

			_UpperCAmelCase	 =     tokenizer.eos_token_id
			_UpperCAmelCase	 =     250_004
			_UpperCAmelCase	 =     tokenizer.eos_token_id

			_UpperCAmelCase	 =     SpeechEncoderDecoderConfig.from_dict(_A					)

			hf_wavavec.save_pretrained(_A					)
			feature_extractor.save_pretrained(_A					)


if __name__ == "__main__":
							UpperCAmelCase__     = 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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
							parser.add_argument(
							    "--encoder_config_path",
							    default="facebook/wav2vec2-xls-r-1b",
							    type=str,
							    help="Path to hf encoder wav2vec2 checkpoint config",
							)
							parser.add_argument(
							    "--decoder_config_path",
							    default="facebook/mbart-large-50-one-to-many-mmt",
							    type=str,
							    help="Path to hf decoder checkpoint config",
							)
							parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
							parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
							parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
							parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
							parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

							UpperCAmelCase__     = parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  MgpstrTokenizer
			UpperCamelCase           =  False
			UpperCamelCase           =  {}
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						int)     ->					List[Any]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase	 =     ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
						# fmt: on
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))

						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['vocab_file'])
						with open(self.vocab_file				,	'w'				,	encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						return MgpstrTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     'tester'
						_UpperCAmelCase	 =     'tester'
						return input_text, output_text



			@unittest.skip('MGP-STR always lower cases letters.')
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					int:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers(do_lower_case=A)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase	 =     '[SPECIAL_TOKEN]'

												tokenizer.add_special_tokens({'cls_token': special_token})
												_UpperCAmelCase	 =     tokenizer.encode([special_token]				,	add_special_tokens=A)
												self.assertEqual(len(A)				,	1)

												_UpperCAmelCase	 =     tokenizer.decode(A				,	skip_special_tokens=A)
												self.assertTrue(special_token not in decoded)



			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase ,  _UpperCAmelCase	 =     self.get_input_output_texts(A)

												_UpperCAmelCase	 =     tokenizer.tokenize(A)
												_UpperCAmelCase	 =     tokenizer.convert_tokens_to_ids(A)
												_UpperCAmelCase	 =     tokenizer.encode(A				,	add_special_tokens=A)
												self.assertListEqual(A				,	A)

												_UpperCAmelCase	 =     tokenizer.convert_ids_to_tokens(A)
												self.assertNotEqual(len(A)				,	0)
												_UpperCAmelCase	 =     tokenizer.decode(A)
												self.assertIsInstance(A				,	A)

												self.assertEqual(text_a.replace(' '				,	'')				,	A)



			@unittest.skip('MGP-STR tokenizer only handles one sequence.')
			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						pass



			@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer')
			def 					_lowerCamelCase (		self     :						Dict)     ->					Tuple:



						"""simple docstring"""






						pass
