





'''simple docstring'''




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]			,     __lowercase							:      List[Any]=1       )		->       Union[str, Any]:


     '''simple docstring'''



     if n_shave_prefix_segments >= 0:
          return ".".join(path.split("."       )[n_shave_prefix_segments:]       )
     else:
          return ".".join(path.split("."       )[:n_shave_prefix_segments]       )
def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Any=0       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   []
     for old_item in old_list:
          _UpperCAmelCase     							=   old_item.replace("in_layers.0"			,     "norm1"       )
          _UpperCAmelCase     							=   new_item.replace("in_layers.2"			,     "conv1"       )

          _UpperCAmelCase     							=   new_item.replace("out_layers.0"			,     "norm2"       )
          _UpperCAmelCase     							=   new_item.replace("out_layers.3"			,     "conv2"       )

          _UpperCAmelCase     							=   new_item.replace("emb_layers.1"			,     "time_emb_proj"       )
          _UpperCAmelCase     							=   new_item.replace("skip_connection"			,     "conv_shortcut"       )

          _UpperCAmelCase     							=   shave_segments(__lowercase			,     n_shave_prefix_segments=__lowercase       )

          mapping.append({"old": old_item, "new": new_item}       )

     return mapping
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      int=0       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   []
     for old_item in old_list:
          _UpperCAmelCase     							=   old_item

          _UpperCAmelCase     							=   new_item.replace("norm.weight"			,     "group_norm.weight"       )
          _UpperCAmelCase     							=   new_item.replace("norm.bias"			,     "group_norm.bias"       )

          _UpperCAmelCase     							=   new_item.replace("proj_out.weight"			,     "proj_attn.weight"       )
          _UpperCAmelCase     							=   new_item.replace("proj_out.bias"			,     "proj_attn.bias"       )

          _UpperCAmelCase     							=   shave_segments(__lowercase			,     n_shave_prefix_segments=__lowercase       )

          mapping.append({"old": old_item, "new": new_item}       )

     return mapping
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple			,     __lowercase							:      Union[str, Any]			,     __lowercase							:      List[Any]=None			,     __lowercase							:      int=None			,     __lowercase							:      str=None       )		->       List[str]:


     '''simple docstring'''



     assert isinstance(__lowercase			,     __lowercase       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

     # Splits the attention layers into three variables.
     if attention_paths_to_split is not None:
          for path, path_map in attention_paths_to_split.items():
               _UpperCAmelCase     							=   old_checkpoint[path]
               _UpperCAmelCase     							=   old_tensor.shape[0] // 3

               _UpperCAmelCase     							=   (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

               _UpperCAmelCase     							=   old_tensor.shape[0] // config["num_head_channels"] // 3

               _UpperCAmelCase     							=   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
               _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   old_tensor.split(channels // num_heads			,     dim=1       )

               _UpperCAmelCase     							=   query.reshape(__lowercase       )
               _UpperCAmelCase     							=   key.reshape(__lowercase       )
               _UpperCAmelCase     							=   value.reshape(__lowercase       )

     for path in paths:
          _UpperCAmelCase     							=   path["new"]

          # These have already been assigned
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          # Global renaming happens here
          _UpperCAmelCase     							=   new_path.replace("middle_block.0"			,     "mid_block.resnets.0"       )
          _UpperCAmelCase     							=   new_path.replace("middle_block.1"			,     "mid_block.attentions.0"       )
          _UpperCAmelCase     							=   new_path.replace("middle_block.2"			,     "mid_block.resnets.1"       )

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    _UpperCAmelCase     							=   new_path.replace(replacement["old"]			,     replacement["new"]       )

        # proj_attn.weight has to be converted from conv 1D to linear
          if "proj_attn.weight" in new_path:
               _UpperCAmelCase     							=   old_checkpoint[path["old"]][:, :, 0]
          else:
               _UpperCAmelCase     							=   old_checkpoint[path["old"]]
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      List[str]       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   {}

     _UpperCAmelCase     							=   checkpoint["time_embed.0.weight"]
     _UpperCAmelCase     							=   checkpoint["time_embed.0.bias"]
     _UpperCAmelCase     							=   checkpoint["time_embed.2.weight"]
     _UpperCAmelCase     							=   checkpoint["time_embed.2.bias"]

     _UpperCAmelCase     							=   checkpoint["input_blocks.0.0.weight"]
     _UpperCAmelCase     							=   checkpoint["input_blocks.0.0.bias"]

     _UpperCAmelCase     							=   checkpoint["out.0.weight"]
     _UpperCAmelCase     							=   checkpoint["out.0.bias"]
     _UpperCAmelCase     							=   checkpoint["out.2.weight"]
     _UpperCAmelCase     							=   checkpoint["out.2.bias"]

     # Retrieves the keys for the input blocks only
     _UpperCAmelCase     							=   len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "input_blocks" in layer}       )
     _UpperCAmelCase     							=   {
         layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
         for layer_id in range(__lowercase       )
     }

     # Retrieves the keys for the middle blocks only
     _UpperCAmelCase     							=   len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "middle_block" in layer}       )
     _UpperCAmelCase     							=   {
         layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
         for layer_id in range(__lowercase       )
     }

     # Retrieves the keys for the output blocks only
     _UpperCAmelCase     							=   len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "output_blocks" in layer}       )
     _UpperCAmelCase     							=   {
         layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
         for layer_id in range(__lowercase       )
     }

     for i in range(1			,     __lowercase       ):
          _UpperCAmelCase     							=   (i - 1) // (config["num_res_blocks"] + 1)
          _UpperCAmelCase     							=   (i - 1) % (config["num_res_blocks"] + 1)

          _UpperCAmelCase     							=   [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
          _UpperCAmelCase     							=   [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

          if f'input_blocks.{i}.0.op.weight' in checkpoint:
               _UpperCAmelCase     							=   checkpoint[
                   f'input_blocks.{i}.0.op.weight'
               ]
               _UpperCAmelCase     							=   checkpoint[
                   f'input_blocks.{i}.0.op.bias'
               ]
               continue

          _UpperCAmelCase     							=   renew_resnet_paths(__lowercase       )
          _UpperCAmelCase     							=   {"old": f'input_blocks.{i}.0', "new": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
          _UpperCAmelCase     							=   {"old": "resnets.2.op", "new": "downsamplers.0.op"}
          assign_to_checkpoint(
              __lowercase			,     __lowercase			,     __lowercase			,     additional_replacements=[meta_path, resnet_op]			,     config=__lowercase       )

          if len(__lowercase       ):
               _UpperCAmelCase     							=   renew_attention_paths(__lowercase       )
               _UpperCAmelCase     							=   {
                   "old": f'input_blocks.{i}.1',
                   "new": f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
               }
               _UpperCAmelCase     							=   {
                   f'input_blocks.{i}.1.qkv.bias': {
                       "key": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                       "query": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                       "value": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                   },
                   f'input_blocks.{i}.1.qkv.weight': {
                       "key": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                       "query": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                       "value": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                   },
               }
               assign_to_checkpoint(
                   __lowercase			,     __lowercase			,     __lowercase			,     additional_replacements=[meta_path]			,     attention_paths_to_split=__lowercase			,     config=__lowercase			,     )

     _UpperCAmelCase     							=   middle_blocks[0]
     _UpperCAmelCase     							=   middle_blocks[1]
     _UpperCAmelCase     							=   middle_blocks[2]

     _UpperCAmelCase     							=   renew_resnet_paths(__lowercase       )
     assign_to_checkpoint(__lowercase			,     __lowercase			,     __lowercase			,     config=__lowercase       )

     _UpperCAmelCase     							=   renew_resnet_paths(__lowercase       )
     assign_to_checkpoint(__lowercase			,     __lowercase			,     __lowercase			,     config=__lowercase       )

     _UpperCAmelCase     							=   renew_attention_paths(__lowercase       )
     _UpperCAmelCase     							=   {
         "middle_block.1.qkv.bias": {
             "key": "mid_block.attentions.0.key.bias",
             "query": "mid_block.attentions.0.query.bias",
             "value": "mid_block.attentions.0.value.bias",
         },
         "middle_block.1.qkv.weight": {
             "key": "mid_block.attentions.0.key.weight",
             "query": "mid_block.attentions.0.query.weight",
             "value": "mid_block.attentions.0.value.weight",
         },
     }
     assign_to_checkpoint(
         __lowercase			,     __lowercase			,     __lowercase			,     attention_paths_to_split=__lowercase			,     config=__lowercase       )

     for i in range(__lowercase       ):
          _UpperCAmelCase     							=   i // (config["num_res_blocks"] + 1)
          _UpperCAmelCase     							=   i % (config["num_res_blocks"] + 1)
          _UpperCAmelCase     							=   [shave_segments(__lowercase			,     2       ) for name in output_blocks[i]]
          _UpperCAmelCase     							=   {}

          for layer in output_block_layers:
               _UpperCAmelCase							,  _UpperCAmelCase     							=   layer.split("."       )[0], shave_segments(__lowercase			,     1       )
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(__lowercase       )
               else:
                    _UpperCAmelCase     							=   [layer_name]

          if len(__lowercase       ) > 1:
               _UpperCAmelCase     							=   [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
               _UpperCAmelCase     							=   [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

               _UpperCAmelCase     							=   renew_resnet_paths(__lowercase       )
               _UpperCAmelCase     							=   renew_resnet_paths(__lowercase       )

               _UpperCAmelCase     							=   {"old": f'output_blocks.{i}.0', "new": f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
               assign_to_checkpoint(__lowercase			,     __lowercase			,     __lowercase			,     additional_replacements=[meta_path]			,     config=__lowercase       )

               if ["conv.weight", "conv.bias"] in output_block_list.values():
                    _UpperCAmelCase     							=   list(output_block_list.values()       ).index(["conv.weight", "conv.bias"]       )
                    _UpperCAmelCase     							=   checkpoint[
                        f'output_blocks.{i}.{index}.conv.weight'
                    ]
                    _UpperCAmelCase     							=   checkpoint[
                        f'output_blocks.{i}.{index}.conv.bias'
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(__lowercase       ) == 2:
                         _UpperCAmelCase     							=   []

               if len(__lowercase       ):
                    _UpperCAmelCase     							=   renew_attention_paths(__lowercase       )
                    _UpperCAmelCase     							=   {
                        "old": f'output_blocks.{i}.1',
                        "new": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                    }
                    _UpperCAmelCase     							=   {
                        f'output_blocks.{i}.1.qkv.bias': {
                            "key": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                            "query": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                            "value": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                        },
                        f'output_blocks.{i}.1.qkv.weight': {
                            "key": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                            "query": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                            "value": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                        },
                    }
                    assign_to_checkpoint(
                        __lowercase			,     __lowercase			,     __lowercase			,     additional_replacements=[meta_path]			,     attention_paths_to_split=to_split if any("qkv" in key for key in attentions       ) else None			,     config=__lowercase			,     )
          else:
               _UpperCAmelCase     							=   renew_resnet_paths(__lowercase			,     n_shave_prefix_segments=1       )
               for path in resnet_0_paths:
                    _UpperCAmelCase     							=   ".".join(["output_blocks", str(__lowercase       ), path["old"]]       )
                    _UpperCAmelCase     							=   ".".join(["up_blocks", str(__lowercase       ), "resnets", str(__lowercase       ), path["new"]]       )

                    _UpperCAmelCase     							=   checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :Optional[Any]       =				argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )

  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the architecture.''',
  )

  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

  __SCREAMING_SNAKE_CASE     :Tuple       =				parser.parse_args()

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    __SCREAMING_SNAKE_CASE     :List[Any]       =				json.loads(f.read())

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				convert_ldm_checkpoint(checkpoint, config)

  if "ldm" in config:
    del config["ldm"]

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    __SCREAMING_SNAKE_CASE     :Optional[Any]       =				DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
    __SCREAMING_SNAKE_CASE     :List[str]       =				VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

    __SCREAMING_SNAKE_CASE     :List[str]       =				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)











'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


__SCREAMING_SNAKE_CASE     :int       =				get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')







@require_sentencepiece
@require_tokenizers
class   A_	(  lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : List[str]           =							PegasusTokenizer
 _lowerCamelCase  : int           =							PegasusTokenizerFast
 _lowerCamelCase  : Union[str, Any]           =							True
 _lowerCamelCase  : List[str]           =							True




 def      lowercase	(					self       :    Optional[int]    ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      _UpperCAmelCase     							=   PegasusTokenizer(snake_case_    )
      tokenizer.save_pretrained(self.tmpdirname    )




 @cached_property
 def      lowercase	(					self       :    Tuple    ):
      return PegasusTokenizer.from_pretrained("google/pegasus-large"    )




 def      lowercase	(					self       :    Union[str, Any]	,			**snake_case_       :    Union[str, Any]    ):
      return PegasusTokenizer.from_pretrained(self.tmpdirname	,			**snake_case_    )




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    Any    ):
      return ("This is a test", "This is a test")




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "</s>"
      _UpperCAmelCase     							=   1

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




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   list(self.get_tokenizer().get_vocab().keys()    )

      self.assertEqual(vocab_keys[0]	,			"<pad>"    )
      self.assertEqual(vocab_keys[1]	,			"</s>"    )
      self.assertEqual(vocab_keys[-1]	,			"v"    )
      self.assertEqual(len(snake_case_    )	,			1_1_0_3    )




 def      lowercase	(					self       :    Any    ):
      self.assertEqual(self.get_tokenizer().vocab_size	,			1_1_0_3    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
      _UpperCAmelCase     							=   self.tokenizer_class.from_pretrained(self.tmpdirname    )
      _UpperCAmelCase     							=   (
          "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
          " </s> <pad> <pad> <pad>"
      )
      _UpperCAmelCase     							=   rust_tokenizer([raw_input_str]	,			return_tensors=snake_case_	,			add_special_tokens=snake_case_    ).input_ids[0]
      _UpperCAmelCase     							=   py_tokenizer([raw_input_str]	,			return_tensors=snake_case_	,			add_special_tokens=snake_case_    ).input_ids[0]
      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      _UpperCAmelCase     							=   "<mask_1> To ensure a <mask_2> flow of bank resolutions."
      _UpperCAmelCase     							=   [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      _UpperCAmelCase     							=   tokenizer([raw_input_str]	,			return_tensors=snake_case_    ).input_ids[0]
      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6_1_0_3
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 1_0_3
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_0_2_4
      _UpperCAmelCase     							=   "To ensure a smooth flow of bank resolutions."
      _UpperCAmelCase     							=   [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      _UpperCAmelCase     							=   tokenizer([raw_input_str]	,			return_tensors=snake_case_    ).input_ids[0]
      self.assertListEqual(snake_case_	,			snake_case_    )
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




 @require_torch
 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   ["This is going to be way too long." * 1_5_0, "short example"]
      _UpperCAmelCase     							=   ["not super long but more than 5 tokens", "tiny"]
      _UpperCAmelCase     							=   self._large_tokenizer(snake_case_	,			padding=snake_case_	,			truncation=snake_case_	,			return_tensors="pt"    )
      _UpperCAmelCase     							=   self._large_tokenizer(
          text_target=snake_case_	,			max_length=5	,			padding=snake_case_	,			truncation=snake_case_	,			return_tensors="pt"    )

      assert batch.input_ids.shape == (2, 1_0_2_4)
      assert batch.attention_mask.shape == (2, 1_0_2_4)
      assert targets["input_ids"].shape == (2, 5)
      assert len(snake_case_    ) == 2  # input_ids, attention_mask.




 @slow
 def      lowercase	(					self       :    Dict    ):
      # fmt: off
      _UpperCAmelCase     							=   {"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=snake_case_	,			model_name="google/bigbird-pegasus-large-arxiv"	,			revision="ba85d0851d708441f91440d509690f1ab6353415"	,			)







@require_sentencepiece
@require_tokenizers
class   A_	(  lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : List[str]           =							PegasusTokenizer
 _lowerCamelCase  : List[Any]           =							PegasusTokenizerFast
 _lowerCamelCase  : int           =							True
 _lowerCamelCase  : Union[str, Any]           =							True




 def      lowercase	(					self       :    Any    ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      _UpperCAmelCase     							=   PegasusTokenizer(snake_case_	,			offset=0	,			mask_token_sent=snake_case_	,			mask_token="[MASK]"    )
      tokenizer.save_pretrained(self.tmpdirname    )




 @cached_property
 def      lowercase	(					self       :    Tuple    ):
      return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"    )




 def      lowercase	(					self       :    Optional[Any]	,			**snake_case_       :    Dict    ):
      return PegasusTokenizer.from_pretrained(self.tmpdirname	,			**snake_case_    )




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    str    ):
      return ("This is a test", "This is a test")




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
      _UpperCAmelCase     							=   self.tokenizer_class.from_pretrained(self.tmpdirname    )
      _UpperCAmelCase     							=   (
          "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
          " <pad> <pad> <pad>"
      )
      _UpperCAmelCase     							=   rust_tokenizer([raw_input_str]	,			return_tensors=snake_case_	,			add_special_tokens=snake_case_    ).input_ids[0]
      _UpperCAmelCase     							=   py_tokenizer([raw_input_str]	,			return_tensors=snake_case_	,			add_special_tokens=snake_case_    ).input_ids[0]
      self.assertListEqual(snake_case_	,			snake_case_    )




 @require_torch
 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   ["This is going to be way too long." * 1_0_0_0, "short example"]
      _UpperCAmelCase     							=   ["not super long but more than 5 tokens", "tiny"]
      _UpperCAmelCase     							=   self._large_tokenizer(snake_case_	,			padding=snake_case_	,			truncation=snake_case_	,			return_tensors="pt"    )
      _UpperCAmelCase     							=   self._large_tokenizer(
          text_target=snake_case_	,			max_length=5	,			padding=snake_case_	,			truncation=snake_case_	,			return_tensors="pt"    )

      assert batch.input_ids.shape == (2, 4_0_9_6)
      assert batch.attention_mask.shape == (2, 4_0_9_6)
      assert targets["input_ids"].shape == (2, 5)
      assert len(snake_case_    ) == 2  # input_ids, attention_mask.




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   (
          "This is an example string that is used to test the original TF implementation against the HF"
          " implementation"
      )

      _UpperCAmelCase     							=   self._large_tokenizer(snake_case_    ).input_ids

      self.assertListEqual(
          snake_case_	,			[1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]	,			)




