'''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          =  logging.get_logger(__name__)

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






def 				snake_case__							(			_A:					Optional[int]      ,   _A:					List[str]      ,   _A:					Dict      ,   _A:					Union[str, Any]      ,   _A:					Any )    ->	Any:

		'''simple docstring'''


		for attribute in key.split(""".""" ):
				lowerCAmelCase   					=  getattr(_A      ,   _A )

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

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






def 				snake_case__							(			_A:					Optional[Any]      ,   _A:					Union[str, Any] )    ->	str:

		'''simple docstring'''


		lowerCAmelCase   					=  []
		lowerCAmelCase   					=  fairseq_model.state_dict()

		lowerCAmelCase   					=  hf_model.feature_extractor

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

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

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

		return proj_weight






def 				snake_case__							(			_A:					Optional[Any]      ,   _A:					List[str]      ,   _A:					Union[str, Any]      ,   _A:					Any      ,   _A:					List[str] )    ->	Any:

		'''simple docstring'''


		lowerCAmelCase   					=  full_name.split("""conv_layers.""" )[-1]
		lowerCAmelCase   					=  name.split(""".""" )
		lowerCAmelCase   					=  int(items[0] )
		lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  value
						logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
		else:
				unused_weights.append(_A )






def 				snake_case__							(			_A:					Union[str, Any] )    ->	List[str]:

		'''simple docstring'''


		lowerCAmelCase					,     lowerCAmelCase   					=  emb.weight.shape
		lowerCAmelCase   					=  nn.Linear(_A      ,   _A      ,   bias=_A )
		lowerCAmelCase   					=  emb.weight.data
		return lin_layer






def 				snake_case__							(			_A:					List[Any] )    ->	List[str]:

		'''simple docstring'''


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

		lowerCAmelCase   					=  len(_A )

		lowerCAmelCase   					=  {
		    """<s>""": 0,
		    """<pad>""": 1,
		    """</s>""": 2,
		    """<unk>""": 3,
		}

		vocab_dict.update(dict(zip(_A      ,   range(4      ,   num_words + 4 ) ) ) )
		return vocab_dict






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

		'''simple docstring'''


		lowerCAmelCase   					=  WavaVecaConfig.from_pretrained(_A )
		lowerCAmelCase   					=  SpeechaTextaConfig.from_pretrained(
		    _A      ,   vocab_size=_A      ,   decoder_layers=_A      ,   do_stable_layer_norm=_A )

		lowerCAmelCase   					=  WavaVecaFeatureExtractor(
		    feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=_A      ,   return_attention_mask=_A      ,   )

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

		# set weights for wav2vec2 encoder
		lowerCAmelCase   					=  WavaVecaModel(_A )
		lowerCAmelCase   					=  recursively_load_weights_wavaveca(model.encoder      ,   _A )

		lowerCAmelCase   					=  SpeechaTextaForCausalLM(_A )
		lowerCAmelCase					,     lowerCAmelCase   					=  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,   strict=_A )

		# set output linear layer
		unexpected_keys.remove("""embed_out""" )
		lowerCAmelCase   					=  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}" )

		lowerCAmelCase   					=  SpeechEncoderDecoderModel(encoder=_A      ,   decoder=_A )
		lowerCAmelCase   					=  False

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

		lowerCAmelCase   					=  create_vocab_dict(_A )

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

		lowerCAmelCase   					=  SpeechaTextaTokenizer(os.path.join(_A      ,   """vocab.json""" ) )
		tokenizer.save_pretrained(_A )

		lowerCAmelCase   					=  hf_wavavec.config.to_dict()
		lowerCAmelCase   					=  tokenizer.pad_token_id
		lowerCAmelCase   					=  tokenizer.bos_token_id
		lowerCAmelCase   					=  tokenizer.eos_token_id
		lowerCAmelCase   					=  """speech_to_text_2"""
		lowerCAmelCase   					=  """wav2vec2"""

		lowerCAmelCase   					=  SpeechEncoderDecoderConfig.from_dict(_A )

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


if __name__ == "__main__":
						__lowercase          =  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_0_2_2_4, type=int, help='''Vocab size of decoder''')
						parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

						__lowercase          =  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 json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       MvpTokenizer
			UpperCAmelCase_			:     Optional[Any]      =       MvpTokenizerFast
			UpperCAmelCase_			:     str      =       True
			UpperCAmelCase_			:     List[Any]      =       filter_roberta_detectors



			def 							a_       (   self):




					"""simple docstring"""


					super().setUp()
					lowerCAmelCase   					=  [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))
					lowerCAmelCase   					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					lowerCAmelCase   					=  {"""unk_token""": """<unk>"""}

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



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return "lower newer", "lower newer"



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""")



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""")



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					lowerCAmelCase   					=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    max_length=len(__lowerCAmelCase)       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)

							self.assertEqual((2, 9)       ,    batch.input_ids.shape)
							self.assertEqual((2, 9)       ,    batch.attention_mask.shape)
							lowerCAmelCase   					=  batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)
							# Test that special tokens are reset



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							# check if input_ids are returned and no labels
							self.assertIn("""input_ids"""       ,    __lowerCAmelCase)
							self.assertIn("""attention_mask"""       ,    __lowerCAmelCase)
							self.assertNotIn("""labels"""       ,    __lowerCAmelCase)
							self.assertNotIn("""decoder_attention_mask"""       ,    __lowerCAmelCase)



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(text_target=__lowerCAmelCase       ,    max_length=32       ,    padding="""max_length"""       ,    return_tensors="""pt""")
							self.assertEqual(32       ,    targets["""input_ids"""].shape[1])



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(
							    ["""I am a small frog""" * 1024, """I am a small frog"""]       ,    padding=__lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)
							self.assertEqual(batch.input_ids.shape       ,    (2, 1024))



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization."""]
					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    text_target=__lowerCAmelCase       ,    return_tensors="""pt""")
							lowerCAmelCase   					=  inputs["""input_ids"""]
							lowerCAmelCase   					=  inputs["""labels"""]
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



			def 							a_       (   self):




					"""simple docstring"""


					pass







			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
									lowerCAmelCase   					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  """A, <mask> AllenNLP sentence."""
									lowerCAmelCase   					=  tokenizer_r.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["""token_type_ids"""])       ,    sum(tokens_p["""token_type_ids"""]))

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])       ,    sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])       ,    )

									lowerCAmelCase   					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
									lowerCAmelCase   					=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
									self.assertSequenceEqual(tokens_r["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])




