




"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase							:    Tuple  							=							random.Random()



def 					a__				(       snake_case__ ,				snake_case__=1.0 ,				snake_case__=None ,				snake_case__=None       )		->      int:
			if rng is None:
						lowerCamelCase       =      global_rng

			lowerCamelCase       =      []
			for batch_idx in range(shape[0]       ):
						values.append([]       )
						for _ in range(shape[1]       ):
									values[-1].append(rng.random() * scale       )

			return values






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=7	,				_a=400	,				_a=2_000	,				_a=1	,				_a=0.0	,				_a=16_000	,				_a=True	,				_a=True	,				):





							"""simple docstring"""
							lowerCamelCase       =      parent
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      min_seq_length
							lowerCamelCase       =      max_seq_length
							lowerCamelCase       =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCamelCase       =      feature_size
							lowerCamelCase       =      padding_value
							lowerCamelCase       =      sampling_rate
							lowerCamelCase       =      return_attention_mask
							lowerCamelCase       =      do_normalize





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}





				def      _lowerCAmelCase       ( self	,				_a=False	,				_a=False				):





							"""simple docstring"""
							def _flatten(_a				):
										return list(itertools.chain(*_a				)				)

							if equal_length:
										lowerCamelCase       =      floats_list((self.batch_size, self.max_seq_length)				)
							else:
										# make sure that inputs increase in size
										lowerCamelCase       =      [
										    _flatten(floats_list((x, self.feature_size)				)				)
										    for x in range(self.min_seq_length	,				self.max_seq_length	,				self.seq_length_diff				)
										]

							if numpify:
										lowerCamelCase       =      [np.asarray(_a				) for x in speech_inputs]

							return speech_inputs






class 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		WavaVecaFeatureExtractor





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      WavaVecaFeatureExtractionTester(self				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							self.assertTrue(np.all(np.mean(_a	,				axis=0				) < 1e-3				)				)
							self.assertTrue(np.all(np.abs(np.var(_a	,				axis=0				) - 1				) < 1e-3				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Tests that all call wrap to encode_plus and batch_encode_plus
							lowerCamelCase       =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							# create three inputs of length 800, 1000, and 1200
							lowerCamelCase       =      [floats_list((1, x)				)[0] for x in range(800	,				1_400	,				200				)]
							lowerCamelCase       =      [np.asarray(_a				) for speech_input in speech_inputs]

							# Test not batched input
							lowerCamelCase       =      feat_extract(speech_inputs[0]	,				return_tensors="""np"""				).input_values
							lowerCamelCase       =      feat_extract(np_speech_inputs[0]	,				return_tensors="""np"""				).input_values
							self.assertTrue(np.allclose(_a	,				_a	,				atol=1e-3				)				)

							# Test batched
							lowerCamelCase       =      feat_extract(_a	,				return_tensors="""np"""				).input_values
							lowerCamelCase       =      feat_extract(_a	,				return_tensors="""np"""				).input_values
							for enc_seq_a, enc_seq_a in zip(_a	,				_a				):
										self.assertTrue(np.allclose(_a	,				_a	,				atol=1e-3				)				)

							# Test 2-D numpy arrays are batched.
							lowerCamelCase       =      [floats_list((1, x)				)[0] for x in (800, 800, 800)]
							lowerCamelCase       =      np.asarray(_a				)
							lowerCamelCase       =      feat_extract(_a	,				return_tensors="""np"""				).input_values
							lowerCamelCase       =      feat_extract(_a	,				return_tensors="""np"""				).input_values
							for enc_seq_a, enc_seq_a in zip(_a	,				_a				):
										self.assertTrue(np.allclose(_a	,				_a	,				atol=1e-3				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							lowerCamelCase       =      [floats_list((1, x)				)[0] for x in range(800	,				1_400	,				200				)]

							lowerCamelCase       =      ["""longest""", """max_length""", """do_not_pad"""]
							lowerCamelCase       =      [None, 1_600, None]
							for max_length, padding in zip(_a	,				_a				):
										lowerCamelCase       =      feat_extract(_a	,				padding=_a	,				max_length=_a	,				return_tensors="""np"""				)
										lowerCamelCase       =      processed.input_values

										self._check_zero_mean_unit_variance(input_values[0][:800]				)
										self.assertTrue(input_values[0][800:].sum() < 1e-6				)
										self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
										self.assertTrue(input_values[0][1_000:].sum() < 1e-6				)
										self._check_zero_mean_unit_variance(input_values[2][:1_200]				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							lowerCamelCase       =      range(800	,				1_400	,				200				)
							lowerCamelCase       =      [floats_list((1, x)				)[0] for x in lengths]

							lowerCamelCase       =      ["""longest""", """max_length""", """do_not_pad"""]
							lowerCamelCase       =      [None, 1_600, None]

							for max_length, padding in zip(_a	,				_a				):
										lowerCamelCase       =      feat_extract(_a	,				max_length=_a	,				padding=_a				)
										lowerCamelCase       =      processed.input_values

										self._check_zero_mean_unit_variance(input_values[0][:800]				)
										self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
										self._check_zero_mean_unit_variance(input_values[2][:1_200]				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							lowerCamelCase       =      [floats_list((1, x)				)[0] for x in range(800	,				1_400	,				200				)]
							lowerCamelCase       =      feat_extract(
							    _a	,				truncation=_a	,				max_length=1_000	,				padding="""max_length"""	,				return_tensors="""np"""				)
							lowerCamelCase       =      processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]				)
							self._check_zero_mean_unit_variance(input_values[1]				)
							self._check_zero_mean_unit_variance(input_values[2]				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							lowerCamelCase       =      [floats_list((1, x)				)[0] for x in range(800	,				1_400	,				200				)]
							lowerCamelCase       =      feat_extract(
							    _a	,				truncation=_a	,				max_length=1_000	,				padding="""longest"""	,				return_tensors="""np"""				)
							lowerCamelCase       =      processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]				)
							self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
							self._check_zero_mean_unit_variance(input_values[2]				)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1_000)				)

							lowerCamelCase       =      [floats_list((1, x)				)[0] for x in range(800	,				1_400	,				200				)]
							lowerCamelCase       =      feat_extract(
							    _a	,				truncation=_a	,				max_length=2_000	,				padding="""longest"""	,				return_tensors="""np"""				)
							lowerCamelCase       =      processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]				)
							self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
							self._check_zero_mean_unit_variance(input_values[2]				)

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1_200)				)





				@require_torch
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import torch

							lowerCamelCase       =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							lowerCamelCase       =      np.random.rand(100				).astype(np.floataa				)
							lowerCamelCase       =      np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
										lowerCamelCase       =      feature_extractor.pad([{"""input_values""": inputs}]	,				return_tensors="""np"""				)
										self.assertTrue(np_processed.input_values.dtype == np.floataa				)
										lowerCamelCase       =      feature_extractor.pad([{"""input_values""": inputs}]	,				return_tensors="""pt"""				)
										self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)





				@slow
				@require_torch
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# this test makes sure that models that are using
							# group norm don't have their feature extractor return the
							# attention_mask
							for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
										lowerCamelCase       =      WavaVecaConfig.from_pretrained(_a				)
										lowerCamelCase       =      WavaVecaFeatureExtractor.from_pretrained(_a				)

										# only "layer" feature extraction norm should make use of
										# attention_mask
										self.assertEqual(feat_extract.return_attention_mask	,				config.feat_extract_norm == """layer"""				)









"""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							:    Optional[int]  							=							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[Any]  							=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      List[Any]:
			for attribute in key.split("""."""       ):
						lowerCamelCase       =      getattr(snake_case__ ,				snake_case__       )

			if weight_type is not None:
						lowerCamelCase       =      getattr(snake_case__ ,				snake_case__       ).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 					a__				(       snake_case__ ,				snake_case__       )		->      Tuple:
			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(
									    snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				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(snake_case__       )[0].split("""."""       )[-2]
																		lowerCamelCase       =      mapped_key.replace("""*""" ,				snake_case__       )
															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(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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[int]:
			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(snake_case__       )



def 					a__				(       snake_case__       )		->      Tuple:
			lowerCamelCase   ,     lowerCamelCase       =      emb.weight.shape
			lowerCamelCase       =      nn.Linear(snake_case__ ,				snake_case__ ,				bias=snake_case__       )
			lowerCamelCase       =      emb.weight.data
			return lin_layer



def 					a__				(       snake_case__       )		->      Optional[int]:
			with open(snake_case__ ,				"""r""" ,				encoding="""utf-8"""       ) as f:
						lowerCamelCase       =      f.readlines()
						lowerCamelCase       =      [line.split(""" """       )[0] for line in lines]

			lowerCamelCase       =      len(snake_case__       )

			lowerCamelCase       =      {
			    """<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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				)		->      Optional[int]:
			lowerCamelCase       =      WavaVecaConfig.from_pretrained(snake_case__       )
			lowerCamelCase       =      SpeechaTextaConfig.from_pretrained(
			    snake_case__ ,				vocab_size=snake_case__ ,				decoder_layers=snake_case__ ,				do_stable_layer_norm=snake_case__       )

			lowerCamelCase       =      WavaVecaFeatureExtractor(
			    feature_size=1 ,				sampling_rate=1_60_00 ,				padding_value=0 ,				do_normalize=snake_case__ ,				return_attention_mask=snake_case__ ,				)

			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(snake_case__       )
			lowerCamelCase       =      recursively_load_weights_wavaveca(model.encoder ,				snake_case__       )

			lowerCamelCase       =      SpeechaTextaForCausalLM(snake_case__       )
			lowerCamelCase   ,     lowerCamelCase       =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,				strict=snake_case__       )

			# 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=snake_case__ ,				decoder=snake_case__       )
			lowerCamelCase       =      False

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

			lowerCamelCase       =      create_vocab_dict(snake_case__       )

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

			lowerCamelCase       =      SpeechaTextaTokenizer(os.path.join(snake_case__ ,				"""vocab.json"""       )       )
			tokenizer.save_pretrained(snake_case__       )

			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(snake_case__       )

			hf_wavavec.save_pretrained(snake_case__       )
			feature_extractor.save_pretrained(snake_case__       )


if __name__ == "__main__":
					lowerCAmelCase							:    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							:    str  							=							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,
					)



