





"""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  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  MODEL_FOR_MASKED_LM_MAPPING
       SCREAMING_SNAKE_CASE_       :				str     						=  TF_MODEL_FOR_MASKED_LM_MAPPING




       def    _UpperCamelCase					(						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    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  pipeline(task='''fill-mask'''		,model='''sshleifer/tiny-distilroberta-base'''		,top_k=2		,framework='''tf'''							)
             __SCREAMING_SNAKE_CASE       :Any		    =  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_80_15, '''token_str''': ''' grouped'''},
                     {'''sequence''': '''My name is accuser''', '''score''': 2.1E-05, '''token''': 2_55_06, '''token_str''': ''' accuser'''},
                 ]		,)

             __SCREAMING_SNAKE_CASE       :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_80_15,
                         '''token_str''': ''' grouped''',
                     },
                     {
                         '''sequence''': '''The largest city in France is accuser''',
                         '''score''': 2.1E-05,
                         '''token''': 2_55_06,
                         '''token_str''': ''' accuser''',
                     },
                 ]		,)

             __SCREAMING_SNAKE_CASE       :Dict		    =  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_36_06, '''token_str''': ''' Clara'''},
                     {'''sequence''': '''My name is Patrick''', '''score''': 2E-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
                     {'''sequence''': '''My name is Te''', '''score''': 1.9E-05, '''token''': 29_41, '''token_str''': ''' Te'''},
                 ]		,)




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE       :str		    =  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_56_76, '''token_str''': ''' Maul'''},
                     {'''sequence''': '''My name isELS''', '''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
                 ]		,)

             __SCREAMING_SNAKE_CASE       :Optional[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 Maul''',
                         '''score''': 2.2E-05,
                         '''token''': 3_56_76,
                         '''token_str''': ''' Maul''',
                     },
                     {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
                 ]		,)

             __SCREAMING_SNAKE_CASE       :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 Patrick''', '''score''': 2.1E-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
                     {'''sequence''': '''My name is Te''', '''score''': 2E-05, '''token''': 29_41, '''token_str''': ''' Te'''},
                     {'''sequence''': '''My name is Clara''', '''score''': 2E-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
                 ]		,)

             __SCREAMING_SNAKE_CASE       :str		    =  unmasker('''My name is <mask> <mask>'''		,top_k=2							)

             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__		,decimals=6							)		,[
                     [
                         {
                             '''score''': 2.2E-05,
                             '''token''': 3_56_76,
                             '''token_str''': ''' Maul''',
                             '''sequence''': '''<s>My name is Maul<mask></s>''',
                         },
                         {'''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
                     ],
                     [
                         {
                             '''score''': 2.2E-05,
                             '''token''': 3_56_76,
                             '''token_str''': ''' Maul''',
                             '''sequence''': '''<s>My name is<mask> Maul</s>''',
                         },
                         {'''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
                     ],
                 ]		,)




       @require_torch_gpu
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






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

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

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






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




       @slow
       @require_tf
       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  unmasker('''My name is <mask>'''							)
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__							)		,[
                     {'''sequence''': '''My name is John''', '''score''': 0.0_0_8, '''token''': 6_10, '''token_str''': ''' John'''},
                     {'''sequence''': '''My name is Chris''', '''score''': 0.0_0_7, '''token''': 15_73, '''token_str''': ''' Chris'''},
                 ]		,)
             __SCREAMING_SNAKE_CASE       :List[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''': 0.2_5_1,
                         '''token''': 22_01,
                         '''token_str''': ''' Paris''',
                     },
                     {
                         '''sequence''': '''The largest city in France is Lyon''',
                         '''score''': 0.2_1_4,
                         '''token''': 1_27_90,
                         '''token_str''': ''' Lyon''',
                     },
                 ]		,)

             __SCREAMING_SNAKE_CASE       :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''': 0.0_0_5, '''token''': 34_99, '''token_str''': ''' Patrick'''},
                     {'''sequence''': '''My name is Clara''', '''score''': 0.0_0_0, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
                     {'''sequence''': '''My name is Te''', '''score''': 0.0_0_0, '''token''': 29_41, '''token_str''': ''' Te'''},
                 ]		,)




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






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




       @require_tf
       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






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




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





             """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)'''							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  [
                 f'''This is another {tokenizer.mask_token} test''',
             ]
             return fill_masker, examples




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  fill_masker.tokenizer
             __SCREAMING_SNAKE_CASE       :Tuple		    =  fill_masker.model

             __SCREAMING_SNAKE_CASE       :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__							)},
                 ]		,)

             __SCREAMING_SNAKE_CASE       :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__							)},
                 ]		,)

             __SCREAMING_SNAKE_CASE       :Any		    =  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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer.get_vocab()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  sorted(vocab.keys()							)[:2]
             # Pipeline argument
             __SCREAMING_SNAKE_CASE       :Dict		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__		,targets=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :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__							)},
                 ]		,)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  {vocab[el] for el in targets}
             self.assertEqual({el['''token'''] for el in outputs}		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [tokenizer.decode([x]							) for x in target_ids]
             self.assertEqual({el['''token_str'''] for el in outputs}		,set(SCREAMING_SNAKE_CASE__							)							)

             # Call argument
             __SCREAMING_SNAKE_CASE       :List[str]		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  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__							)},
                 ]		,)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  {vocab[el] for el in targets}
             self.assertEqual({el['''token'''] for el in outputs}		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  [tokenizer.decode([x]							) for x in target_ids]
             self.assertEqual({el['''token_str'''] for el in outputs}		,set(SCREAMING_SNAKE_CASE__							)							)

             # Score equivalence
             __SCREAMING_SNAKE_CASE       :Any		    =  fill_masker(f'''This is a {tokenizer.mask_token}'''		,targets=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [top_mask['''token_str'''] for top_mask in outputs]
             __SCREAMING_SNAKE_CASE       :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__							):
                   __SCREAMING_SNAKE_CASE       :str		    =  fill_masker(f'''This is a {tokenizer.mask_token}'''		,targets=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  [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__							):
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  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__							):
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  fill_masker(f'''This is a {tokenizer.mask_token}'''		,targets=['''''']							)
                   with self.assertRaises(SCREAMING_SNAKE_CASE__							):
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  fill_masker(f'''This is a {tokenizer.mask_token}'''		,targets=''''''							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__		,top_k=2							)
             __SCREAMING_SNAKE_CASE       :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__							)},
                 ]		,)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  tokenizer.get_vocab()
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__							)

             # top_k=2, ntargets=3
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  sorted(vocab.keys()							)[:3]
             __SCREAMING_SNAKE_CASE       :List[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
             __SCREAMING_SNAKE_CASE       :List[str]		    =  [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__							):
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  tokenizer.get_vocab()
             # String duplicates + id duplicates
             __SCREAMING_SNAKE_CASE       :List[str]		    =  sorted(vocab.keys()							)[:3]
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  [targets[0], targets[1], targets[0], targets[2], targets[1]]
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  fill_masker(f'''My name is {tokenizer.mask_token}'''		,targets=SCREAMING_SNAKE_CASE__		,top_k=10							)

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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  FillMaskPipeline(model=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Any		    =  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__							)},
                     ],
                 ]		,)






"""simple docstring"""
import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class  _SCREAMING_SNAKE_CASE(       A  ):




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





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Any		    =  value_function
             __SCREAMING_SNAKE_CASE       :List[str]		    =  unet
             __SCREAMING_SNAKE_CASE       :int		    =  scheduler
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  env
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  env.get_dataset()
             __SCREAMING_SNAKE_CASE       :str		    =  {}
             for key in self.data.keys():
                   try:
                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.data[key].mean()
                   except:  # noqa: E722
                         pass
             __SCREAMING_SNAKE_CASE       :Dict		    =  {}
             for key in self.data.keys():
                   try:
                         __SCREAMING_SNAKE_CASE       :str		    =  self.data[key].std()
                   except:  # noqa: E722
                         pass
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  env.observation_space.shape[0]
             __SCREAMING_SNAKE_CASE       :int		    =  env.action_space.shape[0]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             return (x_in - self.means[key]) / self.stds[key]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             return x_in * self.stds[key] + self.means[key]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             if type(SCREAMING_SNAKE_CASE__							) is dict:
                   return {k: self.to_torch(SCREAMING_SNAKE_CASE__							) for k, v in x_in.items()}
             elif torch.is_tensor(SCREAMING_SNAKE_CASE__							):
                   return x_in.to(self.unet.device							)
             return torch.tensor(SCREAMING_SNAKE_CASE__		,device=self.unet.device							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             for key, val in cond.items():
                   __SCREAMING_SNAKE_CASE       :Dict		    =  val.clone()
             return x_in




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  x.shape[0]
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  None
             for i in tqdm.tqdm(self.scheduler.timesteps							):
                   # create batch of timesteps to pass into model
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  torch.full((batch_size,)		,SCREAMING_SNAKE_CASE__		,device=self.unet.device		,dtype=torch.long							)
                   for _ in range(SCREAMING_SNAKE_CASE__							):
                         with torch.enable_grad():
                               x.requires_grad_()

                               # permute to match dimension for pre-trained models
                               __SCREAMING_SNAKE_CASE       :str		    =  self.value_function(x.permute(0		,2		,1							)		,SCREAMING_SNAKE_CASE__							).sample
                               __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.autograd.grad([y.sum()]		,[x]							)[0]

                               __SCREAMING_SNAKE_CASE       :int		    =  self.scheduler._get_variance(SCREAMING_SNAKE_CASE__							)
                               __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.exp(0.5 * posterior_variance							)
                               __SCREAMING_SNAKE_CASE       :List[str]		    =  model_std * grad

                         __SCREAMING_SNAKE_CASE       :Dict		    =  0
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  x.detach()
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  x + scale * grad
                         __SCREAMING_SNAKE_CASE       :Any		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.unet(x.permute(0		,2		,1							)		,SCREAMING_SNAKE_CASE__							).sample.permute(0		,2		,1							)

                   # TODO: verify deprecation of this kwarg
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.scheduler.step(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,predict_epsilon=SCREAMING_SNAKE_CASE__							)['''prev_sample''']

                   # apply conditions to the trajectory (set the initial state)
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)
                   __SCREAMING_SNAKE_CASE       :Dict		    =  self.to_torch(SCREAMING_SNAKE_CASE__							)
             return x, y




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=64		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.1							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.normalize(SCREAMING_SNAKE_CASE__		,'''observations'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  obs[None].repeat(SCREAMING_SNAKE_CASE__		,axis=0							)

             __SCREAMING_SNAKE_CASE       :str		    =  {0: self.to_torch(SCREAMING_SNAKE_CASE__							)}
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  (batch_size, planning_horizon, self.state_dim + self.action_dim)

             # generate initial noise and apply our conditions (to make the trajectories start at current state)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  randn_tensor(SCREAMING_SNAKE_CASE__		,device=self.unet.device							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)
             __SCREAMING_SNAKE_CASE       :Any		    =  self.to_torch(SCREAMING_SNAKE_CASE__							)

             # run the diffusion process
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Tuple		    =  self.run_diffusion(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             # sort output trajectories by value
             __SCREAMING_SNAKE_CASE       :Any		    =  y.argsort(0		,descending=SCREAMING_SNAKE_CASE__							).squeeze()
             __SCREAMING_SNAKE_CASE       :Any		    =  x[sorted_idx]
             __SCREAMING_SNAKE_CASE       :str		    =  sorted_values[:, :, : self.action_dim]
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  actions.detach().cpu().numpy()
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.de_normalize(SCREAMING_SNAKE_CASE__		,key='''actions'''							)

             # select the action with the highest value
             if y is not None:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  0
             else:
                   # if we didn't run value guiding, select a random action
                   __SCREAMING_SNAKE_CASE       :Any		    =  np.random.randint(0		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  denorm_actions[selected_index, 0]
             return denorm_actions