




'''simple docstring'''




import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
class   __UpperCamelCase       ( unittest.TestCase				):
       lowercase	:    Optional[int]      						=MODEL_FOR_MASKED_LM_MAPPING
       lowercase	:    Any      						=TF_MODEL_FOR_MASKED_LM_MAPPING






       def      lowercase__     (							self  ):



              """simple docstring"""







              super().tearDown()
              # clean-up as much as possible GPU memory occupied by PyTorch
              gc.collect()
              if is_torch_available():
                     import torch

                     torch.cuda.empty_cache()






       @require_tf
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=pipeline(task='''fill-mask''',		model='''sshleifer/tiny-distilroberta-base''',		top_k=2,		framework='''tf'''  )
              lowerCamelCase_							=unmasker('''My name is <mask>'''  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase,		decimals=6  ),		[
                      {'''sequence''': '''My name is grouped''', '''score''': 2.1e-05, '''token''': 38_015, '''token_str''': ''' grouped'''},
                      {'''sequence''': '''My name is accuser''', '''score''': 2.1e-05, '''token''': 25_506, '''token_str''': ''' accuser'''},
                  ],		)

              lowerCamelCase_							=unmasker('''The largest city in France is <mask>'''  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase,		decimals=6  ),		[
                      {
                          '''sequence''': '''The largest city in France is grouped''',
                          '''score''': 2.1e-05,
                          '''token''': 38_015,
                          '''token_str''': ''' grouped''',
                      },
                      {
                          '''sequence''': '''The largest city in France is accuser''',
                          '''score''': 2.1e-05,
                          '''token''': 25_506,
                          '''token_str''': ''' accuser''',
                      },
                  ],		)

              lowerCamelCase_							=unmasker('''My name is <mask>''',		targets=[''' Patrick''', ''' Clara''', ''' Teven'''],		top_k=3  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase,		decimals=6  ),		[
                      {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 13_606, '''token_str''': ''' Clara'''},
                      {'''sequence''': '''My name is Patrick''', '''score''': 2e-05, '''token''': 3_499, '''token_str''': ''' Patrick'''},
                      {'''sequence''': '''My name is Te''', '''score''': 1.9e-05, '''token''': 2_941, '''token_str''': ''' Te'''},
                  ],		)






       @require_torch
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=pipeline(task='''fill-mask''',		model='''sshleifer/tiny-distilroberta-base''',		top_k=2,		framework='''pt'''  )

              lowerCamelCase_							=unmasker('''My name is <mask>'''  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase,		decimals=6  ),		[
                      {'''sequence''': '''My name is Maul''', '''score''': 2.2e-05, '''token''': 35_676, '''token_str''': ''' Maul'''},
                      {'''sequence''': '''My name isELS''', '''score''': 2.2e-05, '''token''': 16_416, '''token_str''': '''ELS'''},
                  ],		)

              lowerCamelCase_							=unmasker('''The largest city in France is <mask>'''  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase,		decimals=6  ),		[
                      {
                          '''sequence''': '''The largest city in France is Maul''',
                          '''score''': 2.2e-05,
                          '''token''': 35_676,
                          '''token_str''': ''' Maul''',
                      },
                      {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-05, '''token''': 16_416, '''token_str''': '''ELS'''},
                  ],		)

              lowerCamelCase_							=unmasker('''My name is <mask>''',		targets=[''' Patrick''', ''' Clara''', ''' Teven'''],		top_k=3  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase,		decimals=6  ),		[
                      {'''sequence''': '''My name is Patrick''', '''score''': 2.1e-05, '''token''': 3_499, '''token_str''': ''' Patrick'''},
                      {'''sequence''': '''My name is Te''', '''score''': 2e-05, '''token''': 2_941, '''token_str''': ''' Te'''},
                      {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 13_606, '''token_str''': ''' Clara'''},
                  ],		)

              lowerCamelCase_							=unmasker('''My name is <mask> <mask>''',		top_k=2  )

              self.assertEqual(
                  nested_simplify(lowerCAmelCase,		decimals=6  ),		[
                      [
                          {
                              '''score''': 2.2e-05,
                              '''token''': 35_676,
                              '''token_str''': ''' Maul''',
                              '''sequence''': '''<s>My name is Maul<mask></s>''',
                          },
                          {'''score''': 2.2e-05, '''token''': 16_416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
                      ],
                      [
                          {
                              '''score''': 2.2e-05,
                              '''token''': 35_676,
                              '''token_str''': ''' Maul''',
                              '''sequence''': '''<s>My name is<mask> Maul</s>''',
                          },
                          {'''score''': 2.2e-05, '''token''': 16_416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
                      ],
                  ],		)






       @require_torch_gpu
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=pipeline('''fill-mask''',		model='''hf-internal-testing/tiny-random-distilbert''',		device=0,		framework='''pt'''  )

              # convert model to fp16
              pipe.model.half()

              lowerCamelCase_							=pipe('''Paris is the [MASK] of France.'''  )
              # We actually don't care about the result, we just want to make sure
              # it works, meaning the float16 tensor got casted back to float32
              # for postprocessing.
              self.assertIsInstance(lowerCAmelCase,		lowerCAmelCase  )






       @slow
       @require_torch
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=pipeline(task='''fill-mask''',		model='''distilroberta-base''',		top_k=2,		framework='''pt'''  )
              self.run_large_test(lowerCAmelCase  )






       @slow
       @require_tf
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=pipeline(task='''fill-mask''',		model='''distilroberta-base''',		top_k=2,		framework='''tf'''  )
              self.run_large_test(lowerCAmelCase  )






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=unmasker('''My name is <mask>'''  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase  ),		[
                      {'''sequence''': '''My name is John''', '''score''': 0.0_0_8, '''token''': 610, '''token_str''': ''' John'''},
                      {'''sequence''': '''My name is Chris''', '''score''': 0.0_0_7, '''token''': 1_573, '''token_str''': ''' Chris'''},
                  ],		)
              lowerCamelCase_							=unmasker('''The largest city in France is <mask>'''  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase  ),		[
                      {
                          '''sequence''': '''The largest city in France is Paris''',
                          '''score''': 0.2_5_1,
                          '''token''': 2_201,
                          '''token_str''': ''' Paris''',
                      },
                      {
                          '''sequence''': '''The largest city in France is Lyon''',
                          '''score''': 0.2_1_4,
                          '''token''': 12_790,
                          '''token_str''': ''' Lyon''',
                      },
                  ],		)

              lowerCamelCase_							=unmasker('''My name is <mask>''',		targets=[''' Patrick''', ''' Clara''', ''' Teven'''],		top_k=3  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase  ),		[
                      {'''sequence''': '''My name is Patrick''', '''score''': 0.0_0_5, '''token''': 3_499, '''token_str''': ''' Patrick'''},
                      {'''sequence''': '''My name is Clara''', '''score''': 0.0_0_0, '''token''': 13_606, '''token_str''': ''' Clara'''},
                      {'''sequence''': '''My name is Te''', '''score''': 0.0_0_0, '''token''': 2_941, '''token_str''': ''' Te'''},
                  ],		)






       @require_torch
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=pipeline(task='''fill-mask''',		model='''sshleifer/tiny-distilroberta-base''',		framework='''pt'''  )
              lowerCamelCase_							=None
              lowerCamelCase_							=None
              self.run_pipeline_test(lowerCAmelCase,		[]  )






       @require_tf
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=pipeline(task='''fill-mask''',		model='''sshleifer/tiny-distilroberta-base''',		framework='''tf'''  )
              lowerCamelCase_							=None
              lowerCamelCase_							=None
              self.run_pipeline_test(lowerCAmelCase,		[]  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              if tokenizer is None or tokenizer.mask_token_id is None:
                     self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)'''  )

              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase  )
              lowerCamelCase_							=[
                  f'''This is another {tokenizer.mask_token} test''',
              ]
              return fill_masker, examples






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=fill_masker.tokenizer
              lowerCamelCase_							=fill_masker.model

              lowerCamelCase_							=fill_masker(
                  f'''This is a {tokenizer.mask_token}''',		)
              self.assertEqual(
                  lowerCAmelCase,		[
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                  ],		)

              lowerCamelCase_							=fill_masker([f'''This is a {tokenizer.mask_token}''']  )
              self.assertEqual(
                  lowerCAmelCase,		[
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                  ],		)

              lowerCamelCase_							=fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.''']  )
              self.assertEqual(
                  lowerCAmelCase,		[
                      [
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      ],
                      [
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      ],
                  ],		)

              with self.assertRaises(lowerCAmelCase  ):
                     fill_masker([None]  )
              # No mask_token is not supported
              with self.assertRaises(lowerCAmelCase  ):
                     fill_masker('''This is'''  )

              self.run_test_top_k(lowerCAmelCase,		lowerCAmelCase  )
              self.run_test_targets(lowerCAmelCase,		lowerCAmelCase  )
              self.run_test_top_k_targets(lowerCAmelCase,		lowerCAmelCase  )
              self.fill_mask_with_duplicate_targets_and_top_k(lowerCAmelCase,		lowerCAmelCase  )
              self.fill_mask_with_multiple_masks(lowerCAmelCase,		lowerCAmelCase  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=tokenizer.get_vocab()
              lowerCamelCase_							=sorted(vocab.keys()  )[:2]
              # Pipeline argument
              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase,		targets=lowerCAmelCase  )
              lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}'''  )
              self.assertEqual(
                  lowerCAmelCase,		[
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                  ],		)
              lowerCamelCase_							={vocab[el] for el in targets}
              self.assertEqual({el['''token'''] for el in outputs},		lowerCAmelCase  )
              lowerCamelCase_							=[tokenizer.decode([x]  ) for x in target_ids]
              self.assertEqual({el['''token_str'''] for el in outputs},		set(lowerCAmelCase  )  )

              # Call argument
              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase  )
              lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		targets=lowerCAmelCase  )
              self.assertEqual(
                  lowerCAmelCase,		[
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                  ],		)
              lowerCamelCase_							={vocab[el] for el in targets}
              self.assertEqual({el['''token'''] for el in outputs},		lowerCAmelCase  )
              lowerCamelCase_							=[tokenizer.decode([x]  ) for x in target_ids]
              self.assertEqual({el['''token_str'''] for el in outputs},		set(lowerCAmelCase  )  )

              # Score equivalence
              lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		targets=lowerCAmelCase  )
              lowerCamelCase_							=[top_mask['''token_str'''] for top_mask in outputs]
              lowerCamelCase_							=[top_mask['''score'''] for top_mask in outputs]

              # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
              if set(lowerCAmelCase  ) == set(lowerCAmelCase  ):
                     lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		targets=lowerCAmelCase  )
                     lowerCamelCase_							=[top_mask['''score'''] for top_mask in unmasked_targets]
                     self.assertEqual(nested_simplify(lowerCAmelCase  ),		nested_simplify(lowerCAmelCase  )  )

              # Raises with invalid
              with self.assertRaises(lowerCAmelCase  ):
                     lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		targets=[]  )
              # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
              if "" not in tokenizer.get_vocab():
                     with self.assertRaises(lowerCAmelCase  ):
                            lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		targets=['''''']  )
                     with self.assertRaises(lowerCAmelCase  ):
                            lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		targets=''''''  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase,		top_k=2  )
              lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}'''  )
              self.assertEqual(
                  lowerCAmelCase,		[
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                  ],		)

              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase  )
              lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		top_k=2  )
              self.assertEqual(
                  lowerCAmelCase,		[
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                  ],		)
              self.assertEqual(nested_simplify(lowerCAmelCase  ),		nested_simplify(lowerCAmelCase  )  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=tokenizer.get_vocab()
              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase  )

              # top_k=2, ntargets=3
              lowerCamelCase_							=sorted(vocab.keys()  )[:3]
              lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		top_k=2,		targets=lowerCAmelCase  )

              # If we use the most probably targets, and filter differently, we should still
              # have the same results
              lowerCamelCase_							=[el['''token_str'''] for el in sorted(lowerCAmelCase,		key=lambda lowerCAmelCase  : x["score"],		reverse=lowerCAmelCase  )]
              # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
              if set(lowerCAmelCase  ).issubset(lowerCAmelCase  ):
                     lowerCamelCase_							=fill_masker(f'''This is a {tokenizer.mask_token}''',		top_k=3,		targets=lowerCAmelCase  )
                     # They should yield exactly the same result
                     self.assertEqual(nested_simplify(lowerCAmelCase  ),		nested_simplify(lowerCAmelCase  )  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase  )
              lowerCamelCase_							=tokenizer.get_vocab()
              # String duplicates + id duplicates
              lowerCamelCase_							=sorted(vocab.keys()  )[:3]
              lowerCamelCase_							=[targets[0], targets[1], targets[0], targets[2], targets[1]]
              lowerCamelCase_							=fill_masker(f'''My name is {tokenizer.mask_token}''',		targets=lowerCAmelCase,		top_k=10  )

              # The target list contains duplicates, so we can't output more
              # than them
              self.assertEqual(len(lowerCAmelCase  ),		3  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=FillMaskPipeline(model=lowerCAmelCase,		tokenizer=lowerCAmelCase  )

              lowerCamelCase_							=fill_masker(
                  f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}''',		top_k=2  )
              self.assertEqual(
                  lowerCAmelCase,		[
                      [
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      ],
                      [
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      ],
                      [
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                          {'''sequence''': ANY(lowerCAmelCase  ), '''score''': ANY(lowerCAmelCase  ), '''token''': ANY(lowerCAmelCase  ), '''token_str''': ANY(lowerCAmelCase  )},
                      ],
                  ],		)









"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   '▁'

lowerCAmelCase_   						=   {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase_   						=   {
    'facebook/xglm-564M': 2_048,
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Any   	=				PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					str   	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					int   	=				["input_ids", "attention_mask"]







		def __init__(	self						:  int			,_snake_case						:  Dict			,_snake_case						:  Dict="<s>"			,_snake_case						:  Dict="</s>"			,_snake_case						:  str="</s>"			,_snake_case						:  Optional[Any]="<s>"			,_snake_case						:  Optional[Any]="<unk>"			,_snake_case						:  Optional[int]="<pad>"			,_snake_case						:  Optional[Dict[str, Any]] = None			,**_snake_case						:  str			,)					->		None:






				"""simple docstring"""



				lowercase__   :    Any				=			{} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				lowercase__   :    Any				=			7
				lowercase__   :    Optional[int]				=			[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

				lowercase__   :    Dict				=			kwargs.get('''additional_special_tokens'''			,[]						)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=_snake_case			,eos_token=_snake_case			,unk_token=_snake_case			,sep_token=_snake_case			,cls_token=_snake_case			,pad_token=_snake_case			,sp_model_kwargs=self.sp_model_kwargs			,**_snake_case			,)

				lowercase__   :    List[str]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(str(_snake_case						)						)
				lowercase__   :    str				=			vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowercase__   :    Optional[int]				=			1

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowercase__   :    Optional[int]				=			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

				lowercase__   :    List[str]				=			len(self.sp_model						)
				lowercase__   :    Tuple				=			{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words						)}
				self.fairseq_tokens_to_ids.update(_snake_case						)

				lowercase__   :    Union[str, Any]				=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}







		def __getstate__(	self						:  int						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.__dict__.copy()
				lowercase__   :    Optional[int]				=			None
				lowercase__   :    Any				=			self.sp_model.serialized_model_proto()
				return state







		def __setstate__(	self						:  Dict			,_snake_case						:  List[str]						)					->		Any:






				"""simple docstring"""



				lowercase__   :    int				=			d

				# for backward compatibility
				if not hasattr(self			,'''sp_model_kwargs'''						):
						lowercase__   :    Dict				=			{}

				lowercase__   :    Optional[int]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
				lowercase__   :    Optional[Any]				=			[self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None			,_snake_case						:  bool = False						)					->		List[int]:






				"""simple docstring"""



				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=_snake_case			,token_ids_a=_snake_case			,already_has_special_tokens=_snake_case						)

				if token_ids_a is None:
						return [1] + ([0] * len(_snake_case						))
				return [1] + ([0] * len(_snake_case						)) + [1, 1] + ([0] * len(_snake_case						))







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			[self.sep_token_id]

				if token_ids_a is None:
						return len(sep + token_ids_a						) * [0]
				return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]







		@property
		def 					UpperCAmelCase    (	self						:  str						)					->		Tuple:






				"""simple docstring"""



				return len(self.sp_model						) + self.fairseq_offset + self.num_madeup_words







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			{self.convert_ids_to_tokens(_snake_case						): i for i in range(self.vocab_size						)}
				vocab.update(self.added_tokens_encoder						)
				return vocab







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  str						)					->		List[str]:






				"""simple docstring"""



				return self.sp_model.encode(_snake_case			,out_type=_snake_case						)







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Optional[int]						)					->		List[Any]:






				"""simple docstring"""



				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				lowercase__   :    Tuple				=			self.sp_model.PieceToId(_snake_case						)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[str]						)					->		Any:






				"""simple docstring"""



				if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset						)







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Tuple						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			''''''.join(_snake_case						).replace(_snake_case			,''' '''						).strip()
				return out_string







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  str			,_snake_case						:  Optional[str] = None						)					->		Tuple[str]:






				"""simple docstring"""



				if not os.path.isdir(_snake_case						):
						logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
						return
				lowercase__   :    Any				=			os.path.join(
				    _snake_case			,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(_snake_case						) and os.path.isfile(self.vocab_file						):
						copyfile(self.vocab_file			,_snake_case						)
				elif not os.path.isfile(self.vocab_file						):
						with open(_snake_case			,'''wb'''						) as fi:
								lowercase__   :    Dict				=			self.sp_model.serialized_model_proto()
								fi.write(_snake_case						)

				return (out_vocab_file,)
