





import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel

class       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=13   , UpperCAmelCase_=7   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=99   , UpperCAmelCase_=32   , UpperCAmelCase_=5   , UpperCAmelCase_=4   , UpperCAmelCase_=4   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.1   , UpperCAmelCase_=True   , UpperCAmelCase_=512   , UpperCAmelCase_=16   , UpperCAmelCase_=2   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=3   , UpperCAmelCase_=4   , UpperCAmelCase_=None   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =seq_length
								lowerCamelCase	 =is_training
								lowerCamelCase	 =use_input_mask
								lowerCamelCase	 =use_token_type_ids
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =vocab_size
								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =intermediate_multiple_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =hidden_dropout
								lowerCamelCase	 =attention_dropout
								lowerCamelCase	 =weight_tying
								lowerCamelCase	 =max_position_embeddings
								lowerCamelCase	 =type_vocab_size
								lowerCamelCase	 =type_sequence_label_size
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =num_labels
								lowerCamelCase	 =num_choices
								lowerCamelCase	 =scope


				def _snake_case       (		self							):
								lowerCamelCase	 =ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size							)

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

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

								lowerCamelCase	 =self.get_config()

								return config, input_ids, input_mask, token_labels


				def _snake_case       (		self							):
								return GPTNeoXJapaneseConfig(
								    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_multiple_size=self.intermediate_multiple_size   , hidden_act=self.hidden_act   , hidden_dropout=self.hidden_dropout   , attention_dropout=self.attention_dropout   , weight_tying=self.weight_tying   , max_position_embeddings=self.max_position_embeddings   , type_vocab_size=self.type_vocab_size   , is_decoder=UpperCAmelCase_   , initializer_range=self.initializer_range   , )


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.prepare_config_and_inputs()

								lowerCamelCase	 =True

								return config, input_ids, input_mask, token_labels


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =GPTNeoXJapaneseModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =True
								lowerCamelCase	 =GPTNeoXJapaneseModel(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =True
								lowerCamelCase	 =GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								# first forward pass
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , use_cache=UpperCAmelCase_							)
								lowerCamelCase	 =outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowerCamelCase	 =ids_tensor((self.batch_size, 3)   , config.vocab_size							)
								lowerCamelCase	 =ids_tensor((self.batch_size, 3)   , vocab_size=2							)

								# append to next input_ids and
								lowerCamelCase	 =torch.cat([input_ids, next_tokens]   , dim=-1							)
								lowerCamelCase	 =torch.cat([input_mask, next_mask]   , dim=-1							)

								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , output_hidden_states=UpperCAmelCase_							)
								lowerCamelCase	 =output_from_no_past["""hidden_states"""][0]
								lowerCamelCase	 =model(
								    UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , past_key_values=UpperCAmelCase_   , output_hidden_states=UpperCAmelCase_   , )["""hidden_states"""][0]

								# select random slice
								lowerCamelCase	 =ids_tensor((1,)   , output_from_past.shape[-1]							).item()
								lowerCamelCase	 =output_from_no_past[:, -3:, random_slice_idx].detach()
								lowerCamelCase	 =output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(UpperCAmelCase_   , UpperCAmelCase_   , atol=1E-3							)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					unittest.TestCase    ):
				__A      =				(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
				__A      =				(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
				__A      =				(
				    {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
				    if is_torch_available()
				    else {}
				)
				__A      =				False
				__A      =				False
				__A      =				False
				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =GPTNeoXJapaneseModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=UpperCAmelCase_   , hidden_size=37							)


				def _snake_case       (		self							):
								self.config_tester.run_common_tests()


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								# This regression test was failing with PyTorch < 1.3
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_decoder()

								lowerCamelCase	 =None

								self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase_							)


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 ="""abeja/gpt-neox-japanese-2.7b"""

								lowerCamelCase	 =["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]

								lowerCamelCase	 =[
								    """データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
								    """100年後に必要とされる会社は、「人」が中心の会社です。""",
								    """フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
								    """国境の長いトンネルを抜けると、そこは雪国だった。""",
								    """美味しい日本食といえば、やっぱりお寿司ですよね。""",
								]

								lowerCamelCase	 =GPTNeoXJapaneseTokenizer.from_pretrained(UpperCAmelCase_							)
								lowerCamelCase	 =GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCAmelCase_							)

								lowerCamelCase	 =[]
								for prompt in prompts:
												lowerCamelCase	 =tokenizer(UpperCAmelCase_   , return_tensors="""pt"""							).input_ids
												lowerCamelCase	 =model.generate(UpperCAmelCase_   , max_length=50							)
												lowerCamelCase	 =tokenizer.batch_decode(UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_							)
												predicted_outputs += generated_string
								self.assertListEqual(UpperCAmelCase_   , UpperCAmelCase_							)












import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
				# so we check the torch version for backwards compatibility
				if is_torch_less_than_1_11:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        False,
				    )			,      output_path=output_path / """vae_decoder""" / """model.onnx"""			,      ordered_input_names=["""latent_sample""", """return_dict"""]			,      output_names=["""sample"""]			,      dynamic_axes={
				        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
				    }			,      opset=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_path''',
							    type=str,
							    required=True,
							    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
							)

							parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
							parser.add_argument(
							    '''--opset''',
							    default=14,
							    type=int,
							    help='''The version of the ONNX operator set to use.''',
							)
							parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

							UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





