

"""simple docstring"""

import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

a__			:						List[Any]							=			logging.getLogger()
a__			:						int							=			torch.cuda.is_available()
a__			:						str							=			{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}





def 					__UpperCamelCase (     a__:			Path			,						a__:			list      )					->      Dict:




					"""simple docstring"""



					__lowercase        =      '''\n'''.join(a__      )
					Path(a__      ).open('''w'''      ).writelines(a__      )


a__			:						str							=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__			:						int							=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__			:						List[Any]							=			"""patrickvonplaten/t5-tiny-random"""
a__			:						Optional[Any]							=			"""sshleifer/t5-tinier-random"""
a__			:						List[str]							=			"""sshleifer/bart-tiny-random"""
a__			:						Any							=			"""sshleifer/tiny-mbart"""
a__			:						List[Any]							=			"""sshleifer/tiny-marian-en-de"""
a__			:						Any							=			"""stas/tiny-wmt19-en-de"""


a__			:						Optional[int]							=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def 					__UpperCamelCase (     a__:			Dict      )					->      Optional[int]:




					"""simple docstring"""



					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(a__			,						f'''{split}.source'''      )			,						a__      )
										_dump_articles(os.path.join(a__			,						f'''{split}.target'''      )			,						a__      )
					return tmp_dir


class       _snake_case  (    UpperCamelCase							):







						@classmethod
						def 							_UpperCamelCase     (cls				)      ->    Optional[int]:
											logging.disable(logging.CRITICAL				)  # remove noisy download output from tracebacks
											return cls







						@slow
						@require_torch_gpu
						def 							_UpperCamelCase     (self				)      ->    Any:
											__lowercase        =      list_models()
											__lowercase        =      '''sshleifer'''
											__lowercase        =      [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_				)]
											__lowercase        =      ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
											__lowercase        =      []
											for m in model_ids:
																if m in allowed_to_be_broken:
																					continue
																try:
																					AutoConfig.from_pretrained(UpperCAmelCase_				)
																except Exception:
																					failures.append(UpperCAmelCase_				)
											assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
											self._test_distiller_cli(UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      {
											    '''student_encoder_layers''': 2,
											    '''student_decoder_layers''': 1,
											    '''max_epochs''': 4,
											    '''val_check_interval''': 0.2_5,
											    '''alpha_hid''': 2.0,
											    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
											}
											__lowercase        =      self._test_distiller_cli(UpperCAmelCase_  ,				check_contents=UpperCAmelCase_				)

											__lowercase        =      list(Path(model.output_dir				).glob('''*.ckpt'''				)				)
											self.assertEqual(1  ,				len(UpperCAmelCase_				)				)
											__lowercase        =      list(Path(model.output_dir				).glob('''**/*.bin'''				)				)
											self.assertEqual(len(UpperCAmelCase_				)  ,				2				)
											__lowercase        =      lmap(str.strip  ,				Path(model.hparams.data_dir				).joinpath('''test.source'''				).open().readlines()				)
											__lowercase        =      tempfile.mktemp()  # XXX: not being cleaned up
											generate_summaries_or_translations(UpperCAmelCase_  ,				UpperCAmelCase_  ,				str(model.output_dir / '''best_tfmr'''				)				)
											self.assertTrue(Path(UpperCAmelCase_				).exists()				)

											__lowercase        =      self.get_auto_remove_tmp_dir()
											convert_pl_to_hf(ckpts[0]  ,				transformer_ckpts[0].parent  ,				UpperCAmelCase_				)
											assert os.path.exists(os.path.join(UpperCAmelCase_  ,				'''pytorch_model.bin'''				)				)







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_				)
											__lowercase    ,			__lowercase        =      model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
											__lowercase        =      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]  ,				dtype=torch.long  ,				device=model.device				)
											__lowercase        =      target_ids[:, :-1].contiguous()  # Why this line?
											__lowercase        =      target_ids[:, 1:].clone()  # why clone?
											__lowercase        =      model(
											    UpperCAmelCase_  ,				attention_mask=UpperCAmelCase_  ,				decoder_input_ids=UpperCAmelCase_  ,				labels=UpperCAmelCase_  ,				use_cache=UpperCAmelCase_				).loss

											__lowercase        =      model(UpperCAmelCase_  ,				attention_mask=UpperCAmelCase_  ,				decoder_input_ids=UpperCAmelCase_  ,				use_cache=UpperCAmelCase_				).logits

											__lowercase        =      nn.functional.log_softmax(UpperCAmelCase_  ,				dim=-1				)
											__lowercase    ,			__lowercase        =      label_smoothed_nll_loss(
											    UpperCAmelCase_  ,				UpperCAmelCase_  ,				0.1  ,				ignore_index=model.config.pad_token_id				)
											with self.assertRaises(UpperCAmelCase_				):
																# TODO: understand why this breaks
																self.assertEqual(UpperCAmelCase_  ,				UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      {
											    '''student_encoder_layers''': 2,
											    '''student_decoder_layers''': 1,
											    '''num_train_epochs''': 4,
											    '''val_check_interval''': 0.2_5,
											    '''alpha_hid''': 2.0,
											    '''task''': '''translation''',
											    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
											    '''tokenizer_name''': MBART_TINY,
											    '''teacher''': MBART_TINY,
											    '''src_lang''': '''en_XX''',
											    '''tgt_lang''': '''ro_RO''',
											}
											__lowercase        =      self._test_distiller_cli(UpperCAmelCase_  ,				check_contents=UpperCAmelCase_				)
											assert model.model.config.model_type == "mbart"

											__lowercase        =      list(Path(model.output_dir				).glob('''*.ckpt'''				)				)
											self.assertEqual(1  ,				len(UpperCAmelCase_				)				)
											__lowercase        =      list(Path(model.output_dir				).glob('''**/*.bin'''				)				)
											__lowercase        =      list(Path(model.output_dir				).glob('''best_tfmr/*'''				)				)
											assert len(UpperCAmelCase_				) > 2
											self.assertEqual(len(UpperCAmelCase_				)  ,				2				)







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											__lowercase        =      {
											    '''student_encoder_layers''': 1,
											    '''student_decoder_layers''': 1,
											    '''alpha_hid''': 2.0,
											    '''teacher''': T5_TINY,
											    '''model_name_or_path''': T5_TINY,
											    '''tokenizer_name''': T5_TINY,
											}
											self._test_distiller_cli(UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      {
											    '''teacher''': T5_TINY,
											    '''student''': T5_TINIER,
											    '''model_name_or_path''': T5_TINIER,
											    '''tokenizer_name''': T5_TINIER,
											}
											self._test_distiller_cli(UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=True				)      ->    Optional[int]:
											__lowercase        =      {
											    '''label_smoothing''': 0.0,
											    '''early_stopping_patience''': -1,
											    '''train_batch_size''': 1,
											    '''eval_batch_size''': 2,
											    '''max_epochs''': 2,
											    '''alpha_mlm''': 0.2,
											    '''alpha_ce''': 0.8,
											    '''do_predict''': True,
											    '''model_name_or_path''': '''sshleifer/tinier_bart''',
											    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
											    '''val_check_interval''': 0.5,
											}
											default_updates.update(UpperCAmelCase_				)
											__lowercase        =      CHEAP_ARGS.copy()
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase        =      self.get_auto_remove_tmp_dir()

											args_d.update(data_dir=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				**UpperCAmelCase_				)
											__lowercase        =      distill_main(argparse.Namespace(**UpperCAmelCase_				)				)
											if not check_contents:
																return model
											__lowercase        =      os.listdir(UpperCAmelCase_				)
											__lowercase        =      {os.path.basename(UpperCAmelCase_				) for p in contents}
											__lowercase        =      [p for p in contents if p.endswith('''ckpt'''				)]
											assert len(UpperCAmelCase_				) > 0

											self.assertIn('''test_generations.txt'''  ,				UpperCAmelCase_				)
											self.assertIn('''test_results.txt'''  ,				UpperCAmelCase_				)

											__lowercase        =      load_json(model.metrics_save_path				)
											__lowercase        =      metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']  ,				0.0_1				)
											self.assertGreaterEqual(1.0  ,				last_step_stats['''val_avg_gen_time''']				)
											self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']  ,				UpperCAmelCase_				)
											__lowercase        =      int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1				)
											self.assertEqual(len(metrics['''val''']				)  ,				UpperCAmelCase_				)
											self.assertEqual(len(metrics['''test''']				)  ,				1				)
											return model



class       _snake_case  (    UpperCamelCase							):







						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]  ,				)
						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    Tuple:
											__lowercase        =      CHEAP_ARGS.copy()
											__lowercase        =      '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
											__lowercase        =      0.1 if task == '''translation''' else 0

											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase        =      self.get_auto_remove_tmp_dir()
											args_d.update(
											    data_dir=UpperCAmelCase_  ,				model_name_or_path=UpperCAmelCase_  ,				tokenizer_name=UpperCAmelCase_  ,				train_batch_size=2  ,				eval_batch_size=2  ,				output_dir=UpperCAmelCase_  ,				do_predict=UpperCAmelCase_  ,				task=UpperCAmelCase_  ,				src_lang='''en_XX'''  ,				tgt_lang='''ro_RO'''  ,				freeze_encoder=UpperCAmelCase_  ,				freeze_embeds=UpperCAmelCase_  ,				)
											assert "n_train" in args_d
											__lowercase        =      argparse.Namespace(**UpperCAmelCase_				)
											__lowercase        =      main(UpperCAmelCase_				)

											__lowercase        =      module.model.get_input_embeddings()
											assert not input_embeds.weight.requires_grad
											if model == T5_TINY:
																__lowercase        =      module.model.lm_head
																assert not lm_head.weight.requires_grad
																assert (lm_head.weight == input_embeds.weight).all().item()
											elif model == FSMT_TINY:
																__lowercase        =      module.model.model
																__lowercase        =      fsmt.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not fsmt.decoder.embed_tokens.weight.requires_grad
																# check that embeds are not the same
																assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
											else:
																__lowercase        =      module.model.model
																__lowercase        =      bart.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not bart.shared.weight.requires_grad
																# check that embeds are the same
																assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
																assert bart.decoder.embed_tokens == bart.shared

											__lowercase        =      load_json(module.output_dir / '''text_batch.json'''				)
											assert isinstance(UpperCAmelCase_  ,				UpperCAmelCase_				)
											assert len(UpperCAmelCase_				) >= 4







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											__lowercase        =      CHEAP_ARGS.copy()

											__lowercase        =      '''summarization'''
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)

											args_d.update(
											    data_dir=UpperCAmelCase_  ,				tokenizer_name=UpperCAmelCase_  ,				train_batch_size=2  ,				eval_batch_size=2  ,				do_predict=UpperCAmelCase_  ,				task=UpperCAmelCase_  ,				src_lang='''en_XX'''  ,				tgt_lang='''ro_RO'''  ,				freeze_encoder=UpperCAmelCase_  ,				freeze_embeds=UpperCAmelCase_  ,				)

											# test models whose config includes the extra_model_args
											__lowercase        =      BART_TINY
											__lowercase        =      self.get_auto_remove_tmp_dir()
											__lowercase        =      args_d.copy()
											args_da.update(
											    model_name_or_path=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				)
											__lowercase        =      ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
											for p in extra_model_params:
																__lowercase        =      0.5
											__lowercase        =      argparse.Namespace(**UpperCAmelCase_				)
											__lowercase        =      main(UpperCAmelCase_				)
											for p in extra_model_params:
																assert getattr(model.config  ,				UpperCAmelCase_				) == 0.5, f'''failed to override the model config for param {p}'''

											# test models whose config doesn't include the extra_model_args
											__lowercase        =      T5_TINY
											__lowercase        =      self.get_auto_remove_tmp_dir()
											__lowercase        =      args_d.copy()
											args_da.update(
											    model_name_or_path=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				)
											__lowercase        =      '''encoder_layerdrop'''
											__lowercase        =      0.5
											__lowercase        =      argparse.Namespace(**UpperCAmelCase_				)
											with pytest.raises(UpperCAmelCase_				) as excinfo:
																__lowercase        =      main(UpperCAmelCase_				)
											assert str(excinfo.value				) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      CHEAP_ARGS.copy()

											__lowercase        =      '''summarization'''
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)

											__lowercase        =      BART_TINY
											__lowercase        =      self.get_auto_remove_tmp_dir()

											args_d.update(
											    data_dir=UpperCAmelCase_  ,				model_name_or_path=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				tokenizer_name=UpperCAmelCase_  ,				train_batch_size=2  ,				eval_batch_size=2  ,				do_predict=UpperCAmelCase_  ,				task=UpperCAmelCase_  ,				src_lang='''en_XX'''  ,				tgt_lang='''ro_RO'''  ,				freeze_encoder=UpperCAmelCase_  ,				freeze_embeds=UpperCAmelCase_  ,				)

											# emulate finetune.py
											__lowercase        =      argparse.ArgumentParser()
											__lowercase        =      pl.Trainer.add_argparse_args(UpperCAmelCase_				)
											__lowercase        =      SummarizationModule.add_model_specific_args(UpperCAmelCase_  ,				os.getcwd()				)
											__lowercase        =      {'''--help''': True}

											# --help test
											with pytest.raises(UpperCAmelCase_				) as excinfo:
																with CaptureStdout() as cs:
																					__lowercase        =      parser.parse_args(UpperCAmelCase_				)
																assert False, "--help is expected to sys.exit"
											assert excinfo.type == SystemExit
											__lowercase        =      lightning_base.arg_to_scheduler_metavar
											assert expected in cs.out, "--help is expected to list the supported schedulers"

											# --lr_scheduler=non_existing_scheduler test
											__lowercase        =      '''non_existing_scheduler'''
											__lowercase        =      {f'''--lr_scheduler={unsupported_param}'''}
											with pytest.raises(UpperCAmelCase_				) as excinfo:
																with CaptureStderr() as cs:
																					__lowercase        =      parser.parse_args(UpperCAmelCase_				)
																assert False, "invalid argument is expected to sys.exit"
											assert excinfo.type == SystemExit
											__lowercase        =      f'''invalid choice: \'{unsupported_param}\''''
											assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

											# --lr_scheduler=existing_scheduler test
											__lowercase        =      '''cosine'''
											__lowercase        =      args_d.copy()
											__lowercase        =      supported_param
											__lowercase        =      argparse.Namespace(**UpperCAmelCase_				)
											__lowercase        =      main(UpperCAmelCase_				)
											assert (
											    getattr(model.hparams  ,				'''lr_scheduler'''				) == supported_param
											), f'''lr_scheduler={supported_param} shouldn\'t fail'''





"""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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__			:						int							=			logging.get_logger(__name__)

a__			:						Union[str, Any]							=			{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """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 					__UpperCamelCase (     a__:			str			,						a__:			Any			,						a__:			Dict			,						a__:			List[str]			,						a__:			str      )					->      str:




					"""simple docstring"""



					for attribute in key.split('''.'''      ):
										__lowercase        =      getattr(a__			,						a__      )

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

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





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			Optional[int]			,						a__:			Optional[Any]      )					->      Any:




					"""simple docstring"""



					__lowercase        =      []
					__lowercase        =      fairseq_model.state_dict()

					__lowercase        =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__lowercase        =      False
										if "conv_layers" in name:
															load_conv_layer(
															    a__			,						a__			,						a__			,						a__			,						hf_model.config.feat_extract_norm == '''group'''			,						)
															__lowercase        =      True
										else:
															for key, mapped_key in MAPPING.items():
																				__lowercase        =      '''sew_d.''' + 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        =      True
																									if "*" in mapped_key:
																														__lowercase        =      name.split(a__      )[0].split('''.'''      )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														__lowercase        =      mapped_key.replace('''*'''			,						a__      )
																									if "weight_g" in name:
																														__lowercase        =      '''weight_g'''
																									elif "weight_v" in name:
																														__lowercase        =      '''weight_v'''
																									elif "weight" in name:
																														__lowercase        =      '''weight'''
																									elif "bias" in name:
																														__lowercase        =      '''bias'''
																									else:
																														__lowercase        =      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 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			int			,						a__:			List[Any]			,						a__:			int			,						a__:			List[str]      )					->      Optional[Any]:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			List[str]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      SEWDConfig()
					if is_finetuned:
										__lowercase        =      model.wav_encoder.wav_model.cfg
					else:
										__lowercase        =      model.cfg

					__lowercase        =      fs_config.conv_bias
					__lowercase        =      eval(fs_config.conv_feature_layers      )
					__lowercase        =      [x[0] for x in conv_layers]
					__lowercase        =      [x[1] for x in conv_layers]
					__lowercase        =      [x[2] for x in conv_layers]
					__lowercase        =      '''gelu'''
					__lowercase        =      '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
					__lowercase        =      0.0
					__lowercase        =      fs_config.activation_fn.name
					__lowercase        =      fs_config.encoder_embed_dim
					__lowercase        =      0.02
					__lowercase        =      fs_config.encoder_ffn_embed_dim
					__lowercase        =      1e-5
					__lowercase        =      fs_config.encoder_layerdrop
					__lowercase        =      fs_config.encoder_attention_heads
					__lowercase        =      fs_config.conv_pos_groups
					__lowercase        =      fs_config.conv_pos
					__lowercase        =      len(a__      )
					__lowercase        =      fs_config.encoder_layers
					__lowercase        =      fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					__lowercase        =      fs_config.max_position_embeddings
					__lowercase        =      fs_config.position_buckets
					__lowercase        =      fs_config.share_att_key
					__lowercase        =      fs_config.relative_attention
					__lowercase        =      fs_config.position_biased_input
					__lowercase        =      tuple(fs_config.pos_att_type.split('''|'''      )      )
					__lowercase        =      fs_config.norm_rel_ebd

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										__lowercase        =      model.cfg
										__lowercase        =      fs_config.final_dropout
										__lowercase        =      fs_config.layerdrop
					__lowercase        =      fs_config.activation_dropout
					__lowercase        =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					__lowercase        =      fs_config.attention_dropout
					__lowercase        =      fs_config.dropout_input
					__lowercase        =      fs_config.dropout
					__lowercase        =      fs_config.mask_channel_length
					__lowercase        =      fs_config.mask_channel_prob
					__lowercase        =      fs_config.mask_length
					__lowercase        =      fs_config.mask_prob

					__lowercase        =      '''Wav2Vec2FeatureExtractor'''
					__lowercase        =      '''Wav2Vec2CTCTokenizer'''

					return config





@torch.no_grad()
def 					__UpperCamelCase (     a__:			str			,						a__:			str			,						a__:			Optional[int]=None			,						a__:			str=None			,						a__:			Union[str, Any]=True      )					->      List[Any]:




					"""simple docstring"""



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

					if config_path is not None:
										__lowercase        =      SEWDConfig.from_pretrained(a__      )
					else:
										__lowercase        =      convert_config(model[0]			,						a__      )
					__lowercase        =      model[0].eval()

					__lowercase        =      True if config.feat_extract_norm == '''layer''' else False
					__lowercase        =      WavaVecaFeatureExtractor(
					    feature_size=1			,						sampling_rate=1_60_00			,						padding_value=0			,						do_normalize=a__			,						return_attention_mask=a__			,						)

					if is_finetuned:
										if dict_path:
															__lowercase        =      Dictionary.load(a__      )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__lowercase        =      target_dict.pad_index
															__lowercase        =      target_dict.bos_index
															__lowercase        =      target_dict.pad_index
															__lowercase        =      target_dict.bos_index
															__lowercase        =      target_dict.eos_index
															__lowercase        =      len(target_dict.symbols      )
															__lowercase        =      os.path.join(a__			,						'''vocab.json'''      )
															if not os.path.isdir(a__      ):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__      )      )
																				return
															os.makedirs(a__			,						exist_ok=a__      )
															with open(a__			,						'''w'''			,						encoding='''utf-8'''      ) as vocab_handle:
																				json.dump(target_dict.indices			,						a__      )
															__lowercase        =      WavaVecaCTCTokenizer(
															    a__			,						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=a__			,						)
															__lowercase        =      WavaVecaProcessor(feature_extractor=a__			,						tokenizer=a__      )
															processor.save_pretrained(a__      )

										__lowercase        =      SEWDForCTC(a__      )
					else:
										__lowercase        =      SEWDModel(a__      )
										feature_extractor.save_pretrained(a__      )

					recursively_load_weights(a__			,						a__			,						a__      )

					hf_model.save_pretrained(a__      )


if __name__ == "__main__":
							a__			:						Optional[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("""--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"""
							)
							a__			:						Union[str, Any]							=			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)


