

'''simple docstring'''






import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			RobertaTokenizer
  SCREAMING_SNAKE_CASE    :  str							=			RobertaTokenizerFast
  SCREAMING_SNAKE_CASE    :  List[str]							=			True
  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			{'cls_token': '<s>'}






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   __lowercase							  = [
       '''l''',
       '''o''',
       '''w''',
       '''e''',
       '''r''',
       '''s''',
       '''t''',
       '''i''',
       '''d''',
       '''n''',
       '''\u0120''',
       '''\u0120l''',
       '''\u0120n''',
       '''\u0120lo''',
       '''\u0120low''',
       '''er''',
       '''\u0120lowest''',
       '''\u0120newer''',
       '''\u0120wider''',
       '''<unk>''',
   ]
   __lowercase							  = dict(zip(lowercase__					,range(len(lowercase__			)			)			)			)
   __lowercase							  = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
   __lowercase							  = {'''unk_token''': '''<unk>'''}

   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''vocab_file''']			)
   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''merges_file''']			)
   with open(self.vocab_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write(json.dumps(lowercase__			) + '''\n'''			)
   with open(self.merges_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write('''\n'''.join(lowercase__			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,**lowercase__   :     Dict			):
   kwargs.update(self.special_tokens_map			)
   return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,**lowercase__   :     Optional[Any]			):
   kwargs.update(self.special_tokens_map			)
   return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = '''lower newer'''
   __lowercase							  = '''lower newer'''
   return input_text, output_text






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.tokenizer_class(self.vocab_file					,self.merges_file					,**self.special_tokens_map			)
   __lowercase							  = '''lower newer'''
   __lowercase							  = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
   __lowercase							  = tokenizer.tokenize(lowercase__			)  # , add_prefix_space=True)
   self.assertListEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokens + [tokenizer.unk_token]
   __lowercase							  = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__			)					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.get_tokenizer()

   self.assertListEqual(tokenizer.encode('''Hello world!'''					,add_special_tokens=lowercase__			)					,[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]			)
   self.assertListEqual(
       tokenizer.encode('''Hello world! cécé herlolip 418'''					,add_special_tokens=lowercase__			)					,[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]					,)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''roberta-base'''			)

   __lowercase							  = tokenizer.encode('''sequence builders'''					,add_special_tokens=lowercase__			)
   __lowercase							  = tokenizer.encode('''multi-sequence build'''					,add_special_tokens=lowercase__			)

   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,'''multi-sequence build'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)

   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__			)
   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__					,lowercase__			)

   assert encoded_sentence == encoded_text_from_decode
   assert encoded_pair == encoded_pair_from_decode






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_tokenizer()

   __lowercase							  = '''Encode this sequence.'''
   __lowercase							  = tokenizer.byte_encoder[''' '''.encode('''utf-8'''			)[0]]

   # Testing encoder arguments
   __lowercase							  = tokenizer.encode(lowercase__					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
   self.assertNotEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokenizer.encode(lowercase__					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
   self.assertEqual(lowercase__					,lowercase__			)

   tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}			)
   __lowercase							  = tokenizer.encode(lowercase__					,add_special_tokens=lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
   self.assertNotEqual(lowercase__					,lowercase__			)

   # Testing spaces after special tokens
   __lowercase							  = '''<mask>'''
   tokenizer.add_special_tokens(
       {'''mask_token''': AddedToken(lowercase__					,lstrip=lowercase__					,rstrip=lowercase__			)}			)  # mask token has a left space
   __lowercase							  = tokenizer.convert_tokens_to_ids(lowercase__			)

   __lowercase							  = '''Encode <mask> sequence'''
   __lowercase							  = '''Encode <mask>sequence'''

   __lowercase							  = tokenizer.encode(lowercase__			)
   __lowercase							  = encoded.index(lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
   self.assertEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokenizer.encode(lowercase__			)
   __lowercase							  = encoded.index(lowercase__			)
   __lowercase							  = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
   self.assertNotEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
     __lowercase							  = self.rust_tokenizer_class.from_pretrained(lowercase__					,**lowercase__			)
     __lowercase							  = self.tokenizer_class.from_pretrained(lowercase__					,**lowercase__			)
     __lowercase							  = '''A, <mask> AllenNLP sentence.'''
     __lowercase							  = tokenizer_r.encode_plus(lowercase__					,add_special_tokens=lowercase__					,return_token_type_ids=lowercase__			)
     __lowercase							  = tokenizer_p.encode_plus(lowercase__					,add_special_tokens=lowercase__					,return_token_type_ids=lowercase__			)

     # token_type_ids should put 0 everywhere
     self.assertEqual(sum(tokens_r['''token_type_ids''']			)					,sum(tokens_p['''token_type_ids''']			)			)

     # attention_mask should put 1 everywhere, so sum over length should be 1
     self.assertEqual(
         sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)					,sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)					,)

     __lowercase							  = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
     __lowercase							  = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)

     # Rust correctly handles the space before the mask while python doesnt
     self.assertSequenceEqual(tokens_p['''input_ids''']					,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)
     self.assertSequenceEqual(tokens_r['''input_ids''']					,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)

     self.assertSequenceEqual(
         lowercase__					,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
     self.assertSequenceEqual(
         lowercase__					,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   for trim_offsets, add_prefix_space in itertools.product([True, False]					,repeat=2			):
    __lowercase							  = self.rust_tokenizer_class.from_pretrained(
        self.tmpdirname					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)

    __lowercase							  = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
    __lowercase							  = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

    self.assertEqual(pre_tokenizer_state['''add_prefix_space''']					,lowercase__			)

    self.assertEqual(post_processor_state['''add_prefix_space''']					,lowercase__			)
    self.assertEqual(post_processor_state['''trim_offsets''']					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
   # `trim_offsets`
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
     __lowercase							  = '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
     __lowercase							  = F"{text_of_1_token} {text_of_1_token}"

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			) + 1, len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			) + 1, len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			), len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(len(lowercase__			), len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = F" {text}"

     # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
     #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
     # )
     # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
     # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
     # self.assertEqual(
     #     encoding.offset_mapping[1],
     #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
     # )

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(1, 1 + len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(1 + len(lowercase__			) + 1, 1 + len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, 1 + len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(1 + len(lowercase__			), 1 + len(lowercase__			) + 1 + len(lowercase__			))					,)

     __lowercase							  = self.rust_tokenizer_class.from_pretrained(
         lowercase__					,use_fast=lowercase__					,add_prefix_space=lowercase__					,trim_offsets=lowercase__			)
     __lowercase							  = tokenizer_r(lowercase__					,return_offsets_mapping=lowercase__					,add_special_tokens=lowercase__			)
     self.assertEqual(encoding.offset_mapping[0]					,(0, 1 + len(lowercase__			))			)
     self.assertEqual(
         encoding.offset_mapping[1]					,(1 + len(lowercase__			), 1 + len(lowercase__			) + 1 + len(lowercase__			))					,)




'''simple docstring'''






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__=False     ,				):






 """simple docstring"""



 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 		_A     (    A__     ,				A__     ,				A__     ,				A__ = False      ):






 """simple docstring"""



 __lowercase							  = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  __lowercase							  = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''      )
 else:
  __lowercase							  = '''cpu'''
 __lowercase							  = Path(A__      )

 # VAE DECODER
 __lowercase							  = AutoencoderKL.from_pretrained(model_path + '''/vae'''      )
 __lowercase							  = vae_decoder.config.latent_channels
 # forward only through the decoder part
 __lowercase							  = 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__":
 lowerCAmelCase__											=	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''')

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

