
'''simple docstring'''
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__											=get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
		from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowercase__											=5_00_03
lowercase__											=5_00_02







@require_sentencepiece
@require_tokenizers
class   a_ (      UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Dict       =    PLBartTokenizer
			lowerCamelCase__	:			Dict       =    None
			lowerCamelCase__	:			List[str]       =    False




			def 					lowerCAmelCase__					(       self	):
				super().setUp()

				# We have a SentencePiece fixture for testing
				a_      				=						PLBartTokenizer(UpperCAmelCase					,   language_codes="""base"""					,   keep_accents=UpperCAmelCase	)
				tokenizer.save_pretrained(self.tmpdirname	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						PLBartTokenizer(UpperCAmelCase					,   language_codes="""base"""					,   keep_accents=UpperCAmelCase	)

				a_      				=						tokenizer.tokenize("""This is a test"""	)
				self.assertListEqual(UpperCAmelCase					,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(UpperCAmelCase	)					,   [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]					,   )

				a_      				=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        SPIECE_UNDERLINE + """I""",
				        SPIECE_UNDERLINE + """was""",
				        SPIECE_UNDERLINE + """b""",
				        """or""",
				        """n""",
				        SPIECE_UNDERLINE + """in""",
				        SPIECE_UNDERLINE + """""",
				        """9""",
				        """2""",
				        """0""",
				        """0""",
				        """0""",
				        """,""",
				        SPIECE_UNDERLINE + """and""",
				        SPIECE_UNDERLINE + """this""",
				        SPIECE_UNDERLINE + """is""",
				        SPIECE_UNDERLINE + """f""",
				        """al""",
				        """s""",
				        """é""",
				        """.""",
				    ]					,   )
				a_      				=						tokenizer.convert_tokens_to_ids(UpperCAmelCase	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]					,   )

				a_      				=						tokenizer.convert_ids_to_tokens(UpperCAmelCase	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        SPIECE_UNDERLINE + """I""",
				        SPIECE_UNDERLINE + """was""",
				        SPIECE_UNDERLINE + """b""",
				        """or""",
				        """n""",
				        SPIECE_UNDERLINE + """in""",
				        SPIECE_UNDERLINE + """""",
				        """<unk>""",
				        """2""",
				        """0""",
				        """0""",
				        """0""",
				        """,""",
				        SPIECE_UNDERLINE + """and""",
				        SPIECE_UNDERLINE + """this""",
				        SPIECE_UNDERLINE + """is""",
				        SPIECE_UNDERLINE + """f""",
				        """al""",
				        """s""",
				        """<unk>""",
				        """.""",
				    ]					,   )

				a_      				=						tokenizer.vocab_size
				a_      				=						[tokenizer.convert_ids_to_tokens(UpperCAmelCase	) for x in range(end - 4					,   UpperCAmelCase	)]

				self.assertListEqual(UpperCAmelCase					,   ["""__java__""", """__python__""", """__en_XX__""", """<mask>"""]	)

				a_      				=						"""java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
				a_      				=						tokenizer(UpperCAmelCase	).input_ids
				self.assertEqual(
				    tokenizer.decode(UpperCAmelCase					,   skip_special_tokens=UpperCAmelCase					,   clean_up_tokenization_spaces=UpperCAmelCase	)					,   UpperCAmelCase					,   )






			def 					lowerCAmelCase__					(       self	):
				a_      				=						PLBartTokenizer(UpperCAmelCase					,   language_codes="""multi"""					,   keep_accents=UpperCAmelCase	)

				a_      				=						tokenizer.tokenize("""This is a test"""	)
				self.assertListEqual(UpperCAmelCase					,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(UpperCAmelCase	)					,   [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]					,   )

				a_      				=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        SPIECE_UNDERLINE + """I""",
				        SPIECE_UNDERLINE + """was""",
				        SPIECE_UNDERLINE + """b""",
				        """or""",
				        """n""",
				        SPIECE_UNDERLINE + """in""",
				        SPIECE_UNDERLINE + """""",
				        """9""",
				        """2""",
				        """0""",
				        """0""",
				        """0""",
				        """,""",
				        SPIECE_UNDERLINE + """and""",
				        SPIECE_UNDERLINE + """this""",
				        SPIECE_UNDERLINE + """is""",
				        SPIECE_UNDERLINE + """f""",
				        """al""",
				        """s""",
				        """é""",
				        """.""",
				    ]					,   )
				a_      				=						tokenizer.convert_tokens_to_ids(UpperCAmelCase	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]					,   )

				a_      				=						tokenizer.convert_ids_to_tokens(UpperCAmelCase	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        SPIECE_UNDERLINE + """I""",
				        SPIECE_UNDERLINE + """was""",
				        SPIECE_UNDERLINE + """b""",
				        """or""",
				        """n""",
				        SPIECE_UNDERLINE + """in""",
				        SPIECE_UNDERLINE + """""",
				        """<unk>""",
				        """2""",
				        """0""",
				        """0""",
				        """0""",
				        """,""",
				        SPIECE_UNDERLINE + """and""",
				        SPIECE_UNDERLINE + """this""",
				        SPIECE_UNDERLINE + """is""",
				        SPIECE_UNDERLINE + """f""",
				        """al""",
				        """s""",
				        """<unk>""",
				        """.""",
				    ]					,   )
				a_      				=						tokenizer.vocab_size
				a_      				=						[tokenizer.convert_ids_to_tokens(UpperCAmelCase	) for x in range(end - 7					,   UpperCAmelCase	)]

				self.assertListEqual(
				    UpperCAmelCase					,   ["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""]	)
				a_      				=						"""java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
				a_      				=						tokenizer(UpperCAmelCase	).input_ids
				self.assertEqual(
				    tokenizer.decode(UpperCAmelCase					,   skip_special_tokens=UpperCAmelCase					,   clean_up_tokenization_spaces=UpperCAmelCase	)					,   UpperCAmelCase					,   )







@require_torch
@require_sentencepiece
@require_tokenizers
class   a_ (      unittest.TestCase					):
			lowerCamelCase__	:			Tuple       =    'uclanlp/plbart-python-en_XX'
			lowerCamelCase__	:			List[str]       =    [
			    'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
			    'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
			]
			lowerCamelCase__	:			List[str]       =    [
			    'Returns the maximum value of a b c.',
			    'Sums the values of a b c.',
			]
			lowerCamelCase__	:			Dict       =    [
			    134,
			    5452,
			    33460,
			    33441,
			    33463,
			    33465,
			    33463,
			    33449,
			    988,
			    20,
			    33456,
			    19,
			    33456,
			    771,
			    39,
			    4258,
			    889,
			    3318,
			    33441,
			    33463,
			    33465,
			    33463,
			    33449,
			    2471,
			    2,
			    PYTHON_CODE,
			]




			@classmethod
			def 					lowerCAmelCase__					(       cls	):
				a_      				=						PLBartTokenizer.from_pretrained(
				    cls.checkpoint_name					,   language_codes="""base"""					,   src_lang="""python"""					,   tgt_lang="""en_XX"""	)
				a_      				=						1
				return cls




			def 					lowerCAmelCase__					(       self	):
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""]					,   5_00_01	)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""]					,   5_00_02	)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""]					,   5_00_03	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
				self.assertListEqual(self.expected_src_tokens					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				self.assertIn(UpperCAmelCase					,   self.tokenizer.all_special_ids	)
				a_      				=						[EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
				a_      				=						self.tokenizer.decode(UpperCAmelCase					,   skip_special_tokens=UpperCAmelCase	)
				a_      				=						self.tokenizer.decode(generated_ids[1:]					,   skip_special_tokens=UpperCAmelCase	)
				self.assertEqual(UpperCAmelCase					,   UpperCAmelCase	)
				self.assertNotIn(self.tokenizer.eos_token					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 20]
				self.assertIsInstance(src_text[0]					,   UpperCAmelCase	)
				a_      				=						10
				a_      				=						self.tokenizer(UpperCAmelCase					,   max_length=UpperCAmelCase					,   truncation=UpperCAmelCase	).input_ids[0]
				self.assertEqual(ids[-2]					,   2	)
				self.assertEqual(ids[-1]					,   UpperCAmelCase	)
				self.assertEqual(len(UpperCAmelCase	)					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""]	)					,   [5_00_04, 5_00_01]	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						tempfile.mkdtemp()
				a_      				=						self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(UpperCAmelCase	)
				a_      				=						PLBartTokenizer.from_pretrained(UpperCAmelCase	)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,   UpperCAmelCase	)




			@require_torch
			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.tokenizer(self.src_text					,   text_target=self.tgt_text					,   padding=UpperCAmelCase					,   return_tensors="""pt"""	)
				a_      				=						shift_tokens_right(batch["""labels"""]					,   self.tokenizer.pad_token_id	)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				self.assertEqual(batch.input_ids[1][-2:].tolist()					,   [2, PYTHON_CODE]	)
				self.assertEqual(batch.decoder_input_ids[1][0]					,   UpperCAmelCase	)
				self.assertEqual(batch.decoder_input_ids[1][-1]					,   2	)
				self.assertEqual(batch.labels[1][-2:].tolist()					,   [2, EN_CODE]	)




			@require_torch
			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.tokenizer(
				    self.src_text					,   text_target=self.tgt_text					,   padding=UpperCAmelCase					,   truncation=UpperCAmelCase					,   max_length=len(self.expected_src_tokens	)					,   return_tensors="""pt"""					,   )
				a_      				=						shift_tokens_right(batch["""labels"""]					,   self.tokenizer.pad_token_id	)

				self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)

				self.assertEqual((2, 26)					,   batch.input_ids.shape	)
				self.assertEqual((2, 26)					,   batch.attention_mask.shape	)
				a_      				=						batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens					,   UpperCAmelCase	)
				self.assertEqual(2					,   batch.decoder_input_ids[0, -1]	)  # EOS
				# Test that special tokens are reset
				self.assertEqual(self.tokenizer.prefix_tokens					,   []	)
				self.assertEqual(self.tokenizer.suffix_tokens					,   [self.tokenizer.eos_token_id, PYTHON_CODE]	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.tokenizer(self.src_text					,   padding=UpperCAmelCase					,   truncation=UpperCAmelCase					,   max_length=3					,   return_tensors="""pt"""	)
				a_      				=						self.tokenizer(
				    text_target=self.tgt_text					,   padding=UpperCAmelCase					,   truncation=UpperCAmelCase					,   max_length=10					,   return_tensors="""pt"""	)
				a_      				=						targets["""input_ids"""]
				a_      				=						shift_tokens_right(UpperCAmelCase					,   self.tokenizer.pad_token_id	)

				self.assertEqual(batch.input_ids.shape[1]					,   3	)
				self.assertEqual(batch.decoder_input_ids.shape[1]					,   10	)






			@require_torch
			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.tokenizer._build_translation_inputs(
				    """A test"""					,   return_tensors="""pt"""					,   src_lang="""en_XX"""					,   tgt_lang="""java"""	)

				self.assertEqual(
				    nested_simplify(UpperCAmelCase	)					,   {
				        # A, test, EOS, en_XX
				        """input_ids""": [[1_50, 2_42, 2, 5_00_03]],
				        """attention_mask""": [[1, 1, 1, 1]],
				        # java
				        """forced_bos_token_id""": 5_00_01,
				    }					,   )


'''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')
