
import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_							=     logging.get_logger(__name__)






def 						__magic_name__ (    __a			:       List[Any]       ,		__a			:       Optional[Any]						):



     '''simple docstring'''




     UpperCamelCase__				=				[]
     for i in range(encoder_config.num_hidden_layers						):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append(
              (f"encoder.deit.blocks.{i}.norm1.weight", f"encoder.encoder.layer.{i}.layernorm_before.weight")						)
          rename_keys.append((f"encoder.deit.blocks.{i}.norm1.bias", f"encoder.encoder.layer.{i}.layernorm_before.bias")						)
          rename_keys.append(
              (f"encoder.deit.blocks.{i}.attn.proj.weight", f"encoder.encoder.layer.{i}.attention.output.dense.weight")						)
          rename_keys.append(
              (f"encoder.deit.blocks.{i}.attn.proj.bias", f"encoder.encoder.layer.{i}.attention.output.dense.bias")						)
          rename_keys.append(
              (f"encoder.deit.blocks.{i}.norm2.weight", f"encoder.encoder.layer.{i}.layernorm_after.weight")						)
          rename_keys.append((f"encoder.deit.blocks.{i}.norm2.bias", f"encoder.encoder.layer.{i}.layernorm_after.bias")						)
          rename_keys.append(
              (f"encoder.deit.blocks.{i}.mlp.fc1.weight", f"encoder.encoder.layer.{i}.intermediate.dense.weight")						)
          rename_keys.append(
              (f"encoder.deit.blocks.{i}.mlp.fc1.bias", f"encoder.encoder.layer.{i}.intermediate.dense.bias")						)
          rename_keys.append(
              (f"encoder.deit.blocks.{i}.mlp.fc2.weight", f"encoder.encoder.layer.{i}.output.dense.weight")						)
          rename_keys.append((f"encoder.deit.blocks.{i}.mlp.fc2.bias", f"encoder.encoder.layer.{i}.output.dense.bias")						)

     # cls token, position embeddings and patch embeddings of encoder
     rename_keys.extend(
         [
             ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
             ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
             ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
             ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
             ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
             ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
         ]						)

     return rename_keys






def 						__magic_name__ (    __a			:       int       ,		__a			:       List[str]						):



     '''simple docstring'''




     for i in range(encoder_config.num_hidden_layers						):
          # queries, keys and values (only weights, no biases)
          UpperCamelCase__				=				state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight"						)

          UpperCamelCase__				=				in_proj_weight[
              : encoder_config.hidden_size, :
          ]
          UpperCamelCase__				=				in_proj_weight[
              encoder_config.hidden_size : encoder_config.hidden_size * 2, :
          ]
          UpperCamelCase__				=				in_proj_weight[
              -encoder_config.hidden_size :, :
          ]






def 						__magic_name__ (    __a			:       List[str]       ,		__a			:       int       ,		__a			:       Any						):



     '''simple docstring'''




     UpperCamelCase__				=				dct.pop(__UpperCamelCase						)
     UpperCamelCase__				=				val






def 						__magic_name__ (    __a			:       List[Any]						):



     '''simple docstring'''




     if "handwritten" in checkpoint_url:
          UpperCamelCase__				=				"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
          # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
          # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
          # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
          # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
     elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
          UpperCamelCase__				=				"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
     UpperCamelCase__				=				Image.open(requests.get(__UpperCamelCase       ,		stream=__UpperCamelCase						).raw						).convert("""RGB"""						)
     return im






@torch.no_grad()
def 						__magic_name__ (    __a			:       int       ,		__a			:       str						):



     '''simple docstring'''




     UpperCamelCase__				=				ViTConfig(image_size=384       ,		qkv_bias=__UpperCamelCase						)
     UpperCamelCase__				=				TrOCRConfig()

     # size of the architecture
     if "base" in checkpoint_url:
          UpperCamelCase__				=				768
     elif "large" in checkpoint_url:
          # use ViT-large encoder
          UpperCamelCase__				=				1_024
          UpperCamelCase__				=				4_096
          UpperCamelCase__				=				24
          UpperCamelCase__				=				16
          UpperCamelCase__				=				1_024
     else:
          raise ValueError("""Should either find \'base\' or \'large\' in checkpoint URL"""						)

     # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
     if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
          UpperCamelCase__				=				False
          UpperCamelCase__				=				"""relu"""
          UpperCamelCase__				=				1_024
          UpperCamelCase__				=				True
          UpperCamelCase__				=				False
          UpperCamelCase__				=				False

     # load HuggingFace model
     UpperCamelCase__				=				ViTModel(__UpperCamelCase       ,		add_pooling_layer=__UpperCamelCase						)
     UpperCamelCase__				=				TrOCRForCausalLM(__UpperCamelCase						)
     UpperCamelCase__				=				VisionEncoderDecoderModel(encoder=__UpperCamelCase       ,		decoder=__UpperCamelCase						)
     model.eval()

     # load state_dict of original model, rename some keys
     UpperCamelCase__				=				torch.hub.load_state_dict_from_url(__UpperCamelCase       ,		map_location="""cpu"""       ,		check_hash=__UpperCamelCase						)["""model"""]

     UpperCamelCase__				=				create_rename_keys(__UpperCamelCase       ,		__UpperCamelCase						)
     for src, dest in rename_keys:
          rename_key(__UpperCamelCase       ,		__UpperCamelCase       ,		__UpperCamelCase						)
     read_in_q_k_v(__UpperCamelCase       ,		__UpperCamelCase						)

     # remove parameters we don't need
     del state_dict["encoder.deit.head.weight"]
     del state_dict["encoder.deit.head.bias"]
     del state_dict["decoder.version"]

     # add prefix to decoder keys
     for key, val in state_dict.copy().items():
          UpperCamelCase__				=				state_dict.pop(__UpperCamelCase						)
          if key.startswith("""decoder"""						) and "output_projection" not in key:
               UpperCamelCase__				=				val
          else:
               UpperCamelCase__				=				val

          # load state dict
     model.load_state_dict(__UpperCamelCase						)

     # Check outputs on an image
     UpperCamelCase__				=				ViTImageProcessor(size=encoder_config.image_size						)
     UpperCamelCase__				=				RobertaTokenizer.from_pretrained("""roberta-large"""						)
     UpperCamelCase__				=				TrOCRProcessor(__UpperCamelCase       ,		__UpperCamelCase						)

     UpperCamelCase__				=				processor(images=prepare_img(__UpperCamelCase						)       ,		return_tensors="""pt"""						).pixel_values

     # verify logits
     UpperCamelCase__				=				torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
     UpperCamelCase__				=				model(pixel_values=__UpperCamelCase       ,		decoder_input_ids=__UpperCamelCase						)
     UpperCamelCase__				=				outputs.logits

     UpperCamelCase__				=				torch.Size([1, 1, 50_265]						)
     if "trocr-base-handwritten" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]						)
     elif "trocr-large-handwritten" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]						)
     elif "trocr-base-printed" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]						)
     elif "trocr-large-printed" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]						)

     if "stage1" not in checkpoint_url:
          assert logits.shape == expected_shape, "Shape of logits not as expected"
          assert torch.allclose(logits[0, 0, :10]       ,		__UpperCamelCase       ,		atol=1E-3						), "First elements of logits not as expected"

     Path(__UpperCamelCase						).mkdir(exist_ok=__UpperCamelCase						)
     print(f"Saving model to {pytorch_dump_folder_path}"						)
     model.save_pretrained(__UpperCamelCase						)
     print(f"Saving processor to {pytorch_dump_folder_path}"						)
     processor.save_pretrained(__UpperCamelCase						)


if __name__ == "__main__":
    lowerCamelCase_							=     argparse.ArgumentParser()

    parser.add_argument(
        '''--checkpoint_url''',
        default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
        type=str,
        help='''URL to the original PyTorch checkpoint (.pth file).''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
    )
    lowerCamelCase_							=     parser.parse_args()
    convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_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


lowercase_				     =     get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Optional[Any]						 = PegasusTokenizer
   A_						:	int						 = PegasusTokenizerFast
   A_						:	Optional[Any]						 = True
   A_						:	Union[str, Any]						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

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

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


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/pegasus-large'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= '''</s>'''
     __A									= 1

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= 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(_lowerCamelCase  ),       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     self.assertEqual(self.get_tokenizer().vocab_size,       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''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>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # <mask_1> masks whole sentence while <mask_2> masks single word
     __A									= '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
     __A									= [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # The tracebacks for the following asserts are **better** without messages or self.assertEqual
     assert tokenizer.vocab_size == 9_61_03
     assert tokenizer.pad_token_id == 0
     assert tokenizer.eos_token_id == 1
     assert tokenizer.offset == 1_03
     assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
     assert tokenizer.unk_token == "<unk>"
     assert tokenizer.model_max_length == 10_24
     __A									= '''To ensure a smooth flow of bank resolutions.'''
     __A									= [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 1_50, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 10_24)
     assert batch.attention_mask.shape == (2, 10_24)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # fmt: off
     __A									= {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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=_lowerCamelCase,       model_name='''google/bigbird-pegasus-large-arxiv''',       revision='''ba85d0851d708441f91440d509690f1ab6353415''',       )





@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = PegasusTokenizer
   A_						:	Union[str, Any]						 = PegasusTokenizerFast
   A_						:	Any						 = True
   A_						:	str						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

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

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


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
         ''' <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 10_00, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 40_96)
     assert batch.attention_mask.shape == (2, 40_96)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= (
         '''This is an example string that is used to test the original TF implementation against the HF'''
         ''' implementation'''
     )

     __A									= self._large_tokenizer(_lowerCamelCase  ).input_ids

     self.assertListEqual(
         _lowerCamelCase,       [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1],       )




