
'''simple docstring'''
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=5_12					,   UpperCAmelCase=16					,   UpperCAmelCase=2					,   UpperCAmelCase=0.02					,   UpperCAmelCase=3					,   UpperCAmelCase=4					,   UpperCAmelCase=None					,   ):
				a_      				=						parent
				a_      				=						batch_size
				a_      				=						seq_length
				a_      				=						is_training
				a_      				=						use_input_mask
				a_      				=						use_token_type_ids
				a_      				=						use_labels
				a_      				=						vocab_size
				a_      				=						hidden_size
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						intermediate_multiple_size
				a_      				=						hidden_act
				a_      				=						hidden_dropout
				a_      				=						attention_dropout
				a_      				=						weight_tying
				a_      				=						max_position_embeddings
				a_      				=						type_vocab_size
				a_      				=						type_sequence_label_size
				a_      				=						initializer_range
				a_      				=						num_labels
				a_      				=						num_choices
				a_      				=						scope




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

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

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

				a_      				=						self.get_config()

				return config, input_ids, input_mask, token_labels




			def 					lowerCAmelCase__					(       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 					lowerCAmelCase__					(       self	):
				a_				,  a_				,  a_				,  a_      				=						self.prepare_config_and_inputs()

				a_      				=						True

				return config, input_ids, input_mask, token_labels




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						GPTNeoXJapaneseModel(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase	)
				self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						True
				a_      				=						GPTNeoXJapaneseModel(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)
				self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   labels=UpperCAmelCase	)
				self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						True
				a_      				=						GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()

				# first forward pass
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   use_cache=UpperCAmelCase	)
				a_      				=						outputs.past_key_values

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

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

				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   output_hidden_states=UpperCAmelCase	)
				a_      				=						output_from_no_past["""hidden_states"""][0]
				a_      				=						model(
				    UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   past_key_values=UpperCAmelCase					,   output_hidden_states=UpperCAmelCase					,   )["""hidden_states"""][0]

				# select random slice
				a_      				=						ids_tensor((1,)					,   output_from_past.shape[-1]	).item()
				a_      				=						output_from_no_past[:, -3:, random_slice_idx].detach()
				a_      				=						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 					lowerCAmelCase__					(       self	):
				a_      				=						self.prepare_config_and_inputs()
				a_				,  a_				,  a_				,  a_      				=						config_and_inputs
				a_      				=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
				return config, inputs_dict







@require_torch
class   a_ (      UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Union[str, Any]       =    (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
			lowerCamelCase__	:			Tuple       =    (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__	:			List[Any]       =    (
			    {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__	:			int       =    False
			lowerCamelCase__	:			Any       =    False
			lowerCamelCase__	:			int       =    False
			lowerCamelCase__	:			Tuple       =    False




			def 					lowerCAmelCase__					(       self	):
				a_      				=						GPTNeoXJapaneseModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase					,   hidden_size=37	)




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




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_				,  a_				,  a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_				,  a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)




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

				a_      				=						None

				self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_				,  a_				,  a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase	)






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

				a_      				=						["""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、"""]

				a_      				=						[
				    """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。""",
				]

				a_      				=						GPTNeoXJapaneseTokenizer.from_pretrained(UpperCAmelCase	)
				a_      				=						GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCAmelCase	)

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


'''simple docstring'''
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase__											=version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__=False  ,      ):
	output_path.parent.mkdir(parents=A__  ,      exist_ok=A__						)
	# 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(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      use_external_data_format=A__  ,      enable_onnx_checker=A__  ,      opset_version=A__  ,      )
	else:
		export(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      opset_version=A__  ,      )






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

	# VAE DECODER
	a_      				=						AutoencoderKL.from_pretrained(model_path + """/vae"""						)
	a_      				=						vae_decoder.config.latent_channels
	# forward only through the decoder part
	a_      				=						vae_decoder.decode
	onnx_export(
	    A__  ,      model_args=(
	        torch.randn(1  ,      A__  ,      25  ,      25						).to(device=A__  ,      dtype=A__						),
	        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=A__  ,      )
	del vae_decoder


if __name__ == "__main__":
		lowercase__											=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')

		lowercase__											=parser.parse_args()
		print(args.output_path)
		convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
		print('SD: Done: ONNX')
