"""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()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Optional[int]   =   {
    '''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       A__    (	lowercase_     :	Tuple      , lowercase_     :	str      , lowercase_     :	Union[str, Any]      , lowercase_     :	Any      , lowercase_     :	Tuple						)  ->							Dict:
   for attribute in key.split('.'						):
      __lowerCamelCase    		=					getattr(lowercase_      , lowercase_						)

   if weight_type is not None:
      __lowerCamelCase    		=					getattr(lowercase_      , lowercase_						).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__    (	lowercase_     :	Dict      , lowercase_     :	Union[str, Any]      , lowercase_     :	int						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					[]
   __lowerCamelCase    		=					fairseq_model.state_dict()

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

   for name, value in fairseq_dict.items():
      __lowerCamelCase    		=					False
      if "conv_layers" in name:
         load_conv_layer(
             lowercase_      , lowercase_      , lowercase_      , lowercase_      , hf_model.config.feat_extract_norm == 'group'      , )
         __lowerCamelCase    		=					True
      else:
         for key, mapped_key in MAPPING.items():
            __lowerCamelCase    		=					'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]:
               __lowerCamelCase    		=					True
               if "*" in mapped_key:
                  __lowerCamelCase    		=					name.split(lowercase_						)[0].split('.'						)[-2]
                  if not layer_index.isnumeric():
                     continue
                  __lowerCamelCase    		=					mapped_key.replace('*'      , lowercase_						)
               if "weight_g" in name:
                  __lowerCamelCase    		=					'weight_g'
               elif "weight_v" in name:
                  __lowerCamelCase    		=					'weight_v'
               elif "weight" in name:
                  __lowerCamelCase    		=					'weight'
               elif "bias" in name:
                  __lowerCamelCase    		=					'bias'
               else:
                  __lowerCamelCase    		=					None
               set_recursively(lowercase_      , lowercase_      , lowercase_      , lowercase_      , lowercase_						)
            continue
      if not is_used:
         unused_weights.append(lowercase_						)

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



def       A__    (	lowercase_     :	List[Any]      , lowercase_     :	int      , lowercase_     :	List[Any]      , lowercase_     :	int      , lowercase_     :	Union[str, Any]						)  ->							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(lowercase_						)



def       A__    (	lowercase_     :	Dict      , lowercase_     :	int						)  ->							Optional[int]:
   __lowerCamelCase    		=					SEWDConfig()
   if is_finetuned:
      __lowerCamelCase    		=					model.wav_encoder.wav_model.cfg
   else:
      __lowerCamelCase    		=					model.cfg

   __lowerCamelCase    		=					fs_config.conv_bias
   __lowerCamelCase    		=					eval(fs_config.conv_feature_layers						)
   __lowerCamelCase    		=					[x[0] for x in conv_layers]
   __lowerCamelCase    		=					[x[1] for x in conv_layers]
   __lowerCamelCase    		=					[x[2] for x in conv_layers]
   __lowerCamelCase    		=					'gelu'
   __lowerCamelCase    		=					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
   __lowerCamelCase    		=					0.0
   __lowerCamelCase    		=					fs_config.activation_fn.name
   __lowerCamelCase    		=					fs_config.encoder_embed_dim
   __lowerCamelCase    		=					0.0_2
   __lowerCamelCase    		=					fs_config.encoder_ffn_embed_dim
   __lowerCamelCase    		=					1e-5
   __lowerCamelCase    		=					fs_config.encoder_layerdrop
   __lowerCamelCase    		=					fs_config.encoder_attention_heads
   __lowerCamelCase    		=					fs_config.conv_pos_groups
   __lowerCamelCase    		=					fs_config.conv_pos
   __lowerCamelCase    		=					len(lowercase_						)
   __lowerCamelCase    		=					fs_config.encoder_layers
   __lowerCamelCase    		=					fs_config.squeeze_factor
   # DeBERTa-specific parameters:
   __lowerCamelCase    		=					fs_config.max_position_embeddings
   __lowerCamelCase    		=					fs_config.position_buckets
   __lowerCamelCase    		=					fs_config.share_att_key
   __lowerCamelCase    		=					fs_config.relative_attention
   __lowerCamelCase    		=					fs_config.position_biased_input
   __lowerCamelCase    		=					tuple(fs_config.pos_att_type.split('|'						)						)
   __lowerCamelCase    		=					fs_config.norm_rel_ebd

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

   __lowerCamelCase    		=					'Wav2Vec2FeatureExtractor'
   __lowerCamelCase    		=					'Wav2Vec2CTCTokenizer'

   return config



@torch.no_grad()
def       A__    (	lowercase_     :	Any      , lowercase_     :	Tuple      , lowercase_     :	Dict=None      , lowercase_     :	Any=None      , lowercase_     :	Optional[int]=True						)  ->							Optional[int]:

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

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

   __lowerCamelCase    		=					True if config.feat_extract_norm == 'layer' else False
   __lowerCamelCase    		=					WavaVecaFeatureExtractor(
       feature_size=1      , sampling_rate=16000      , padding_value=0      , do_normalize=lowercase_      , return_attention_mask=lowercase_      , )

   if is_finetuned:
      if dict_path:
         __lowerCamelCase    		=					Dictionary.load(lowercase_						)

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

      __lowerCamelCase    		=					SEWDForCTC(lowercase_						)
   else:
      __lowerCamelCase    		=					SEWDModel(lowercase_						)
      feature_extractor.save_pretrained(lowercase_						)

   recursively_load_weights(lowercase_      , lowercase_      , lowercase_						)

   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		List[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'''
     )
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )














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       =    logging.getLogger()
_a       =    torch.cuda.is_available()
_a       =    {
    '''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 __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						_UpperCAmelCase							   =     '\n'.join(__lowerCAmelCase      )
						Path(__lowerCAmelCase      ).open('w'      ).writelines(__lowerCAmelCase      )


_a       =    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_a       =    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_a       =    '''patrickvonplaten/t5-tiny-random'''
_a       =    '''sshleifer/t5-tinier-random'''
_a       =    '''sshleifer/bart-tiny-random'''
_a       =    '''sshleifer/tiny-mbart'''
_a       =    '''sshleifer/tiny-marian-en-de'''
_a       =    '''stas/tiny-wmt19-en-de'''


_a       =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def __A       ( __lowerCAmelCase      )->    Tuple:





						"""simple docstring"""



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





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@classmethod
	def 			UpperCamelCase       (				cls       ):
							"""simple docstring"""







							logging.disable(logging.CRITICAL       )  # remove noisy download output from tracebacks
							return cls

	@slow
	@require_torch_gpu
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     list_models()
							_UpperCAmelCase							   =     'sshleifer'
							_UpperCAmelCase							   =     [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase       )]
							_UpperCAmelCase							   =     ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
							_UpperCAmelCase							   =     []
							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       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'max_epochs': 4,
							    'val_check_interval': 0.25,
							    'alpha_hid': 2.0,
							    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )

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

							_UpperCAmelCase							   =     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       ):
							"""simple docstring"""







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

							_UpperCAmelCase							   =     model(UpperCAmelCase			,       attention_mask=UpperCAmelCase			,       decoder_input_ids=UpperCAmelCase			,       use_cache=UpperCAmelCase       ).logits

							_UpperCAmelCase							   =     nn.functional.log_softmax(UpperCAmelCase			,       dim=-1       )
							_UpperCAmelCase   ,					_UpperCAmelCase							   =     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       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'num_train_epochs': 4,
							    'val_check_interval': 0.25,
							    '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',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )
							assert model.model.config.model_type == "mbart"

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    '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       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    '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       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    '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       )
							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       **UpperCAmelCase       )
							_UpperCAmelCase							   =     distill_main(argparse.Namespace(**UpperCAmelCase       )       )
							if not check_contents:
													return model
							_UpperCAmelCase							   =     os.listdir(UpperCAmelCase       )
							_UpperCAmelCase							   =     {os.path.basename(UpperCAmelCase       ) for p in contents}
							_UpperCAmelCase							   =     [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       )

							_UpperCAmelCase							   =     load_json(model.metrics_save_path       )
							_UpperCAmelCase							   =     metrics['val'][-1]
							self.assertGreaterEqual(last_step_stats['val_avg_gen_time']			,       0.01       )
							self.assertGreaterEqual(1.0			,       last_step_stats['val_avg_gen_time']       )
							self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""]			,       UpperCAmelCase       )
							_UpperCAmelCase							   =     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 __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,       )
	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
							_UpperCAmelCase							   =     0.1 if task == 'translation' else 0

							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )

							_UpperCAmelCase							   =     module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
													_UpperCAmelCase							   =     module.model.lm_head
													assert not lm_head.weight.requires_grad
													assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     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:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     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

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

							_UpperCAmelCase							   =     'summarization'
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
													_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     T5_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     'encoder_layerdrop'
							_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert str(excinfo.value       ) == F"""model config doesn't have a `{unsupported_param}` attribute"""

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

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

							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     argparse.ArgumentParser()
							_UpperCAmelCase							   =     pl.Trainer.add_argparse_args(UpperCAmelCase       )
							_UpperCAmelCase							   =     SummarizationModule.add_model_specific_args(UpperCAmelCase			,       os.getcwd()       )
							_UpperCAmelCase							   =     {'--help': True}

							# --help test
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStdout() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     'non_existing_scheduler'
							_UpperCAmelCase							   =     {F"""--lr_scheduler={unsupported_param}"""}
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStderr() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     'cosine'
							_UpperCAmelCase							   =     args_d.copy()
							_UpperCAmelCase							   =     supported_param
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert (
							    getattr(model.hparams			,       'lr_scheduler'       ) == supported_param
							), F"""lr_scheduler={supported_param} shouldn't fail"""
