


import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=99					,  _lowerCamelCase=13					,  _lowerCamelCase=16					,  _lowerCamelCase=7					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=False					,  _lowerCamelCase=True					,  _lowerCamelCase=2					,  _lowerCamelCase=32					,  _lowerCamelCase=4					,  _lowerCamelCase=4					,  _lowerCamelCase=30					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=None					,  ):
       a						:Union[str, Any] 							=       parent
       a						:Dict 							=       batch_size
       a						:Tuple 							=       decoder_seq_length
       # For common tests
       a						:int 							=       self.decoder_seq_length
       a						:Optional[int] 							=       is_training
       a						:Optional[Any] 							=       use_attention_mask
       a						:Tuple 							=       use_labels

       a						:Any 							=       vocab_size
       a						:Union[str, Any] 							=       d_model
       a						:str 							=       d_model
       a						:int 							=       decoder_layers
       a						:Tuple 							=       decoder_layers
       a						:Optional[int] 							=       decoder_ffn_dim
       a						:str 							=       decoder_attention_heads
       a						:Optional[int] 							=       decoder_attention_heads
       a						:List[Any] 							=       eos_token_id
       a						:Tuple 							=       bos_token_id
       a						:Any 							=       pad_token_id
       a						:Union[str, Any] 							=       decoder_start_token_id
       a						:Optional[Any] 							=       use_cache
       a						:Optional[Any] 							=       max_position_embeddings

       a						:Dict 							=       None
       a						:Optional[Any] 							=       decoder_seq_length
       a						:Optional[int] 							=       2
       a						:int 							=       1




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size    )

       a						:int 							=       None
       if self.use_attention_mask:
             a						:Optional[int] 							=       ids_tensor([self.batch_size, self.decoder_seq_length]					,  vocab_size=2    )

       a						:Dict 							=       None
       if self.use_labels:
             a						:Any 							=       ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size    )

       a						:List[Any] 							=       TrOCRConfig(
           vocab_size=self.vocab_size					,  d_model=self.d_model					,  decoder_layers=self.decoder_layers					,  decoder_ffn_dim=self.decoder_ffn_dim					,  decoder_attention_heads=self.decoder_attention_heads					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.bos_token_id					,  use_cache=self.use_cache					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  max_position_embeddings=self.max_position_embeddings					,  )

       return (config, input_ids, attention_mask, lm_labels)




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       a						:int 							=       True
       a						:Any 							=       TrOCRDecoder(config=_lowerCamelCase    ).to(_lowerCamelCase    ).eval()
       a						:Tuple 							=       input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       a						:Any 							=       model(_lowerCamelCase					,  use_cache=_lowerCamelCase    )
       a						:List[Any] 							=       model(_lowerCamelCase    )
       a						:Union[str, Any] 							=       model(_lowerCamelCase					,  use_cache=_lowerCamelCase    )

       self.parent.assertTrue(len(_lowerCamelCase    ) == len(_lowerCamelCase    )    )
       self.parent.assertTrue(len(_lowerCamelCase    ) == len(_lowerCamelCase    ) + 1    )

       a						:Optional[Any] 							=       outputs['''past_key_values''']

       # create hypothetical next token and extent to next_input_ids
       a						:int 							=       ids_tensor((2, 1)					,  config.vocab_size - 1    ) + 1

       # append to next input_ids and
       a						:str 							=       torch.cat([input_ids, next_tokens]					,  dim=-1    )

       a						:List[Any] 							=       model(_lowerCamelCase    )['''last_hidden_state''']
       a						:List[Any] 							=       model(_lowerCamelCase					,  past_key_values=_lowerCamelCase    )['''last_hidden_state''']

       # select random slice
       a						:List[str] 							=       ids_tensor((1,)					,  output_from_past.shape[-1]    ).item()
       a						:Optional[int] 							=       output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       a						:List[Any] 							=       output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(_lowerCamelCase					,  _lowerCamelCase					,  atol=1e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.prepare_config_and_inputs()
       a							,				a							,				a							,				a						:Union[str, Any] 							=       config_and_inputs

       a						:Optional[Any] 							=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_torch
class 		_snake_case  (      _snake_case						,    _snake_case						,    _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE__      =       (TrOCRForCausalLM,) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE__      =       {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
 SCREAMING_SNAKE_CASE__      =       True
 SCREAMING_SNAKE_CASE__      =       False




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       TrOCRStandaloneDecoderModelTester(self					,  is_training=_lowerCamelCase    )
       a						:Tuple 							=       ConfigTester(self					,  config_class=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.config_tester.run_common_tests()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return




 @unittest.skip('''The model doesn\'t support left padding'''    )  # and it's not used enough to be worth fixing :)
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass






"""simple docstring"""




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a      :		Optional[int]           =   get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizer
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizerFast
     _UpperCamelCase							:			Dict							=		True
     _UpperCamelCase							:			Tuple							=		True






     def     __A  (		self  ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     """<pad>"""
       _lowerCAmelCase						:       List[Any]  			=     1

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]				,     """<s>"""  )
       self.assertEqual(vocab_keys[1]				,     """<pad>"""  )
       self.assertEqual(len(a__  )				,     1008  )






     def     __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size				,     1008  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )

       _lowerCAmelCase						:       Dict  			=     tokenizer.tokenize("""This is a test"""  )
       self.assertListEqual(a__				,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a__  )				,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,     )

       _lowerCAmelCase						:       Any  			=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
       self.assertListEqual(
           a__				,     [
               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""",
               """.""",
           ]				,     )
       _lowerCAmelCase						:       List[str]  			=     tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(
           a__				,     [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]				,     )

       _lowerCAmelCase						:       Optional[int]  			=     tokenizer.convert_ids_to_tokens(a__  )
       self.assertListEqual(
           a__				,     [
               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     __A  (		self  ):
       return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )






     def     __A  (		self  ):
       with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(a__				,     f.name  )
         _lowerCAmelCase						:       Union[str, Any]  			=     XGLMTokenizer(f.name				,     keep_accents=a__  )
         _lowerCAmelCase						:       List[str]  			=     pickle.dumps(a__  )
       pickle.loads(a__  )






     def     __A  (		self  ):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase						:       List[str]  			=     self.get_tokenizer()
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_rust_tokenizer()

       _lowerCAmelCase						:       Tuple  			=     """I was born in 92000, and this is falsé."""

       _lowerCAmelCase						:       List[Any]  			=     tokenizer.tokenize(a__  )
       _lowerCAmelCase						:       Tuple  			=     rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
       _lowerCAmelCase						:       str  			=     rust_tokenizer.encode(a__				,     add_special_tokens=a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       int  			=     self.get_rust_tokenizer()
       _lowerCAmelCase						:       Dict  			=     tokenizer.encode(a__  )
       _lowerCAmelCase						:       List[Any]  			=     rust_tokenizer.encode(a__  )
       self.assertListEqual(a__				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """Hello World!"""
       _lowerCAmelCase						:       Optional[int]  			=     [2, 31227, 4447, 35]

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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     (
           """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"""
       )
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
       # fmt: on

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






     @slow
     def     __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     {
           """input_ids""": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
           """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, 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

       self.tokenizer_integration_test_util(
           expected_encoding=a__				,     model_name="""facebook/xglm-564M"""				,     padding=a__				,     )




