


'''simple docstring'''



import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any   ):
					__a			:       Optional[Any]					    =  [
					    '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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Optional[int]					    =  emb.weight.shape
					__a			:       Union[str, Any]					    =  nn.Linear(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      bias=SCREAMING_SNAKE_CASE_   )
					__a			:       Tuple					    =  emb.weight.data
					return lin_layer






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   List[str]=None   ):
					__a			:       Tuple					    =  {}
					for old_key in state_dict.keys():
										__a			:       Optional[Any]					    =  old_key
										if "moe_layer.experts." in key:
															if expert_idx is not None:
																				__a			:       str					    =  key.replace('moe_layer.experts.0'						,      F"""ffn.experts.expert_{expert_idx}"""   )
															else:
																				__a			:       str					    =  key.replace('moe_layer.experts.'						,      'ffn.experts.expert_'   )
										if "gate" in key:
															__a			:       Optional[int]					    =  key.replace('.moe_layer.gate.wg'						,      '.ffn.router.classifier'   )
										if "fc2" and "experts" not in key:
															__a			:       str					    =  key.replace('.fc2.'						,      '.ffn.fc2.'   )
										if "fc1" and "experts" not in key:
															__a			:       Tuple					    =  key.replace('.fc1.'						,      '.ffn.fc1.'   )
										if ".encoder_attn." in key:
															__a			:       List[Any]					    =  key.replace('.encoder_attn.'						,      '.cross_attention.'   )
										if "encoder_attn_layer_norm" in key:
															__a			:       Any					    =  key.replace('encoder_attn_layer_norm'						,      'cross_attention_layer_norm'   )
										if "final_layer_norm" in key:
															__a			:       Union[str, Any]					    =  key.replace('final_layer_norm'						,      'ff_layer_norm'   )
										__a			:       List[str]					    =  state_dict[old_key]
					return new_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   List[str] = WEIGHTS_NAME   ):
					__a			:       Dict					    =  []
					__a			:       Optional[Any]					    =  0
					os.makedirs(SCREAMING_SNAKE_CASE_						,      exist_ok=SCREAMING_SNAKE_CASE_   )

					for expert in range(SCREAMING_SNAKE_CASE_   ):
										__a			:       Tuple					    =  switch_checkpoint_path + F"""-rank-{expert}.pt"""
										if os.path.isfile(SCREAMING_SNAKE_CASE_   ):
															__a			:       str					    =  torch.load(SCREAMING_SNAKE_CASE_   )['model']
															remove_ignore_keys_(SCREAMING_SNAKE_CASE_   )
															__a			:       int					    =  rename_fairseq_keys(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
															__a			:       str					    =  os.path.join(
															    SCREAMING_SNAKE_CASE_						,      weights_name.replace('.bin'						,      F"""-{len(SCREAMING_SNAKE_CASE_   )+1:05d}-of-???.bin"""   )   )
															torch.save(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
															sharded_state_dicts.append(expert_state.keys()   )
															total_size += sum([value.numel() for key, value in expert_state.items()]   ) * dtype_byte_size(
															    expert_state[list(SCREAMING_SNAKE_CASE_   )[0]].dtype   )

    # Add the last block
					__a			:       Optional[int]					    =  os.path.join(SCREAMING_SNAKE_CASE_						,      weights_name.replace('.bin'						,      F"""-{len(SCREAMING_SNAKE_CASE_   )+1:05d}-of-???.bin"""   )   )
					__a			:       Optional[Any]					    =  torch.load(switch_checkpoint_path + '-shared.pt'   )['model']
					remove_ignore_keys_(SCREAMING_SNAKE_CASE_   )
					__a			:       int					    =  rename_fairseq_keys(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
					__a			:       int					    =  shared_weights['decoder.embed_tokens.weight']
					sharded_state_dicts.append(shared_weights.keys()   )

					# If we only have the shared weights (dummy model/experts saved on the same file)
					if len(SCREAMING_SNAKE_CASE_   ) == 1:
										__a			:       Optional[Any]					    =  os.path.join(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
										torch.save(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
										return {weights_name: sharded_state_dicts[0]}, None
					else:
										torch.save(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
					# Otherwise, let's build the index
					__a			:       Any					    =  {}
					for idx, shard in enumerate(SCREAMING_SNAKE_CASE_   ):
										__a			:       Any					    =  weights_name.replace('.bin'						,      F"""-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_   ):05d}.bin"""   )
										__a			:       Optional[Any]					    =  os.path.join(SCREAMING_SNAKE_CASE_						,      weights_name.replace('.bin'						,      F"""-{idx+1:05d}-of-???.bin"""   )   )
										os.rename(SCREAMING_SNAKE_CASE_						,      os.path.join(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )   )
										for key in shard:
															__a			:       int					    =  shard_file

    # Add the metadata
					__a			:       Any					    =  {'total_size': total_size}
					__a			:       str					    =  {'metadata': metadata, 'weight_map': weight_map}

					with open(os.path.join(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )						,      'w'						,      encoding='utf-8'   ) as f:
										__a			:       List[Any]					    =  json.dumps(SCREAMING_SNAKE_CASE_						,      indent=2						,      sort_keys=SCREAMING_SNAKE_CASE_   ) + '\n'
										f.write(SCREAMING_SNAKE_CASE_   )

					return metadata, index


if __name__ == "__main__":
	__lowercase	:						Optional[Any]       =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--nllb_moe_checkpoint_path',
	    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
	    type=str,
	    required=False,
	    help='Path to a directory containing a folder per layer. Follows the original Google format.',
	)
	parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
	    type=str,
	    required=False,
	    help='Path to the output pytorch model.',
	)
	__lowercase	:						Any       =	parser.parse_args()
	__lowercase       ,      __lowercase	:						List[str]       =	shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    1_28,
	    args.dtype,
	)

	__lowercase	:						int       =	NllbMoeConfig.from_pretrained(
	    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
	)
	config.save_pretrained(args.pytorch_dump_folder_path)
	__lowercase	:						Optional[int]       =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print('Done')
	model.save_pretrained(args.pytorch_dump_folder_path)





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      A__							:






				def __init__(						self					:      str    ,     a					:      Optional[Any]    ,     a					:      int=13    ,     a					:      str=7    ,     a					:      str=True    ,     a					:      List[str]=True    ,     a					:      Optional[Any]=True    ,     a					:      int=True    ,     a					:      List[Any]=99    ,     a					:      List[Any]=32    ,     a					:      Tuple=5    ,     a					:      Any=4    ,     a					:      Optional[int]=37    ,     a					:      Tuple="gelu"    ,     a					:      Any=0.1    ,     a					:      int=0.1    ,     a					:      List[Any]=128    ,     a					:      Union[str, Any]=32    ,     a					:      Union[str, Any]=16    ,     a					:      Dict=2    ,     a					:      List[Any]=0.0_2    ,     a					:      Optional[Any]=3    ,     a					:      List[Any]=4    ,     a					:      Optional[int]=None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		parent
							lowerCAmelCase__      :      Dict         =		batch_size
							lowerCAmelCase__      :      Optional[Any]         =		seq_length
							lowerCAmelCase__      :      Optional[Any]         =		is_training
							lowerCAmelCase__      :      Union[str, Any]         =		use_input_mask
							lowerCAmelCase__      :      List[Any]         =		use_token_type_ids
							lowerCAmelCase__      :      str         =		use_labels
							lowerCAmelCase__      :      Optional[Any]         =		vocab_size
							lowerCAmelCase__      :      Union[str, Any]         =		hidden_size
							lowerCAmelCase__      :      Optional[int]         =		num_hidden_layers
							lowerCAmelCase__      :      Optional[int]         =		num_attention_heads
							lowerCAmelCase__      :      List[Any]         =		intermediate_size
							lowerCAmelCase__      :      List[str]         =		hidden_act
							lowerCAmelCase__      :      List[Any]         =		hidden_dropout_prob
							lowerCAmelCase__      :      Optional[int]         =		attention_probs_dropout_prob
							lowerCAmelCase__      :      Dict         =		max_position_embeddings
							lowerCAmelCase__      :      Any         =		type_vocab_size
							lowerCAmelCase__      :      Any         =		type_sequence_label_size
							lowerCAmelCase__      :      List[Any]         =		initializer_range
							lowerCAmelCase__      :      Dict         =		num_labels
							lowerCAmelCase__      :      Any         =		num_choices
							lowerCAmelCase__      :      Union[str, Any]         =		scope






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size   )

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

							lowerCAmelCase__      :      Tuple         =		None
							if self.use_token_type_ids:
										lowerCAmelCase__      :      List[Any]         =		ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size   )

							lowerCAmelCase__      :      Optional[int]         =		None
							lowerCAmelCase__      :      Optional[Any]         =		None
							lowerCAmelCase__      :      Optional[int]         =		None
							if self.use_labels:
										lowerCAmelCase__      :      Any         =		ids_tensor([self.batch_size]    ,     self.type_sequence_label_size   )
										lowerCAmelCase__      :      Union[str, Any]         =		ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels   )
										lowerCAmelCase__      :      List[Any]         =		ids_tensor([self.batch_size]    ,     self.num_choices   )

							lowerCAmelCase__      :      Dict         =		self.get_config()

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






				def 							_lowerCamelCase		(						self					:      List[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 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							(
							    (
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				
							)      :      List[Any]         =		self.prepare_config_and_inputs()

							lowerCAmelCase__      :      List[Any]         =		True
							lowerCAmelCase__      :      Tuple         =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							lowerCAmelCase__      :      Tuple         =		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 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Optional[int]    ,     a					:      Tuple    ,     a					:      Optional[int]    ,     a					:      List[Any]    ,     a					:      Tuple    ,     a					:      List[str]    ,     a					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		NezhaModel(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Dict         =		model(a    ,     attention_mask=a    ,     token_type_ids=a   )
							lowerCAmelCase__      :      List[str]         =		model(a    ,     token_type_ids=a   )
							lowerCAmelCase__      :      Any         =		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 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Union[str, Any]    ,     a					:      Dict    ,     a					:      List[Any]    ,     a					:      Optional[Any]    ,     a					:      int    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Tuple    ,     a					:      List[str]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		True
							lowerCAmelCase__      :      Optional[int]         =		NezhaModel(a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Any         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     encoder_hidden_states=a    ,     encoder_attention_mask=a    ,     )
							lowerCAmelCase__      :      Dict         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     encoder_hidden_states=a    ,     )
							lowerCAmelCase__      :      List[str]         =		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 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      str    ,     a					:      List[str]    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		NezhaForMaskedLM(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Dict         =		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 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Optional[int]    ,     a					:      List[Any]    ,     a					:      int    ,     a					:      List[str]    ,     a					:      Union[str, Any]    ,     a					:      int    ,     a					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		NezhaForNextSentencePrediction(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      str         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a    ,     )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, 2)   )






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Optional[int]    ,     a					:      str    ,     a					:      List[str]    ,     a					:      int    ,     a					:      Dict    ,     a					:      Optional[Any]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		NezhaForPreTraining(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Optional[int]         =		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 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      Dict    ,     a					:      List[str]    ,     a					:      Any    ,     a					:      Any    ,     a					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		NezhaForQuestionAnswering(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Tuple         =		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 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      str    ,     a					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      Optional[Any]    ,     a					:      Dict    ,     a					:      str    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		self.num_labels
							lowerCAmelCase__      :      Optional[Any]         =		NezhaForSequenceClassification(a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Tuple         =		model(a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a   )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)   )






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      Dict    ,     a					:      str    ,     a					:      Optional[Any]    ,     a					:      Optional[int]    ,     a					:      List[str]    ,     a					:      Dict    ,     a					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.num_labels
							lowerCAmelCase__      :      str         =		NezhaForTokenClassification(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Any         =		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 							_lowerCamelCase		(						self					:      int    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Optional[int]    ,     a					:      Optional[int]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.num_choices
							lowerCAmelCase__      :      Any         =		NezhaForMultipleChoice(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      int         =		input_ids.unsqueeze(1   ).expand(-1    ,     self.num_choices    ,     -1   ).contiguous()
							lowerCAmelCase__      :      str         =		token_type_ids.unsqueeze(1   ).expand(-1    ,     self.num_choices    ,     -1   ).contiguous()
							lowerCAmelCase__      :      Optional[Any]         =		input_mask.unsqueeze(1   ).expand(-1    ,     self.num_choices    ,     -1   ).contiguous()
							lowerCAmelCase__      :      Any         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a    ,     )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_choices)   )






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				
							)      :      Any         =		config_and_inputs
							lowerCAmelCase__      :      str         =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class      A__							(     __magic_name__						,   __magic_name__						,   __magic_name__						,   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 							_lowerCamelCase		(						self					:      str    ,     a					:      Tuple    ,     a					:      int    ,     a					:      Dict=False   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		super()._prepare_for_class(a    ,     a    ,     return_labels=a   )

							if return_labels:
										if model_class in get_values(a   ):
													lowerCAmelCase__      :      Tuple         =		torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.seq_length)    ,     dtype=torch.long    ,     device=a   )
													lowerCAmelCase__      :      Optional[Any]         =		torch.zeros(
													    self.model_tester.batch_size    ,     dtype=torch.long    ,     device=a   )
							return inputs_dict






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		NezhaModelTester(self   )
							lowerCAmelCase__      :      Optional[int]         =		ConfigTester(self    ,     config_class=a    ,     hidden_size=37   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							self.config_tester.run_common_tests()






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							(
							    (
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				
							)      :      str         =		self.model_tester.prepare_config_and_inputs_for_decoder()

							lowerCAmelCase__      :      str         =		None

							self.model_tester.create_and_check_model_as_decoder(
							    a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*a   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_next_sequence_prediction(*a   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*a   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*a   )






				@slow
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__      :      Optional[Any]         =		NezhaModel.from_pretrained(a   )
										self.assertIsNotNone(a   )






				@slow
				@require_torch_gpu
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		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

										lowerCAmelCase__      :      Dict         =		True
										lowerCAmelCase__      :      Any         =		model_class(config=a   )

										lowerCAmelCase__      :      Union[str, Any]         =		self._prepare_for_class(a    ,     a   )
										lowerCAmelCase__      :      int         =		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'   )   )
													lowerCAmelCase__      :      Any         =		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      A__							(     unittest.TestCase	):






				@slow
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		NezhaModel.from_pretrained('sijunhe/nezha-cn-base'   )
							lowerCAmelCase__      :      Any         =		torch.tensor([[0, 1, 2, 3, 4, 5]]   )
							lowerCAmelCase__      :      Union[str, Any]         =		torch.tensor([[0, 1, 1, 1, 1, 1]]   )
							with torch.no_grad():
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     attention_mask=a   )[0]
							lowerCAmelCase__      :      Union[str, Any]         =		torch.Size((1, 6, 768)   )
							self.assertEqual(output.shape    ,     a   )
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]   )

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






				@slow
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'   )
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor([[0, 1, 2, 3, 4, 5]]   )
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor([[1, 1, 1, 1, 1, 1]]   )
							with torch.no_grad():
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     attention_mask=a   )[0]
							lowerCAmelCase__      :      int         =		torch.Size((1, 6, 21_128)   )
							self.assertEqual(output.shape    ,     a   )
							lowerCAmelCase__      :      List[Any]         =		torch.tensor(
							    [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]   )

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