
"""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       lowerCAmelCase__				(						unittest.TestCase	):
     __a						=					MODEL_FOR_MASKED_LM_MAPPING
     __a						=					TF_MODEL_FOR_MASKED_LM_MAPPING







     def      lowercase	(							self			:			Any			):
       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			:			Optional[int]			):
       _snake_case					     =       pipeline(task='''fill-mask'''    ,      model='''sshleifer/tiny-distilroberta-base'''    ,      top_k=2    ,      framework='''tf'''			)
       _snake_case					     =       unmasker('''My name is <mask>'''			)
       self.assertEqual(
           nested_simplify(_lowerCamelCase    ,      decimals=6			)    ,      [
               {'''sequence''': '''My name is grouped''', '''score''': 2.1e-05, '''token''': 38015, '''token_str''': ''' grouped'''},
               {'''sequence''': '''My name is accuser''', '''score''': 2.1e-05, '''token''': 25506, '''token_str''': ''' accuser'''},
           ]    ,      )

       _snake_case					     =       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''': 38015,
                   '''token_str''': ''' grouped''',
               },
               {
                   '''sequence''': '''The largest city in France is accuser''',
                   '''score''': 2.1e-05,
                   '''token''': 25506,
                   '''token_str''': ''' accuser''',
               },
           ]    ,      )

       _snake_case					     =       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''': 13606, '''token_str''': ''' Clara'''},
               {'''sequence''': '''My name is Patrick''', '''score''': 2e-05, '''token''': 3499, '''token_str''': ''' Patrick'''},
               {'''sequence''': '''My name is Te''', '''score''': 1.9e-05, '''token''': 2941, '''token_str''': ''' Te'''},
           ]    ,      )







     @require_torch
     def      lowercase	(							self			:			Any			):
       _snake_case					     =       pipeline(task='''fill-mask'''    ,      model='''sshleifer/tiny-distilroberta-base'''    ,      top_k=2    ,      framework='''pt'''			)

       _snake_case					     =       unmasker('''My name is <mask>'''			)
       self.assertEqual(
           nested_simplify(_lowerCamelCase    ,      decimals=6			)    ,      [
               {'''sequence''': '''My name is Maul''', '''score''': 2.2e-05, '''token''': 35676, '''token_str''': ''' Maul'''},
               {'''sequence''': '''My name isELS''', '''score''': 2.2e-05, '''token''': 16416, '''token_str''': '''ELS'''},
           ]    ,      )

       _snake_case					     =       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''': 35676,
                   '''token_str''': ''' Maul''',
               },
               {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-05, '''token''': 16416, '''token_str''': '''ELS'''},
           ]    ,      )

       _snake_case					     =       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''': 3499, '''token_str''': ''' Patrick'''},
               {'''sequence''': '''My name is Te''', '''score''': 2e-05, '''token''': 2941, '''token_str''': ''' Te'''},
               {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 13606, '''token_str''': ''' Clara'''},
           ]    ,      )

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

       self.assertEqual(
           nested_simplify(_lowerCamelCase    ,      decimals=6			)    ,      [
               [
                   {
                       '''score''': 2.2e-05,
                       '''token''': 35676,
                       '''token_str''': ''' Maul''',
                       '''sequence''': '''<s>My name is Maul<mask></s>''',
                   },
                   {'''score''': 2.2e-05, '''token''': 16416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
               ],
               [
                   {
                       '''score''': 2.2e-05,
                       '''token''': 35676,
                       '''token_str''': ''' Maul''',
                       '''sequence''': '''<s>My name is<mask> Maul</s>''',
                   },
                   {'''score''': 2.2e-05, '''token''': 16416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
               ],
           ]    ,      )







     @require_torch_gpu
     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       pipeline('''fill-mask'''    ,      model='''hf-internal-testing/tiny-random-distilbert'''    ,      device=0    ,      framework='''pt'''			)

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

       _snake_case					     =       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			:			Any			):
       _snake_case					     =       pipeline(task='''fill-mask'''    ,      model='''distilroberta-base'''    ,      top_k=2    ,      framework='''pt'''			)
       self.run_large_test(_lowerCamelCase			)







     @slow
     @require_tf
     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       pipeline(task='''fill-mask'''    ,      model='''distilroberta-base'''    ,      top_k=2    ,      framework='''tf'''			)
       self.run_large_test(_lowerCamelCase			)







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       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''': 1573, '''token_str''': ''' Chris'''},
           ]    ,      )
       _snake_case					     =       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''': 2201,
                   '''token_str''': ''' Paris''',
               },
               {
                   '''sequence''': '''The largest city in France is Lyon''',
                   '''score''': 0.2_1_4,
                   '''token''': 12790,
                   '''token_str''': ''' Lyon''',
               },
           ]    ,      )

       _snake_case					     =       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''': 3499, '''token_str''': ''' Patrick'''},
               {'''sequence''': '''My name is Clara''', '''score''': 0.0_0_0, '''token''': 13606, '''token_str''': ''' Clara'''},
               {'''sequence''': '''My name is Te''', '''score''': 0.0_0_0, '''token''': 2941, '''token_str''': ''' Te'''},
           ]    ,      )







     @require_torch
     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       pipeline(task='''fill-mask'''    ,      model='''sshleifer/tiny-distilroberta-base'''    ,      framework='''pt'''			)
       _snake_case					     =       None
       _snake_case					     =       None
       self.run_pipeline_test(_lowerCamelCase    ,      []			)







     @require_tf
     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       pipeline(task='''fill-mask'''    ,      model='''sshleifer/tiny-distilroberta-base'''    ,      framework='''tf'''			)
       _snake_case					     =       None
       _snake_case					     =       None
       self.run_pipeline_test(_lowerCamelCase    ,      []			)







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			Dict    ,      _lowerCamelCase			:			List[Any]			):
       if tokenizer is None or tokenizer.mask_token_id is None:
         self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)'''			)

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







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Optional[int]			):
       _snake_case					     =       fill_masker.tokenizer
       _snake_case					     =       fill_masker.model

       _snake_case					     =       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			)},
           ]    ,      )

       _snake_case					     =       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			)},
           ]    ,      )

       _snake_case					     =       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			:			int    ,      _lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       tokenizer.get_vocab()
       _snake_case					     =       sorted(vocab.keys()			)[:2]
       # Pipeline argument
       _snake_case					     =       FillMaskPipeline(model=_lowerCamelCase    ,      tokenizer=_lowerCamelCase    ,      targets=_lowerCamelCase			)
       _snake_case					     =       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			)},
           ]    ,      )
       _snake_case					     =       {vocab[el] for el in targets}
       self.assertEqual({el['''token'''] for el in outputs}    ,      _lowerCamelCase			)
       _snake_case					     =       [tokenizer.decode([x]			) for x in target_ids]
       self.assertEqual({el['''token_str'''] for el in outputs}    ,      set(_lowerCamelCase			)			)

       # Call argument
       _snake_case					     =       FillMaskPipeline(model=_lowerCamelCase    ,      tokenizer=_lowerCamelCase			)
       _snake_case					     =       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			)},
           ]    ,      )
       _snake_case					     =       {vocab[el] for el in targets}
       self.assertEqual({el['''token'''] for el in outputs}    ,      _lowerCamelCase			)
       _snake_case					     =       [tokenizer.decode([x]			) for x in target_ids]
       self.assertEqual({el['''token_str'''] for el in outputs}    ,      set(_lowerCamelCase			)			)

       # Score equivalence
       _snake_case					     =       fill_masker(f'''This is a {tokenizer.mask_token}'''    ,      targets=_lowerCamelCase			)
       _snake_case					     =       [top_mask['''token_str'''] for top_mask in outputs]
       _snake_case					     =       [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			):
         _snake_case					     =       fill_masker(f'''This is a {tokenizer.mask_token}'''    ,      targets=_lowerCamelCase			)
         _snake_case					     =       [top_mask['''score'''] for top_mask in unmasked_targets]
         self.assertEqual(nested_simplify(_lowerCamelCase			)    ,      nested_simplify(_lowerCamelCase			)			)

       # Raises with invalid
       with self.assertRaises(_lowerCamelCase			):
         _snake_case					     =       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			):
           _snake_case					     =       fill_masker(f'''This is a {tokenizer.mask_token}'''    ,      targets=['''''']			)
         with self.assertRaises(_lowerCamelCase			):
           _snake_case					     =       fill_masker(f'''This is a {tokenizer.mask_token}'''    ,      targets=''''''			)







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       FillMaskPipeline(model=_lowerCamelCase    ,      tokenizer=_lowerCamelCase    ,      top_k=2			)
       _snake_case					     =       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			)},
           ]    ,      )

       _snake_case					     =       FillMaskPipeline(model=_lowerCamelCase    ,      tokenizer=_lowerCamelCase			)
       _snake_case					     =       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			:			List[str]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       tokenizer.get_vocab()
       _snake_case					     =       FillMaskPipeline(model=_lowerCamelCase    ,      tokenizer=_lowerCamelCase			)

       # top_k=2, ntargets=3
       _snake_case					     =       sorted(vocab.keys()			)[:3]
       _snake_case					     =       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
       _snake_case					     =       [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			):
         _snake_case					     =       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			:			str    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			List[Any]			):
       _snake_case					     =       FillMaskPipeline(model=_lowerCamelCase    ,      tokenizer=_lowerCamelCase			)
       _snake_case					     =       tokenizer.get_vocab()
       # String duplicates + id duplicates
       _snake_case					     =       sorted(vocab.keys()			)[:3]
       _snake_case					     =       [targets[0], targets[1], targets[0], targets[2], targets[1]]
       _snake_case					     =       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			:			List[str]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       FillMaskPipeline(model=_lowerCamelCase    ,      tokenizer=_lowerCamelCase			)

       _snake_case					     =       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 argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCAmelCase__          = ['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase__          = {'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


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

UpperCAmelCase__          = ' Hello world! cécé herlolip'

UpperCAmelCase__          = [
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  _snake_case					     =       [
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCamelCase ,					__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)
  _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					'''bart.large.cnn'''		).eval()
  hub_interface.model.load_state_dict(sd['''model''']		)
  return hub_interface
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Union[str, Any]:
  _snake_case     ,					_snake_case					     =       emb.weight.shape
  _snake_case					     =       nn.Linear(__lowerCamelCase ,					__lowerCamelCase ,					bias=__lowerCamelCase		)
  _snake_case					     =       emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any]=None		)     ->					List[Any]:
  if not os.path.exists(__lowerCamelCase		):
    _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					__lowerCamelCase		).eval()
  else:
    _snake_case					     =       load_xsum_checkpoint(__lowerCamelCase		)

  bart.model.upgrade_state_dict(bart.model.state_dict()		)
  if hf_checkpoint_name is None:
    _snake_case					     =       checkpoint_path.replace('''.''' ,					'''-'''		)
  _snake_case					     =       BartConfig.from_pretrained(__lowerCamelCase		)
  _snake_case					     =       bart.encode(__lowerCamelCase		).unsqueeze(0		)
  _snake_case					     =       BartTokenizer.from_pretrained(__lowerCamelCase		).encode(__lowerCamelCase ,					return_tensors='''pt'''		).unsqueeze(0		)
  if not torch.eq(__lowerCamelCase ,					__lowerCamelCase		).all():
    raise ValueError(
        f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''		)

  if checkpoint_path == "bart.large.mnli":
    _snake_case					     =       bart.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''model.decoder.embed_tokens.weight''']
    for src, dest in mnli_rename_keys:
      rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       BartForSequenceClassification(__lowerCamelCase		).eval()
    model.load_state_dict(__lowerCamelCase		)
    _snake_case					     =       bart.predict('''mnli''' ,					__lowerCamelCase ,					return_logits=__lowerCamelCase		)
    _snake_case					     =       model(__lowerCamelCase		)[0]  # logits
  else:  # no classification heads to worry about
    _snake_case					     =       bart.model.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''decoder.embed_tokens.weight''']
    _snake_case					     =       bart.extract_features(__lowerCamelCase		)
    if hf_checkpoint_name == "facebook/bart-large":
      _snake_case					     =       BartModel(__lowerCamelCase		).eval()
      model.load_state_dict(__lowerCamelCase		)
      _snake_case					     =       model(__lowerCamelCase		).model[0]
    else:
      _snake_case					     =       BartForConditionalGeneration(__lowerCamelCase		).eval()  # an existing summarization ckpt
      model.model.load_state_dict(__lowerCamelCase		)
      if hasattr(__lowerCamelCase ,					'''lm_head'''		):
        _snake_case					     =       make_linear_from_emb(model.model.shared		)
      _snake_case					     =       model.model(__lowerCamelCase		)[0]

    # Check results
  if fairseq_output.shape != new_model_outputs.shape:
    raise ValueError(
        f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''		)
  if (fairseq_output != new_model_outputs).any().item():
    raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  model.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
 )
 UpperCAmelCase__          = parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



