
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			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	MODEL_FOR_MASKED_LM_MAPPING
     SCREAMING_SNAKE_CASE_		 =	TF_MODEL_FOR_MASKED_LM_MAPPING






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """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  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					pipeline(task='''fill-mask'''    ,    model='''sshleifer/tiny-distilroberta-base'''    ,    top_k=2    ,    framework='''tf'''	)
           UpperCamelCase__     : int											=					unmasker('''My name is <mask>'''	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    decimals=6	)    ,    [
                   {'''sequence''': '''My name is grouped''', '''score''': 2.1e-05, '''token''': 3_8_0_1_5, '''token_str''': ''' grouped'''},
                   {'''sequence''': '''My name is accuser''', '''score''': 2.1e-05, '''token''': 2_5_5_0_6, '''token_str''': ''' accuser'''},
               ]    ,    )

           UpperCamelCase__     : List[Any]											=					unmasker('''The largest city in France is <mask>'''	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    decimals=6	)    ,    [
                   {
                       '''sequence''': '''The largest city in France is grouped''',
                       '''score''': 2.1e-05,
                       '''token''': 3_8_0_1_5,
                       '''token_str''': ''' grouped''',
                   },
                   {
                       '''sequence''': '''The largest city in France is accuser''',
                       '''score''': 2.1e-05,
                       '''token''': 2_5_5_0_6,
                       '''token_str''': ''' accuser''',
                   },
               ]    ,    )

           UpperCamelCase__     : str											=					unmasker('''My name is <mask>'''    ,    targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,    top_k=3	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    decimals=6	)    ,    [
                   {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
                   {'''sequence''': '''My name is Patrick''', '''score''': 2e-05, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
                   {'''sequence''': '''My name is Te''', '''score''': 1.9e-05, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
               ]    ,    )






     @require_torch
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					pipeline(task='''fill-mask'''    ,    model='''sshleifer/tiny-distilroberta-base'''    ,    top_k=2    ,    framework='''pt'''	)

           UpperCamelCase__     : Optional[Any]											=					unmasker('''My name is <mask>'''	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    decimals=6	)    ,    [
                   {'''sequence''': '''My name is Maul''', '''score''': 2.2e-05, '''token''': 3_5_6_7_6, '''token_str''': ''' Maul'''},
                   {'''sequence''': '''My name isELS''', '''score''': 2.2e-05, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
               ]    ,    )

           UpperCamelCase__     : Optional[int]											=					unmasker('''The largest city in France is <mask>'''	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    decimals=6	)    ,    [
                   {
                       '''sequence''': '''The largest city in France is Maul''',
                       '''score''': 2.2e-05,
                       '''token''': 3_5_6_7_6,
                       '''token_str''': ''' Maul''',
                   },
                   {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-05, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
               ]    ,    )

           UpperCamelCase__     : int											=					unmasker('''My name is <mask>'''    ,    targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,    top_k=3	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    decimals=6	)    ,    [
                   {'''sequence''': '''My name is Patrick''', '''score''': 2.1e-05, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
                   {'''sequence''': '''My name is Te''', '''score''': 2e-05, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
                   {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
               ]    ,    )

           UpperCamelCase__     : Any											=					unmasker('''My name is <mask> <mask>'''    ,    top_k=2	)

           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    decimals=6	)    ,    [
                   [
                       {
                           '''score''': 2.2e-05,
                           '''token''': 3_5_6_7_6,
                           '''token_str''': ''' Maul''',
                           '''sequence''': '''<s>My name is Maul<mask></s>''',
                       },
                       {'''score''': 2.2e-05, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
                   ],
                   [
                       {
                           '''score''': 2.2e-05,
                           '''token''': 3_5_6_7_6,
                           '''token_str''': ''' Maul''',
                           '''sequence''': '''<s>My name is<mask> Maul</s>''',
                       },
                       {'''score''': 2.2e-05, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
                   ],
               ]    ,    )






     @require_torch_gpu
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







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

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

           UpperCamelCase__     : List[Any]											=					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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     @slow
     @require_torch
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					pipeline(task='''fill-mask'''    ,    model='''distilroberta-base'''    ,    top_k=2    ,    framework='''pt'''	)
           self.run_large_test(__SCREAMING_SNAKE_CASE	)






     @slow
     @require_tf
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : str											=					pipeline(task='''fill-mask'''    ,    model='''distilroberta-base'''    ,    top_k=2    ,    framework='''tf'''	)
           self.run_large_test(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : int											=					unmasker('''My name is <mask>'''	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE	)    ,    [
                   {'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 6_1_0, '''token_str''': ''' John'''},
                   {'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 1_5_7_3, '''token_str''': ''' Chris'''},
               ]    ,    )
           UpperCamelCase__     : Any											=					unmasker('''The largest city in France is <mask>'''	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE	)    ,    [
                   {
                       '''sequence''': '''The largest city in France is Paris''',
                       '''score''': 0.251,
                       '''token''': 2_2_0_1,
                       '''token_str''': ''' Paris''',
                   },
                   {
                       '''sequence''': '''The largest city in France is Lyon''',
                       '''score''': 0.214,
                       '''token''': 1_2_7_9_0,
                       '''token_str''': ''' Lyon''',
                   },
               ]    ,    )

           UpperCamelCase__     : int											=					unmasker('''My name is <mask>'''    ,    targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,    top_k=3	)
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE	)    ,    [
                   {'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
                   {'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
                   {'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
               ]    ,    )






     @require_torch
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					pipeline(task='''fill-mask'''    ,    model='''sshleifer/tiny-distilroberta-base'''    ,    framework='''pt'''	)
           UpperCamelCase__     : Union[str, Any]											=					None
           UpperCamelCase__     : Dict											=					None
           self.run_pipeline_test(__SCREAMING_SNAKE_CASE    ,    []	)






     @require_tf
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					pipeline(task='''fill-mask'''    ,    model='''sshleifer/tiny-distilroberta-base'''    ,    framework='''tf'''	)
           UpperCamelCase__     : Union[str, Any]											=					None
           UpperCamelCase__     : Dict											=					None
           self.run_pipeline_test(__SCREAMING_SNAKE_CASE    ,    []	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """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)'''	)

           UpperCamelCase__     : List[Any]											=					FillMaskPipeline(model=__SCREAMING_SNAKE_CASE    ,    tokenizer=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					[
               F'''This is another {tokenizer.mask_token} test''',
           ]
           return fill_masker, examples






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					fill_masker.tokenizer
           UpperCamelCase__     : int											=					fill_masker.model

           UpperCamelCase__     : Optional[Any]											=					fill_masker(
               F'''This is a {tokenizer.mask_token}'''    ,    )
           self.assertEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
               ]    ,    )

           UpperCamelCase__     : Tuple											=					fill_masker([F'''This is a {tokenizer.mask_token}''']	)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
               ]    ,    )

           UpperCamelCase__     : Optional[int]											=					fill_masker([F'''This is a {tokenizer.mask_token}''', F'''Another {tokenizer.mask_token} great test.''']	)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   [
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   ],
                   [
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   ],
               ]    ,    )

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

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Optional[Any]:




           """simple docstring"""







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

           # Call argument
           UpperCamelCase__     : Optional[Any]											=					FillMaskPipeline(model=__SCREAMING_SNAKE_CASE    ,    tokenizer=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					fill_masker(F'''This is a {tokenizer.mask_token}'''    ,    targets=__SCREAMING_SNAKE_CASE	)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
               ]    ,    )
           UpperCamelCase__     : int											=					{vocab[el] for el in targets}
           self.assertEqual({el['''token'''] for el in outputs}    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[Any]											=					[tokenizer.decode([x]	) for x in target_ids]
           self.assertEqual({el['''token_str'''] for el in outputs}    ,    set(__SCREAMING_SNAKE_CASE	)	)

           # Score equivalence
           UpperCamelCase__     : str											=					fill_masker(F'''This is a {tokenizer.mask_token}'''    ,    targets=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					[top_mask['''token_str'''] for top_mask in outputs]
           UpperCamelCase__     : Optional[Any]											=					[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(__SCREAMING_SNAKE_CASE	) == set(__SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : Optional[int]											=					fill_masker(F'''This is a {tokenizer.mask_token}'''    ,    targets=__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : List[str]											=					[top_mask['''score'''] for top_mask in unmasked_targets]
                 self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE	)    ,    nested_simplify(__SCREAMING_SNAKE_CASE	)	)

           # Raises with invalid
           with self.assertRaises(__SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : Tuple											=					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(__SCREAMING_SNAKE_CASE	):
                       UpperCamelCase__     : List[Any]											=					fill_masker(F'''This is a {tokenizer.mask_token}'''    ,    targets=['''''']	)
                 with self.assertRaises(__SCREAMING_SNAKE_CASE	):
                       UpperCamelCase__     : Optional[int]											=					fill_masker(F'''This is a {tokenizer.mask_token}'''    ,    targets=''''''	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					FillMaskPipeline(model=__SCREAMING_SNAKE_CASE    ,    tokenizer=__SCREAMING_SNAKE_CASE    ,    top_k=2	)
           UpperCamelCase__     : List[Any]											=					fill_masker(F'''This is a {tokenizer.mask_token}'''	)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
               ]    ,    )

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					tokenizer.get_vocab()
           UpperCamelCase__     : Optional[int]											=					FillMaskPipeline(model=__SCREAMING_SNAKE_CASE    ,    tokenizer=__SCREAMING_SNAKE_CASE	)

           # top_k=2, ntargets=3
           UpperCamelCase__     : str											=					sorted(vocab.keys()	)[:3]
           UpperCamelCase__     : Optional[Any]											=					fill_masker(F'''This is a {tokenizer.mask_token}'''    ,    top_k=2    ,    targets=__SCREAMING_SNAKE_CASE	)

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					FillMaskPipeline(model=__SCREAMING_SNAKE_CASE    ,    tokenizer=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					tokenizer.get_vocab()
           # String duplicates + id duplicates
           UpperCamelCase__     : List[str]											=					sorted(vocab.keys()	)[:3]
           UpperCamelCase__     : List[Any]											=					[targets[0], targets[1], targets[0], targets[2], targets[1]]
           UpperCamelCase__     : List[str]											=					fill_masker(F'''My name is {tokenizer.mask_token}'''    ,    targets=__SCREAMING_SNAKE_CASE    ,    top_k=1_0	)

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					FillMaskPipeline(model=__SCREAMING_SNAKE_CASE    ,    tokenizer=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Optional[int]											=					fill_masker(
               F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''    ,    top_k=2	)
           self.assertEqual(
               __SCREAMING_SNAKE_CASE    ,    [
                   [
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   ],
                   [
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   ],
                   [
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                       {'''sequence''': ANY(__SCREAMING_SNAKE_CASE	), '''score''': ANY(__SCREAMING_SNAKE_CASE	), '''token''': ANY(__SCREAMING_SNAKE_CASE	), '''token_str''': ANY(__SCREAMING_SNAKE_CASE	)},
                   ],
               ]    ,    )




import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : List[Any]											=					OrderedDict()
      for key, value in state_dict.items():
            if key.startswith('''module.encoder'''		):
                  UpperCamelCase__     : Any											=					key.replace('''module.encoder'''			,		'''glpn.encoder'''		)
            if key.startswith('''module.decoder'''		):
                  UpperCamelCase__     : Optional[Any]											=					key.replace('''module.decoder'''			,		'''decoder.stages'''		)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  UpperCamelCase__     : Union[str, Any]											=					key[key.find('''patch_embed'''		) + len('''patch_embed'''		)]
                  UpperCamelCase__     : Tuple											=					key.replace(f'''patch_embed{idx}'''			,		f'''patch_embeddings.{int(UpperCamelCase__		)-1}'''		)
            if "norm" in key:
                  UpperCamelCase__     : Dict											=					key.replace('''norm'''			,		'''layer_norm'''		)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  UpperCamelCase__     : int											=					key[key.find('''glpn.encoder.layer_norm'''		) + len('''glpn.encoder.layer_norm'''		)]
                  UpperCamelCase__     : Tuple											=					key.replace(f'''layer_norm{idx}'''			,		f'''layer_norm.{int(UpperCamelCase__		)-1}'''		)
            if "layer_norm1" in key:
                  UpperCamelCase__     : Tuple											=					key.replace('''layer_norm1'''			,		'''layer_norm_1'''		)
            if "layer_norm2" in key:
                  UpperCamelCase__     : Union[str, Any]											=					key.replace('''layer_norm2'''			,		'''layer_norm_2'''		)
            if "block" in key:
                  # replace for example block1 by block.0
                  UpperCamelCase__     : Optional[Any]											=					key[key.find('''block'''		) + len('''block'''		)]
                  UpperCamelCase__     : str											=					key.replace(f'''block{idx}'''			,		f'''block.{int(UpperCamelCase__		)-1}'''		)
            if "attn.q" in key:
                  UpperCamelCase__     : Dict											=					key.replace('''attn.q'''			,		'''attention.self.query'''		)
            if "attn.proj" in key:
                  UpperCamelCase__     : Any											=					key.replace('''attn.proj'''			,		'''attention.output.dense'''		)
            if "attn" in key:
                  UpperCamelCase__     : List[Any]											=					key.replace('''attn'''			,		'''attention.self'''		)
            if "fc1" in key:
                  UpperCamelCase__     : int											=					key.replace('''fc1'''			,		'''dense1'''		)
            if "fc2" in key:
                  UpperCamelCase__     : str											=					key.replace('''fc2'''			,		'''dense2'''		)
            if "linear_pred" in key:
                  UpperCamelCase__     : List[str]											=					key.replace('''linear_pred'''			,		'''classifier'''		)
            if "linear_fuse" in key:
                  UpperCamelCase__     : Union[str, Any]											=					key.replace('''linear_fuse.conv'''			,		'''linear_fuse'''		)
                  UpperCamelCase__     : Optional[int]											=					key.replace('''linear_fuse.bn'''			,		'''batch_norm'''		)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  UpperCamelCase__     : str											=					key[key.find('''linear_c'''		) + len('''linear_c'''		)]
                  UpperCamelCase__     : Tuple											=					key.replace(f'''linear_c{idx}'''			,		f'''linear_c.{int(UpperCamelCase__		)-1}'''		)
            if "bot_conv" in key:
                  UpperCamelCase__     : List[Any]											=					key.replace('''bot_conv'''			,		'''0.convolution'''		)
            if "skip_conv1" in key:
                  UpperCamelCase__     : str											=					key.replace('''skip_conv1'''			,		'''1.convolution'''		)
            if "skip_conv2" in key:
                  UpperCamelCase__     : Tuple											=					key.replace('''skip_conv2'''			,		'''2.convolution'''		)
            if "fusion1" in key:
                  UpperCamelCase__     : List[Any]											=					key.replace('''fusion1'''			,		'''1.fusion'''		)
            if "fusion2" in key:
                  UpperCamelCase__     : Tuple											=					key.replace('''fusion2'''			,		'''2.fusion'''		)
            if "fusion3" in key:
                  UpperCamelCase__     : int											=					key.replace('''fusion3'''			,		'''3.fusion'''		)
            if "fusion" in key and "conv" in key:
                  UpperCamelCase__     : List[str]											=					key.replace('''conv'''			,		'''convolutional_layer'''		)
            if key.startswith('''module.last_layer_depth'''		):
                  UpperCamelCase__     : Tuple											=					key.replace('''module.last_layer_depth'''			,		'''head.head'''		)
            UpperCamelCase__     : List[str]											=					value

      return new_state_dict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      # for each of the encoder blocks:
      for i in range(config.num_encoder_blocks		):
            for j in range(config.depths[i]		):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  UpperCamelCase__     : Optional[Any]											=					state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
                  UpperCamelCase__     : int											=					state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''		)
                  # next, add keys and values (in that order) to the state dict
                  UpperCamelCase__     : Union[str, Any]											=					kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCamelCase__     : Tuple											=					kv_bias[: config.hidden_sizes[i]]
                  UpperCamelCase__     : Tuple											=					kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCamelCase__     : Optional[Any]											=					kv_bias[config.hidden_sizes[i] :]


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__     : Any											=					Image.open(requests.get(UpperCamelCase__			,		stream=UpperCamelCase__		).raw		)

      return image


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False			,		UpperCamelCase__=None		):
      UpperCamelCase__     : Any											=					GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,		decoder_hidden_size=6_4			,		depths=[3, 8, 2_7, 3]		)

      # load image processor (only resize + rescale)
      UpperCamelCase__     : str											=					GLPNImageProcessor()

      # prepare image
      UpperCamelCase__     : List[str]											=					prepare_img()
      UpperCamelCase__     : List[str]											=					image_processor(images=UpperCamelCase__			,		return_tensors='''pt'''		).pixel_values

      logger.info('''Converting model...'''		)

      # load original state dict
      UpperCamelCase__     : Tuple											=					torch.load(UpperCamelCase__			,		map_location=torch.device('''cpu'''		)		)

      # rename keys
      UpperCamelCase__     : Optional[Any]											=					rename_keys(UpperCamelCase__		)

      # key and value matrices need special treatment
      read_in_k_v(UpperCamelCase__			,		UpperCamelCase__		)

      # create HuggingFace model and load state dict
      UpperCamelCase__     : str											=					GLPNForDepthEstimation(UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__		)
      model.eval()

      # forward pass
      UpperCamelCase__     : Union[str, Any]											=					model(UpperCamelCase__		)
      UpperCamelCase__     : Optional[int]											=					outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  UpperCamelCase__     : Tuple											=					torch.tensor(
                      [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]		)
            elif "kitti" in model_name:
                  UpperCamelCase__     : Union[str, Any]											=					torch.tensor(
                      [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		)
            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''		)

            UpperCamelCase__     : Optional[Any]											=					torch.Size([1, 4_8_0, 6_4_0]		)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]			,		UpperCamelCase__			,		atol=1e-4		)
            print('''Looks ok!'''		)

      # finally, push to hub if required
      if push_to_hub:
            logger.info('''Pushing model and image processor to the hub...'''		)
            model.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase__			,		UpperCamelCase__		)			,		organization='''nielsr'''			,		commit_message='''Add model'''			,		use_temp_dir=UpperCamelCase__			,		)
            image_processor.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase__			,		UpperCamelCase__		)			,		organization='''nielsr'''			,		commit_message='''Add image processor'''			,		use_temp_dir=UpperCamelCase__			,		)


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

    parser.add_argument(
        "--checkpoint_path",
        default=None,
        type=str,
        help="Path 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."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
    )
    parser.add_argument(
        "--model_name",
        default="glpn-kitti",
        type=str,
        help="Name of the model in case you're pushing to the hub.",
    )
    lowerCamelCase  =parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


