






import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







class 			UpperCAmelCase_						(					lowercase,	unittest.TestCase ):



      """simple docstring"""
      UpperCamelCase_				:					Optional[Any]           =CanineTokenizer
      UpperCamelCase_				:					str           =False
      def     UpperCAmelCase						(				self )				->      Any:
          super().setUp()
          UpperCamelCase       :List[Any]								=   CanineTokenizer()
          tokenizer.save_pretrained(self.tmpdirname )
      @cached_property
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          return CanineTokenizer.from_pretrained('''google/canine-s''' )
      def     UpperCAmelCase						(				self    ,				**SCREAMING_SNAKE_CASE_ )				->      List[Any]:
          UpperCamelCase       :List[str]								=   self.tokenizer_class.from_pretrained(self.tmpdirname    ,				**__lowerCAmelCase )
          UpperCamelCase       :List[Any]								=   1024
          return tokenizer
      @require_torch
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :List[Any]								=   self.canine_tokenizer
          UpperCamelCase       :List[str]								=   ['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
          # fmt: off
          UpperCamelCase       :Dict								=   [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
          # fmt: on
          UpperCamelCase       :str								=   tokenizer(__lowerCAmelCase    ,				padding=__lowerCAmelCase    ,				return_tensors='''pt''' )
          self.assertIsInstance(__lowerCAmelCase    ,				__lowerCAmelCase )

          UpperCamelCase       :List[Any]								=   list(batch.input_ids.numpy()[0] )

          self.assertListEqual(__lowerCAmelCase    ,				__lowerCAmelCase )

          self.assertEqual((2, 39)    ,				batch.input_ids.shape )
          self.assertEqual((2, 39)    ,				batch.attention_mask.shape )
      @require_torch
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :int								=   self.canine_tokenizer
          UpperCamelCase       :List[Any]								=   ['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
          UpperCamelCase       :List[Any]								=   tokenizer(__lowerCAmelCase    ,				padding=__lowerCAmelCase    ,				return_tensors='''pt''' )
          # check if input_ids, attention_mask and token_type_ids are returned
          self.assertIn('''input_ids'''    ,				__lowerCAmelCase )
          self.assertIn('''attention_mask'''    ,				__lowerCAmelCase )
          self.assertIn('''token_type_ids'''    ,				__lowerCAmelCase )
      @require_torch
      def     UpperCAmelCase						(				self )				->      Dict:
          UpperCamelCase       :Any								=   self.canine_tokenizer
          UpperCamelCase       :Tuple								=   [
              '''What\'s the weater?''',
              '''It\'s about 25 degrees.''',
          ]
          UpperCamelCase       :Optional[int]								=   tokenizer(
              text_target=__lowerCAmelCase    ,				max_length=32    ,				padding='''max_length'''    ,				truncation=__lowerCAmelCase    ,				return_tensors='''pt''' )
          self.assertEqual(32    ,				targets['''input_ids'''].shape[1] )
      def     UpperCAmelCase						(				self )				->      List[str]:
          UpperCamelCase       :Tuple								=   self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  self.assertNotEqual(tokenizer.model_max_length    ,				42 )

        # Now let's start the test
          UpperCamelCase       :Optional[Any]								=   self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  # Isolate this from the other tests because we save additional tokens/etc
                  UpperCamelCase       :List[str]								=   tempfile.mkdtemp()

                  UpperCamelCase       :str								=   ''' He is very happy, UNwant\u00E9d,running'''
                  UpperCamelCase       :Optional[Any]								=   tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  tokenizer.save_pretrained(__lowerCAmelCase )

                  UpperCamelCase       :Any								=   tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                  UpperCamelCase       :int								=   after_tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  self.assertListEqual(__lowerCAmelCase    ,				__lowerCAmelCase )

                  shutil.rmtree(__lowerCAmelCase )

          UpperCamelCase       :int								=   self.get_tokenizers(model_max_length=42 )
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  # Isolate this from the other tests because we save additional tokens/etc
                  UpperCamelCase       :List[Any]								=   tempfile.mkdtemp()

                  UpperCamelCase       :Dict								=   ''' He is very happy, UNwant\u00E9d,running'''

                  UpperCamelCase       :List[Any]								=   tokenizer.additional_special_tokens

                  # We can add a new special token for Canine as follows:
                  UpperCamelCase       :Optional[Any]								=   chr(0XE_0_0_7 )
                  additional_special_tokens.append(__lowerCAmelCase )
                  tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
                  UpperCamelCase       :Tuple								=   tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  tokenizer.save_pretrained(__lowerCAmelCase )

                  UpperCamelCase       :int								=   tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                  UpperCamelCase       :Any								=   after_tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  self.assertListEqual(__lowerCAmelCase    ,				__lowerCAmelCase )
                  self.assertIn(__lowerCAmelCase    ,				after_tokenizer.additional_special_tokens )
                  self.assertEqual(after_tokenizer.model_max_length    ,				42 )

                  UpperCamelCase       :Tuple								=   tokenizer.__class__.from_pretrained(__lowerCAmelCase    ,				model_max_length=43 )
                  self.assertEqual(tokenizer.model_max_length    ,				43 )

                  shutil.rmtree(__lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :Union[str, Any]								=   self.get_tokenizers(do_lower_case=__lowerCAmelCase )
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  UpperCamelCase							,   UpperCamelCase       :int								=   self.get_clean_sequence(__lowerCAmelCase )

                  # a special token for Canine can be defined as follows:
                  UpperCamelCase       :Tuple								=   0XE_0_0_5
                  UpperCamelCase       :List[str]								=   chr(__lowerCAmelCase )

                  tokenizer.add_special_tokens({'''cls_token''': special_token} )
                  UpperCamelCase       :Optional[Any]								=   tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  self.assertEqual(len(__lowerCAmelCase )    ,				1 )

                  UpperCamelCase       :Tuple								=   tokenizer.decode(ids + encoded_special_token    ,				clean_up_tokenization_spaces=__lowerCAmelCase )
                  UpperCamelCase       :List[Any]								=   tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )

                  UpperCamelCase       :List[Any]								=   tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  UpperCamelCase       :Union[str, Any]								=   tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  self.assertEqual(__lowerCAmelCase    ,				input_encoded + special_token_id )

                  UpperCamelCase       :Dict								=   tokenizer.decode(__lowerCAmelCase    ,				skip_special_tokens=__lowerCAmelCase )
                  self.assertTrue(special_token not in decoded )
      def     UpperCAmelCase						(				self )				->      List[str]:
          UpperCamelCase       :str								=   self.get_tokenizers(do_lower_case=__lowerCAmelCase )
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  UpperCamelCase       :Optional[Any]								=   chr(0XE_0_0_5 )
                  UpperCamelCase       :Tuple								=   chr(0XE_0_0_6 )

                  # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                  tokenizer.add_tokens([SPECIAL_TOKEN_1]    ,				special_tokens=__lowerCAmelCase )
                  # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                  # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                  tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]} )

                  UpperCamelCase       :Tuple								=   tokenizer.tokenize(__lowerCAmelCase )
                  UpperCamelCase       :List[Any]								=   tokenizer.tokenize(__lowerCAmelCase )

                  self.assertEqual(len(__lowerCAmelCase )    ,				1 )
                  self.assertEqual(len(__lowerCAmelCase )    ,				1 )
                  self.assertEqual(token_a[0]    ,				__lowerCAmelCase )
                  self.assertEqual(token_a[0]    ,				__lowerCAmelCase )
      @require_tokenizers
      def     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :int								=   self.get_tokenizers(do_lower_case=__lowerCAmelCase )
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  # a special token for Canine can be defined as follows:
                  UpperCamelCase       :List[Any]								=   0XE_0_0_6
                  UpperCamelCase       :List[Any]								=   chr(__lowerCAmelCase )

                  UpperCamelCase       :Optional[int]								=   AddedToken(__lowerCAmelCase    ,				lstrip=__lowerCAmelCase )
                  tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]} )

                  with tempfile.TemporaryDirectory() as tmp_dir_name:
                      tokenizer.save_pretrained(__lowerCAmelCase )
                      tokenizer.from_pretrained(__lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      Any:
          UpperCamelCase       :Optional[int]								=   []
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(__lowerCAmelCase )

                  with open(os.path.join(__lowerCAmelCase    ,				'''special_tokens_map.json''' )    ,				encoding='''utf-8''' ) as json_file:
                      UpperCamelCase       :List[Any]								=   json.load(__lowerCAmelCase )

                  with open(os.path.join(__lowerCAmelCase    ,				'''tokenizer_config.json''' )    ,				encoding='''utf-8''' ) as json_file:
                      UpperCamelCase       :Union[str, Any]								=   json.load(__lowerCAmelCase )

                  # a special token for Canine can be defined as follows:
                  UpperCamelCase       :Any								=   0XE_0_0_6
                  UpperCamelCase       :Union[str, Any]								=   chr(__lowerCAmelCase )

                  UpperCamelCase       :Tuple								=   [new_token_a]
                  UpperCamelCase       :str								=   [new_token_a]

                  with open(os.path.join(__lowerCAmelCase    ,				'''special_tokens_map.json''' )    ,				'''w'''    ,				encoding='''utf-8''' ) as outfile:
                      json.dump(__lowerCAmelCase    ,				__lowerCAmelCase )
                  with open(os.path.join(__lowerCAmelCase    ,				'''tokenizer_config.json''' )    ,				'''w'''    ,				encoding='''utf-8''' ) as outfile:
                      json.dump(__lowerCAmelCase    ,				__lowerCAmelCase )

                  # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                  # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                  # "special_tokens_map.json" files
                  UpperCamelCase       :Optional[int]								=   tokenizer_class.from_pretrained(__lowerCAmelCase    ,				extra_ids=0 )
                  self.assertIn(__lowerCAmelCase    ,				tokenizer_without_change_in_init.additional_special_tokens )
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      [new_token_a]    ,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                          tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) )    ,				)

                  UpperCamelCase       :Union[str, Any]								=   0XE_0_0_7
                  UpperCamelCase       :int								=   chr(__lowerCAmelCase )
                  # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                  UpperCamelCase       :Optional[Any]								=   [AddedToken(__lowerCAmelCase    ,				lstrip=__lowerCAmelCase )]
                  UpperCamelCase       :int								=   tokenizer_class.from_pretrained(
                      __lowerCAmelCase    ,				additional_special_tokens=__lowerCAmelCase    ,				extra_ids=0 )

                  self.assertIn(__lowerCAmelCase    ,				tokenizer.additional_special_tokens )
                  # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      [new_token_a]    ,				tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
      @require_tokenizers
      def     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :Optional[int]								=   self.get_tokenizers(do_lower_case=__lowerCAmelCase )
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  UpperCamelCase       :List[Any]								=   '''hello world'''
                  if self.space_between_special_tokens:
                      UpperCamelCase       :Optional[int]								=   '''[CLS] hello world [SEP]'''
                  else:
                      UpperCamelCase       :Any								=   input
                  UpperCamelCase       :str								=   tokenizer.encode(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase )
                  UpperCamelCase       :Optional[int]								=   tokenizer.decode(__lowerCAmelCase    ,				spaces_between_special_tokens=self.space_between_special_tokens )
                  self.assertIn(__lowerCAmelCase    ,				[output, output.lower()] )
      def     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :Tuple								=   self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
                  UpperCamelCase       :Tuple								=   [
                      '''bos_token''',
                      '''eos_token''',
                      '''unk_token''',
                      '''sep_token''',
                      '''pad_token''',
                      '''cls_token''',
                      '''mask_token''',
                  ]

          UpperCamelCase       :List[str]								=   '''a'''
          UpperCamelCase       :Optional[int]								=   ord(__lowerCAmelCase )

          for attr in attributes_list:
              setattr(__lowerCAmelCase    ,				attr + '''_id'''    ,				__lowerCAmelCase )
              self.assertEqual(getattr(__lowerCAmelCase    ,				__lowerCAmelCase )    ,				__lowerCAmelCase )
              self.assertEqual(getattr(__lowerCAmelCase    ,				attr + '''_id''' )    ,				__lowerCAmelCase )

              setattr(__lowerCAmelCase    ,				attr + '''_id'''    ,				__lowerCAmelCase )
              self.assertEqual(getattr(__lowerCAmelCase    ,				__lowerCAmelCase )    ,				__lowerCAmelCase )
              self.assertEqual(getattr(__lowerCAmelCase    ,				attr + '''_id''' )    ,				__lowerCAmelCase )

          setattr(__lowerCAmelCase    ,				'''additional_special_tokens_ids'''    ,				[] )
          self.assertListEqual(getattr(__lowerCAmelCase    ,				'''additional_special_tokens''' )    ,				[] )
          self.assertListEqual(getattr(__lowerCAmelCase    ,				'''additional_special_tokens_ids''' )    ,				[] )

          UpperCamelCase       :List[Any]								=   0XE_0_0_6
          UpperCamelCase       :str								=   chr(__lowerCAmelCase )
          setattr(__lowerCAmelCase    ,				'''additional_special_tokens_ids'''    ,				[additional_special_token_id] )
          self.assertListEqual(getattr(__lowerCAmelCase    ,				'''additional_special_tokens''' )    ,				[additional_special_token] )
          self.assertListEqual(getattr(__lowerCAmelCase    ,				'''additional_special_tokens_ids''' )    ,				[additional_special_token_id] )
      def     UpperCAmelCase						(				self )				->      Tuple:
          pass
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          pass
      def     UpperCAmelCase						(				self )				->      Dict:
          pass
      def     UpperCAmelCase						(				self )				->      str:
          pass
      def     UpperCAmelCase						(				self )				->      int:
          pass
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          pass
      def     UpperCAmelCase						(				self )				->      Dict:
          pass




      def     UpperCAmelCase						(				self )				->      str:
          pass






"""simple docstring"""






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


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


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''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(lowerCAmelCase__		)

      # 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(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).to(device=lowerCAmelCase__					,		dtype=lowerCAmelCase__		),
              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=lowerCAmelCase__					,		)
      del vae_decoder


if __name__ == "__main__":
       __lowerCAmelCase    :       Tuple					     =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=1_4,
           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    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



