
"""simple docstring"""





import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				)
				from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=7							,						__a=True							,						__a=True							,						__a=True							,						__a=True							,						__a=99							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=1_28							,						__a=32							,						__a=16							,						__a=2							,						__a=0.02							,						__a=3							,						__a=4							,						__a=None							,						)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    seq_length
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_input_mask
							_UpperCamelCase									=    use_token_type_ids
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    vocab_size
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    max_position_embeddings
							_UpperCamelCase									=    type_vocab_size
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    num_labels
							_UpperCamelCase									=    num_choices
							_UpperCamelCase									=    scope



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size)

							_UpperCamelCase									=    None
							if self.use_input_mask:
												_UpperCamelCase									=    random_attention_mask([self.batch_size, self.seq_length])

							_UpperCamelCase									=    None
							if self.use_token_type_ids:
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size)

							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels)
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.num_choices)

							_UpperCamelCase									=    self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							return NezhaConfig(
							    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						is_decoder=__a							,						initializer_range=self.initializer_range							,						)



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






							'''simple docstring'''
							(
							    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    
							)									=    self.prepare_config_and_inputs()

							_UpperCamelCase									=    True
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
							_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						vocab_size=2)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))
							self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    True
							_UpperCamelCase									=    NezhaModel(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						encoder_hidden_states=__a							,						encoder_attention_mask=__a							,						)
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						encoder_hidden_states=__a							,						)
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))
							self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaForMaskedLM(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaForNextSentencePrediction(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a							,						)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, 2))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaForPreTraining(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a							,						next_sentence_label=__a							,						)
							self.parent.assertEqual(result.prediction_logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size))
							self.parent.assertEqual(result.seq_relationship_logits.shape							,						(self.batch_size, 2))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaForQuestionAnswering(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						start_positions=__a							,						end_positions=__a							,						)
							self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    NezhaForSequenceClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    NezhaForTokenClassification(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_choices
							_UpperCamelCase									=    NezhaForMultipleChoice(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    input_ids.unsqueeze(1).expand(-1							,						self.num_choices							,						-1).contiguous()
							_UpperCamelCase									=    token_type_ids.unsqueeze(1).expand(-1							,						self.num_choices							,						-1).contiguous()
							_UpperCamelCase									=    input_mask.unsqueeze(1).expand(-1							,						self.num_choices							,						-1).contiguous()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a							,						)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices))




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    
							)									=    config_and_inputs
							_UpperCamelCase									=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (
		        NezhaModel,
		        NezhaForMaskedLM,
		        NezhaForMultipleChoice,
		        NezhaForNextSentencePrediction,
		        NezhaForPreTraining,
		        NezhaForQuestionAnswering,
		        NezhaForSequenceClassification,
		        NezhaForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase__         = (
		    {
		        'feature-extraction': NezhaModel,
		        'fill-mask': NezhaForMaskedLM,
		        'question-answering': NezhaForQuestionAnswering,
		        'text-classification': NezhaForSequenceClassification,
		        'token-classification': NezhaForTokenClassification,
		        'zero-shot': NezhaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase__         = True



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a=False)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    super()._prepare_for_class(__a							,						__a							,						return_labels=__a)

							if return_labels:
												if model_class in get_values(__a):
																	_UpperCamelCase									=    torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)							,						dtype=torch.long							,						device=__a)
																	_UpperCamelCase									=    torch.zeros(
																	    self.model_tester.batch_size							,						dtype=torch.long							,						device=__a)
							return inputs_dict



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							# This regression test was failing with PyTorch < 1.3
							(
							    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    
							)									=    self.model_tester.prepare_config_and_inputs_for_decoder()

							_UpperCamelCase									=    None

							self.model_tester.create_and_check_model_as_decoder(
							    __a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_next_sequence_prediction(*__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__a)



		@slow
		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    NezhaModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




		@slow
		@require_torch_gpu
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
												# NezhaForMultipleChoice behaves incorrectly in JIT environments.
												if model_class == NezhaForMultipleChoice:
																	return

												_UpperCamelCase									=    True
												_UpperCamelCase									=    model_class(config=__a)

												_UpperCamelCase									=    self._prepare_for_class(__a							,						__a)
												_UpperCamelCase									=    torch.jit.trace(
												    __a							,						(inputs_dict['''input_ids'''].to('''cpu'''), inputs_dict['''attention_mask'''].to('''cpu''')))

												with tempfile.TemporaryDirectory() as tmp:
																	torch.jit.save(__a							,						os.path.join(__a							,						'''bert.pt'''))
																	_UpperCamelCase									=    torch.jit.load(os.path.join(__a							,						'''bert.pt''')							,						map_location=__a)
																	loaded(inputs_dict['''input_ids'''].to(__a)							,						inputs_dict['''attention_mask'''].to(__a))






@require_torch
class        _UpperCAmelCase(       unittest.TestCase      ):



		@slow
		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaModel.from_pretrained('''sijunhe/nezha-cn-base''')
							_UpperCamelCase									=    torch.tensor([[0, 1, 2, 3, 4, 5]])
							_UpperCamelCase									=    torch.tensor([[0, 1, 1, 1, 1, 1]])
							with torch.no_grad():
												_UpperCamelCase									=    model(__a							,						attention_mask=__a)[0]
							_UpperCamelCase									=    torch.Size((1, 6, 7_68))
							self.assertEqual(output.shape							,						__a)
							_UpperCamelCase									=    torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]])

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,						__a							,						atol=1e-4))




		@slow
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base''')
							_UpperCamelCase									=    torch.tensor([[0, 1, 2, 3, 4, 5]])
							_UpperCamelCase									=    torch.tensor([[1, 1, 1, 1, 1, 1]])
							with torch.no_grad():
												_UpperCamelCase									=    model(__a							,						attention_mask=__a)[0]
							_UpperCamelCase									=    torch.Size((1, 6, 2_11_28))
							self.assertEqual(output.shape							,						__a)
							_UpperCamelCase									=    torch.tensor(
							    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]])

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,						__a							,						atol=1e-4))







'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """encoder.embed_positions._float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  lowercase_     =       args.share_decoder_input_output_embed

  lowercase_     =       [int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
  lowercase_     =       SpeechaTextConfig(
      vocab_size=SCREAMING_SNAKE_CASE_ ,	max_source_positions=args.max_source_positions ,	max_target_positions=args.max_target_positions ,	encoder_layers=args.encoder_layers ,	decoder_layers=args.decoder_layers ,	encoder_attention_heads=args.encoder_attention_heads ,	decoder_attention_heads=args.decoder_attention_heads ,	encoder_ffn_dim=args.encoder_ffn_embed_dim ,	decoder_ffn_dim=args.decoder_ffn_embed_dim ,	d_model=args.encoder_embed_dim ,	dropout=args.dropout ,	attention_dropout=args.attention_dropout ,	activation_dropout=args.activation_dropout ,	activation_function="""relu""" ,	num_conv_layers=len(SCREAMING_SNAKE_CASE_						) ,	conv_channels=args.conv_channels ,	conv_kernel_sizes=SCREAMING_SNAKE_CASE_ ,	input_feat_per_channel=args.input_feat_per_channel ,	input_channels=args.input_channels ,	tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,	num_beams=5 ,	max_length=2_00 ,	use_cache=SCREAMING_SNAKE_CASE_ ,	decoder_start_token_id=2 ,	early_stopping=SCREAMING_SNAKE_CASE_ ,	)

  lowercase_     =       SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       model.model.load_state_dict(SCREAMING_SNAKE_CASE_ ,	strict=SCREAMING_SNAKE_CASE_						)
  if len(SCREAMING_SNAKE_CASE_						) > 0 and not set(SCREAMING_SNAKE_CASE_						) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
        f""" but all the following weights are missing {missing}"""						)

  if tie_embeds:
    lowercase_     =       make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    lowercase_     =       lm_head_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       __snake_case						  =		parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

