

import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     int:
       A_		   = XCLIPTextConfig()

       # derive patch size from model name
       A_		   = model_name.find("""patch"""			)
       A_		   = int(model_name[start_idx + len("""patch"""			) : start_idx + len("""patch"""			) + 2]			)
       A_		   = XCLIPVisionConfig(patch_size=UpperCAmelCase__,    num_frames=UpperCAmelCase__			)

       if "large" in model_name:
              A_		   = 7_68
              A_		   = 30_72
              A_		   = 12

              A_		   = 10_24
              A_		   = 40_96
              A_		   = 16
              A_		   = 24
              A_		   = 7_68
              A_		   = 30_72

       if model_name == "xclip-large-patch14-16-frames":
              A_		   = 3_36

       A_		   = XCLIPConfig.from_text_vision_configs(UpperCAmelCase__,    UpperCAmelCase__			)

       if "large" in model_name:
              A_		   = 7_68

       return config
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       # text encoder
       if name == "token_embedding.weight":
              A_		   = name.replace("""token_embedding.weight""",    """text_model.embeddings.token_embedding.weight"""			)
       if name == "positional_embedding":
              A_		   = name.replace("""positional_embedding""",    """text_model.embeddings.position_embedding.weight"""			)
       if "ln_1" in name:
              A_		   = name.replace("""ln_1""",    """layer_norm1"""			)
       if "ln_2" in name:
              A_		   = name.replace("""ln_2""",    """layer_norm2"""			)
       if "c_fc" in name:
              A_		   = name.replace("""c_fc""",    """fc1"""			)
       if "c_proj" in name:
              A_		   = name.replace("""c_proj""",    """fc2"""			)
       if name.startswith("""transformer.resblocks"""			):
              A_		   = name.replace("""transformer.resblocks""",    """text_model.encoder.layers"""			)
       if "attn.out_proj" in name and "message" not in name:
              A_		   = name.replace("""attn.out_proj""",    """self_attn.out_proj"""			)
       if "ln_final" in name:
              A_		   = name.replace("""ln_final""",    """text_model.final_layer_norm"""			)
       # visual encoder
       if name == "visual.class_embedding":
              A_		   = name.replace("""visual.class_embedding""",    """vision_model.embeddings.class_embedding"""			)
       if name == "visual.positional_embedding":
              A_		   = name.replace("""visual.positional_embedding""",    """vision_model.embeddings.position_embedding.weight"""			)
       if name.startswith("""visual.transformer.resblocks"""			):
              A_		   = name.replace("""visual.transformer.resblocks""",    """vision_model.encoder.layers"""			)
       if "visual.conv1" in name:
              A_		   = name.replace("""visual.conv1""",    """vision_model.embeddings.patch_embedding"""			)
       if "visual.ln_pre" in name:
              A_		   = name.replace("""visual.ln_pre""",    """vision_model.pre_layernorm"""			)
       if "visual.ln_post" in name:
              A_		   = name.replace("""visual.ln_post""",    """vision_model.post_layernorm"""			)
       if "visual.proj" in name:
              A_		   = name.replace("""visual.proj""",    """visual_projection.weight"""			)
       if "text_projection" in name:
              A_		   = name.replace("""text_projection""",    """text_projection.weight"""			)
       # things on top
       if "prompts_visual_proj" in name:
              A_		   = name.replace("""prompts_visual_proj""",    """prompts_visual_projection"""			)
       if "prompts_visual_ln" in name:
              A_		   = name.replace("""prompts_visual_ln""",    """prompts_visual_layernorm"""			)
       # mit
       if name == "mit.positional_embedding":
              A_		   = name.replace("""positional""",    """position"""			)
       if name.startswith("""mit.resblocks"""			):
              A_		   = name.replace("""mit.resblocks""",    """mit.encoder.layers"""			)
       # prompts generator
       if name.startswith("""prompts_generator.norm"""			):
              A_		   = name.replace("""prompts_generator.norm""",    """prompts_generator.layernorm"""			)

       return name
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[Any]:
       for key in orig_state_dict.copy().keys():
              A_		   = orig_state_dict.pop(UpperCAmelCase__			)

              if "attn.in_proj" in key:
                     A_		   = key.split("""."""			)
                     if key.startswith("""visual"""			):
                            A_		   = key_split[3]
                            A_		   = config.vision_config.hidden_size
                            if "message_attn" in key:
                                   if "weight" in key:
                                          A_		   = val[
                                              :dim, :
                                          ]
                                          A_		   = val[
                                              dim : dim * 2, :
                                          ]
                                          A_		   = val[
                                              -dim:, :
                                          ]
                                   else:
                                          A_		   = val[
                                              :dim
                                          ]
                                          A_		   = val[
                                              dim : dim * 2
                                          ]
                                          A_		   = val[
                                              -dim:
                                          ]
                            else:
                                   if "weight" in key:
                                          A_		   = val[
                                              :dim, :
                                          ]
                                          A_		   = val[
                                              dim : dim * 2, :
                                          ]
                                          A_		   = val[
                                              -dim:, :
                                          ]
                                   else:
                                          A_		   = val[:dim]
                                          A_		   = val[
                                              dim : dim * 2
                                          ]
                                          A_		   = val[-dim:]
                     elif key.startswith("""mit"""			):
                            A_		   = key_split[2]
                            A_		   = config.vision_config.mit_hidden_size
                            if "weight" in key:
                                   A_		   = val[:dim, :]
                                   A_		   = val[dim : dim * 2, :]
                                   A_		   = val[-dim:, :]
                            else:
                                   A_		   = val[:dim]
                                   A_		   = val[dim : dim * 2]
                                   A_		   = val[-dim:]
                     else:
                            A_		   = key_split[2]
                            A_		   = config.text_config.hidden_size
                            if "weight" in key:
                                   A_		   = val[:dim, :]
                                   A_		   = val[
                                       dim : dim * 2, :
                                   ]
                                   A_		   = val[-dim:, :]
                            else:
                                   A_		   = val[:dim]
                                   A_		   = val[
                                       dim : dim * 2
                                   ]
                                   A_		   = val[-dim:]
              else:
                     A_		   = rename_key(UpperCAmelCase__			)
                     if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                            A_		   = val.T
                     A_		   = val

       return orig_state_dict
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       if num_frames == 8:
              A_		   = 'eating_spaghetti_8_frames.npy'
       elif num_frames == 16:
              A_		   = 'eating_spaghetti.npy'
       elif num_frames == 32:
              A_		   = 'eating_spaghetti_32_frames.npy'
       A_		   = hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video""",    filename=UpperCAmelCase__,    repo_type="""dataset""",    )
       A_		   = np.load(UpperCAmelCase__			)
       return list(UpperCAmelCase__			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__=None,    UpperCAmelCase__=False			)     ->     str:
       A_		   = {
           # fully supervised kinetics-400 checkpoints
           'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
           'xclip-base-patch32-16-frames': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
           ),
           'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
           'xclip-base-patch16-16-frames': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
           ),
           'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
           'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
           # fully supervised kinetics-600 checkpoints
           'xclip-base-patch16-kinetics-600': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
           ),
           'xclip-base-patch16-kinetics-600-16-frames': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
           ),
           'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
           # few shot
           'xclip-base-patch16-hmdb-2-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
           ),
           'xclip-base-patch16-hmdb-4-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
           ),
           'xclip-base-patch16-hmdb-8-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
           ),
           'xclip-base-patch16-hmdb-16-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
           ),
           'xclip-base-patch16-ucf-2-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
           ),
           'xclip-base-patch16-ucf-4-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
           ),
           'xclip-base-patch16-ucf-8-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
           ),
           'xclip-base-patch16-ucf-16-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
           ),
           # zero shot
           'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
       }

       A_		   = model_to_url[model_name]
       A_		   = 8
       if "16-frames" in model_name:
              A_		   = 16
       elif "shot" in model_name:
              A_		   = 32

       A_		   = get_xclip_config(UpperCAmelCase__,    UpperCAmelCase__			)
       A_		   = XCLIPModel(UpperCAmelCase__			)
       model.eval()

       if "drive" in checkpoint_url:
              A_		   = 'pytorch_model.bin'
              gdown.cached_download(UpperCAmelCase__,    UpperCAmelCase__,    quiet=UpperCAmelCase__			)
              A_		   = torch.load(UpperCAmelCase__,    map_location="""cpu"""			)['model']
       else:
              A_		   = torch.hub.load_state_dict_from_url(UpperCAmelCase__			)['model']

       A_		   = convert_state_dict(UpperCAmelCase__,    UpperCAmelCase__			)

       A_		   = XCLIPModel(UpperCAmelCase__			)
       A_		   = model.load_state_dict(UpperCAmelCase__,    strict=UpperCAmelCase__			)
       assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
       model.eval()

       A_		   = 3_36 if model_name == 'xclip-large-patch14-16-frames' else 2_24
       A_		   = VideoMAEImageProcessor(size=UpperCAmelCase__			)
       A_		   = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			)
       A_		   = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""			)
       A_		   = XCLIPProcessor(image_processor=UpperCAmelCase__,    tokenizer=UpperCAmelCase__			)

       A_		   = prepare_video(UpperCAmelCase__			)
       A_		   = processor(
           text=["""playing sports""", """eating spaghetti""", """go shopping"""],    videos=UpperCAmelCase__,    return_tensors="""pt""",    padding=UpperCAmelCase__			)

       print("""Shape of pixel values:""",    inputs.pixel_values.shape			)

       with torch.no_grad():
              A_		   = model(**UpperCAmelCase__			)

       # Verify outputs
       A_		   = outputs.logits_per_video
       A_		   = logits_per_video.softmax(dim=1			)
       print("""Probs:""",    UpperCAmelCase__			)
       # kinetics-400
       if model_name == "xclip-base-patch32":
              A_		   = torch.tensor([[0.0_019, 0.9_951, 0.0_030]]			)
       elif model_name == "xclip-base-patch32-16-frames":
              A_		   = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]			)
       elif model_name == "xclip-base-patch16":
              A_		   = torch.tensor([[0.0_083, 0.9_681, 0.0_236]]			)
       elif model_name == "xclip-base-patch16-16-frames":
              A_		   = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]			)
       elif model_name == "xclip-large-patch14":
              A_		   = torch.tensor([[0.0_062, 0.9_864, 0.0_075]]			)
       elif model_name == "xclip-large-patch14-16-frames":
              A_		   = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]			)
       # kinetics-600
       elif model_name == "xclip-base-patch16-kinetics-600":
              A_		   = torch.tensor([[0.0_555, 0.8_914, 0.0_531]]			)
       elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
              A_		   = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]			)
       elif model_name == "xclip-large-patch14-kinetics-600":
              A_		   = torch.tensor([[0.0_036, 0.9_920, 0.0_045]]			)
       # few shot
       elif model_name == "xclip-base-patch16-hmdb-2-shot":
              A_		   = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]			)
       elif model_name == "xclip-base-patch16-hmdb-4-shot":
              A_		   = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]			)
       elif model_name == "xclip-base-patch16-hmdb-8-shot":
              A_		   = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]			)
       elif model_name == "xclip-base-patch16-hmdb-16-shot":
              A_		   = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]			)
       elif model_name == "xclip-base-patch16-ucf-2-shot":
              A_		   = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]			)
       elif model_name == "xclip-base-patch16-ucf-4-shot":
              A_		   = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]			)
       elif model_name == "xclip-base-patch16-ucf-8-shot":
              A_		   = torch.tensor([[0.0_027, 0.9_904, 0.0_070]]			)
       elif model_name == "xclip-base-patch16-ucf-16-shot":
              A_		   = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]			)
       # zero shot
       elif model_name == "xclip-base-patch16-zero-shot":
              A_		   = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]			)
       else:
              raise ValueError(F'''Model name {model_name} not supported'''			)
       assert torch.allclose(UpperCAmelCase__,    UpperCAmelCase__,    atol=1e-3			)
       print("""Looks ok!"""			)

       if pytorch_dump_folder_path is not None:
              print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
              model.save_pretrained(UpperCAmelCase__			)

       if push_to_hub:
              print("""Pushing model, processor and slow tokenizer files to the hub..."""			)
              model.push_to_hub(UpperCAmelCase__,    organization="""nielsr"""			)
              processor.push_to_hub(UpperCAmelCase__,    organization="""nielsr"""			)
              slow_tokenizer.push_to_hub(UpperCAmelCase__,    organization="""nielsr"""			)


if __name__ == "__main__":
      __lowerCamelCase						    =      argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default='''xclip-base-patch32''',
          type=str,
          help='''Name of the model.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )
      parser.add_argument(
          '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
      )

      __lowerCamelCase						    =      parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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


@require_sentencepiece
@require_tokenizers
class    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						ReformerTokenizer
    lowercase		   =						ReformerTokenizerFast
    lowercase		   =						True
    lowercase		   =						False
    lowercase		   =						True



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           super().setUp()

           A_		   = ReformerTokenizer(UpperCamelCase__						,			keep_accents=UpperCamelCase__    )
           tokenizer.save_pretrained(self.tmpdirname    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = """<s>"""
           A_		   = 1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__    )						,			UpperCamelCase__    )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__    )						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = list(self.get_tokenizer().get_vocab().keys()    )

           self.assertEqual(vocab_keys[0]						,			"""<unk>"""    )
           self.assertEqual(vocab_keys[1]						,			"""<s>"""    )
           self.assertEqual(vocab_keys[-1]						,			"""j"""    )
           self.assertEqual(len(UpperCamelCase__    )						,			1000    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           self.assertEqual(self.get_tokenizer().vocab_size						,			1000    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           if not self.test_rust_tokenizer:
                  return

           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()

           A_		   = """I was born in 92000, and this is falsé."""

           A_		   = tokenizer.tokenize(UpperCamelCase__    )
           A_		   = rust_tokenizer.tokenize(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = self.get_rust_tokenizer()
           A_		   = tokenizer.encode(UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__=15    )  -> int:





           '''simple docstring'''
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                         A_		   = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )

                         # Simple input
                         A_		   = """This is a simple input"""
                         A_		   = ["""This is a simple input 1""", """This is a simple input 2"""]
                         A_		   = ("""This is a simple input""", """This is a pair""")
                         A_		   = [
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = ReformerTokenizer(UpperCamelCase__						,			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__    )						,			[285, 46, 10, 170, 382]						,			)

           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__						,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 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>""",
                   """.""",
               ]						,			)



    @cached_property
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""    )



    @slow
    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = """Hello World!"""
           A_		   = [126, 32, 262, 152, 38, 72, 287]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @slow
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = (
               """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
               """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
           )
           A_		   = [
               108,
               265,
               24,
               111,
               4,
               258,
               156,
               35,
               28,
               275,
               3,
               259,
               297,
               260,
               84,
               4,
               35,
               110,
               44,
               8,
               259,
               91,
               268,
               21,
               11,
               209,
               274,
               109,
               266,
               277,
               117,
               86,
               93,
               315,
               258,
               278,
               258,
               277,
               258,
               0,
               258,
               288,
               258,
               319,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               287,
               258,
               315,
               258,
               289,
               258,
               278,
               99,
               269,
               266,
               262,
               8,
               259,
               241,
               4,
               217,
               230,
               268,
               266,
               55,
               168,
               106,
               75,
               193,
               266,
               223,
               27,
               49,
               26,
               282,
               25,
               264,
               299,
               19,
               26,
               0,
               258,
               277,
               117,
               86,
               93,
               176,
               183,
               270,
               11,
               262,
               42,
               61,
               265,
           ]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @require_torch
    @slow
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           A_		   = list(self.big_tokenizer.get_vocab().keys()    )[:10]
           A_		   = """ """.join(UpperCamelCase__    )
           A_		   = self.big_tokenizer.encode_plus(UpperCamelCase__						,			return_tensors="""pt"""    )
           A_		   = self.big_tokenizer.batch_encode_plus([sequence, sequence]						,			return_tensors="""pt"""    )

           A_		   = ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           A_		   = encoded_sequence["""input_ids"""].shape
           A_		   = ReformerModel(UpperCamelCase__    )

           # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                  model(**UpperCamelCase__    )
                  model(**UpperCamelCase__    )







    @slow
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           # fmt: off
           A_		   = {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # This tokenizer does not know some characters like ")".
           # That is the reason why we use very simple texts here.
           # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
           A_		   = [
               """This is a very simple sentence.""",
               """The quick brown fox jumps over the lazy dog.""",
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase__						,			model_name="""google/reformer-crime-and-punishment"""						,			revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""						,			padding=UpperCamelCase__						,			sequences=UpperCamelCase__						,			)
