



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  a				(     unittest.TestCase							):
		__lowerCAmelCase      :       List[str]											=   MODEL_FOR_MASKED_LM_MAPPING
		__lowerCAmelCase      :       int											=   TF_MODEL_FOR_MASKED_LM_MAPPING



		def       __lowerCamelCase     (	self	:List[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       __lowerCamelCase     (	self	:Optional[Any]    ):
						snake_case__      : Union[str, Any]							=      pipeline(task='''fill-mask'''  ,model='''sshleifer/tiny-distilroberta-base'''  ,top_k=2  ,framework='''tf'''    )
						snake_case__      : Union[str, Any]							=      unmasker('''My name is <mask>'''    )
						self.assertEqual(
						    nested_simplify(SCREAMING_SNAKE_CASE_  ,decimals=6    )  ,[
						        {'''sequence''': '''My name is grouped''', '''score''': 2.1e-0_5, '''token''': 3_8_0_1_5, '''token_str''': ''' grouped'''},
						        {'''sequence''': '''My name is accuser''', '''score''': 2.1e-0_5, '''token''': 2_5_5_0_6, '''token_str''': ''' accuser'''},
						    ]  ,)

						snake_case__      : Tuple							=      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-0_5,
						            '''token''': 3_8_0_1_5,
						            '''token_str''': ''' grouped''',
						        },
						        {
						            '''sequence''': '''The largest city in France is accuser''',
						            '''score''': 2.1e-0_5,
						            '''token''': 2_5_5_0_6,
						            '''token_str''': ''' accuser''',
						        },
						    ]  ,)

						snake_case__      : 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 Clara''', '''score''': 2e-0_5, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
						        {'''sequence''': '''My name is Patrick''', '''score''': 2e-0_5, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
						        {'''sequence''': '''My name is Te''', '''score''': 1.9e-0_5, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
						    ]  ,)



		@require_torch
		def       __lowerCamelCase     (	self	:List[str]    ):
						snake_case__      : int							=      pipeline(task='''fill-mask'''  ,model='''sshleifer/tiny-distilroberta-base'''  ,top_k=2  ,framework='''pt'''    )

						snake_case__      : int							=      unmasker('''My name is <mask>'''    )
						self.assertEqual(
						    nested_simplify(SCREAMING_SNAKE_CASE_  ,decimals=6    )  ,[
						        {'''sequence''': '''My name is Maul''', '''score''': 2.2e-0_5, '''token''': 3_5_6_7_6, '''token_str''': ''' Maul'''},
						        {'''sequence''': '''My name isELS''', '''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
						    ]  ,)

						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 Maul''',
						            '''score''': 2.2e-0_5,
						            '''token''': 3_5_6_7_6,
						            '''token_str''': ''' Maul''',
						        },
						        {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
						    ]  ,)

						snake_case__      : List[Any]							=      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-0_5, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
						        {'''sequence''': '''My name is Te''', '''score''': 2e-0_5, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
						        {'''sequence''': '''My name is Clara''', '''score''': 2e-0_5, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
						    ]  ,)

						snake_case__      : Dict							=      unmasker('''My name is <mask> <mask>'''  ,top_k=2    )

						self.assertEqual(
						    nested_simplify(SCREAMING_SNAKE_CASE_  ,decimals=6    )  ,[
						        [
						            {
						                '''score''': 2.2e-0_5,
						                '''token''': 3_5_6_7_6,
						                '''token_str''': ''' Maul''',
						                '''sequence''': '''<s>My name is Maul<mask></s>''',
						            },
						            {'''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
						        ],
						        [
						            {
						                '''score''': 2.2e-0_5,
						                '''token''': 3_5_6_7_6,
						                '''token_str''': ''' Maul''',
						                '''sequence''': '''<s>My name is<mask> Maul</s>''',
						            },
						            {'''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
						        ],
						    ]  ,)



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

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

						snake_case__      : Tuple							=      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       __lowerCamelCase     (	self	:Union[str, Any]    ):
						snake_case__      : Dict							=      pipeline(task='''fill-mask'''  ,model='''distilroberta-base'''  ,top_k=2  ,framework='''pt'''    )
						self.run_large_test(SCREAMING_SNAKE_CASE_    )



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



		def       __lowerCamelCase     (	self	:Any  ,__lowercase	:Optional[int]    ):
						snake_case__      : Dict							=      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'''},
						    ]  ,)
						snake_case__      : List[str]							=      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''',
						        },
						    ]  ,)

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



		@require_tf
		def       __lowerCamelCase     (	self	:str    ):
						snake_case__      : List[str]							=      pipeline(task='''fill-mask'''  ,model='''sshleifer/tiny-distilroberta-base'''  ,framework='''tf'''    )
						snake_case__      : List[str]							=      None
						snake_case__      : Dict							=      None
						self.run_pipeline_test(SCREAMING_SNAKE_CASE_  ,[]    )



		def       __lowerCamelCase     (	self	:List[Any]  ,__lowercase	:List[str]  ,__lowercase	:int  ,__lowercase	:Optional[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__      : Tuple							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_    )
						snake_case__      : Tuple							=      [
						    F"""This is another {tokenizer.mask_token} test""",
						]
						return fill_masker, examples



		def       __lowerCamelCase     (	self	:Tuple  ,__lowercase	:int  ,__lowercase	:List[str]    ):
						snake_case__      : Optional[int]							=      fill_masker.tokenizer
						snake_case__      : Optional[int]							=      fill_masker.model

						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_    )},
						    ]  ,)

						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_    )},
						    ]  ,)

						snake_case__      : Union[str, 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       __lowerCamelCase     (	self	:Union[str, Any]  ,__lowercase	:Optional[int]  ,__lowercase	:Optional[Any]    ):
						snake_case__      : List[Any]							=      tokenizer.get_vocab()
						snake_case__      : Union[str, Any]							=      sorted(vocab.keys()    )[:2]
						# Pipeline argument
						snake_case__      : List[Any]							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_  ,targets=SCREAMING_SNAKE_CASE_    )
						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_    )},
						    ]  ,)
						snake_case__      : Dict							=      {vocab[el] for el in targets}
						self.assertEqual({el['''token'''] for el in outputs}  ,SCREAMING_SNAKE_CASE_    )
						snake_case__      : int							=      [tokenizer.decode([x]    ) for x in target_ids]
						self.assertEqual({el['''token_str'''] for el in outputs}  ,set(SCREAMING_SNAKE_CASE_    )    )

						# Call argument
						snake_case__      : Optional[int]							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_    )
						snake_case__      : Dict							=      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_    )},
						    ]  ,)
						snake_case__      : List[Any]							=      {vocab[el] for el in targets}
						self.assertEqual({el['''token'''] for el in outputs}  ,SCREAMING_SNAKE_CASE_    )
						snake_case__      : List[str]							=      [tokenizer.decode([x]    ) for x in target_ids]
						self.assertEqual({el['''token_str'''] for el in outputs}  ,set(SCREAMING_SNAKE_CASE_    )    )

						# Score equivalence
						snake_case__      : Any							=      fill_masker(F"""This is a {tokenizer.mask_token}"""  ,targets=SCREAMING_SNAKE_CASE_    )
						snake_case__      : Dict							=      [top_mask['''token_str'''] for top_mask in outputs]
						snake_case__      : List[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_    ):
										snake_case__      : List[str]							=      fill_masker(F"""This is a {tokenizer.mask_token}"""  ,targets=SCREAMING_SNAKE_CASE_    )
										snake_case__      : List[Any]							=      [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_    ):
										snake_case__      : Optional[int]							=      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_    ):
														snake_case__      : Optional[int]							=      fill_masker(F"""This is a {tokenizer.mask_token}"""  ,targets=['''''']    )
										with self.assertRaises(SCREAMING_SNAKE_CASE_    ):
														snake_case__      : List[Any]							=      fill_masker(F"""This is a {tokenizer.mask_token}"""  ,targets=''''''    )



		def       __lowerCamelCase     (	self	:str  ,__lowercase	:List[Any]  ,__lowercase	:List[str]    ):
						snake_case__      : Any							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_  ,top_k=2    )
						snake_case__      : List[str]							=      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_    )},
						    ]  ,)

						snake_case__      : Union[str, Any]							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_    )
						snake_case__      : Optional[int]							=      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       __lowerCamelCase     (	self	:Union[str, Any]  ,__lowercase	:Optional[Any]  ,__lowercase	:Dict    ):
						snake_case__      : Union[str, Any]							=      tokenizer.get_vocab()
						snake_case__      : Optional[Any]							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_    )

						# top_k=2, ntargets=3
						snake_case__      : Union[str, Any]							=      sorted(vocab.keys()    )[:3]
						snake_case__      : Optional[int]							=      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
						snake_case__      : int							=      [el['''token_str'''] for el in sorted(SCREAMING_SNAKE_CASE_  ,key=lambda __lowercase    : 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_    ):
										snake_case__      : Optional[int]							=      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       __lowerCamelCase     (	self	:Union[str, Any]  ,__lowercase	:Optional[Any]  ,__lowercase	:List[Any]    ):
						snake_case__      : str							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_    )
						snake_case__      : Tuple							=      tokenizer.get_vocab()
						# String duplicates + id duplicates
						snake_case__      : List[Any]							=      sorted(vocab.keys()    )[:3]
						snake_case__      : str							=      [targets[0], targets[1], targets[0], targets[2], targets[1]]
						snake_case__      : Optional[int]							=      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       __lowerCamelCase     (	self	:Dict  ,__lowercase	:int  ,__lowercase	:List[Any]    ):
						snake_case__      : str							=      FillMaskPipeline(model=SCREAMING_SNAKE_CASE_  ,tokenizer=SCREAMING_SNAKE_CASE_    )

						snake_case__      : List[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 logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCAmelCase__				=					logging.getLogger(__name__)





@dataclass
class 			snake_case  :
							UpperCAmelCase__      =   field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,      )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''Whether tp freeze the encoder.'''} )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''Whether  to freeze the embeddings.'''} )







@dataclass
class 			snake_case  :
							UpperCAmelCase__      =   field(
							    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
							UpperCAmelCase__      =   field(
							    default='''summarization'''							,      metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}							,      )
							UpperCAmelCase__      =   field(
							    default=1_024							,      metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }							,      )
							UpperCAmelCase__      =   field(
							    default=128							,      metadata={
							        '''help''': (
							            '''The maximum total sequence length for target text after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }							,      )
							UpperCAmelCase__      =   field(
							    default=142							,      metadata={
							        '''help''': (
							            '''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded. '''
							            '''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
							            '''during ``evaluate`` and ``predict``.'''
							        )
							    }							,      )
							UpperCAmelCase__      =   field(
							    default=142							,      metadata={
							        '''help''': (
							            '''The maximum total sequence length for test target text after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }							,      )
							UpperCAmelCase__      =   field(default=-1							,      metadata={'''help''': '''# training examples. -1 means use all.'''} )
							UpperCAmelCase__      =   field(default=-1							,      metadata={'''help''': '''# validation examples. -1 means use all.'''} )
							UpperCAmelCase__      =   field(default=-1							,      metadata={'''help''': '''# test examples. -1 means use all.'''} )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''Source language id for translation.'''} )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''Target language id for translation.'''} )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''# num_beams to use for evaluation.'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}							,      )







def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		logger.info(F'***** {split} metrics *****'     )
		for key in sorted(metrics.keys()     ):
				logger.info(F'  {key} = {metrics[key]}'     )
		save_json(__a,				os.path.join(__a,				F'{split}_results.json'     )     )



def 							_lowerCamelCase			(			):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		SCREAMING_SNAKE_CASE_    			=			HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)     )

		if len(sys.argv     ) == 2 and sys.argv[1].endswith('''.json'''     ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			parser.parse_args_into_dataclasses()

		check_output_dir(__a     )

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',				datefmt='''%m/%d/%Y %H:%M:%S''',				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,				)
		logger.warning(
		    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',				training_args.local_rank,				training_args.device,				training_args.n_gpu,				bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED     ),				training_args.fpaa,				)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank     ):
				transformers.utils.logging.set_verbosity_info()
		logger.info('''Training/evaluation parameters %s''',				__a     )

		# Set seed
		set_seed(training_args.seed     )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		SCREAMING_SNAKE_CASE_    			=			AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path,				cache_dir=model_args.cache_dir,				)

		SCREAMING_SNAKE_CASE_    			=			('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
		for p in extra_model_params:
				if getattr(__a,				__a,				__a     ):
						assert hasattr(__a,				__a     ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
						setattr(__a,				__a,				getattr(__a,				__a     )     )

		SCREAMING_SNAKE_CASE_    			=			AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,				cache_dir=model_args.cache_dir,				)
		SCREAMING_SNAKE_CASE_    			=			AutoModelForSeqaSeqLM.from_pretrained(
		    model_args.model_name_or_path,				from_tf='''.ckpt''' in model_args.model_name_or_path,				config=__a,				cache_dir=model_args.cache_dir,				)

		# use task specific params
		use_task_specific_params(__a,				data_args.task     )

		# set num_beams for evaluation
		if data_args.eval_beams is None:
				SCREAMING_SNAKE_CASE_    			=			model.config.num_beams

		# set decoder_start_token_id for MBart
		if model.config.decoder_start_token_id is None and isinstance(__a,				(MBartTokenizer, MBartTokenizerFast)     ):
				assert (
				    data_args.tgt_lang is not None and data_args.src_lang is not None
				), "mBart requires --tgt_lang and --src_lang"
				if isinstance(__a,				__a     ):
						SCREAMING_SNAKE_CASE_    			=			tokenizer.lang_code_to_id[data_args.tgt_lang]
				else:
						SCREAMING_SNAKE_CASE_    			=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang     )

		if model_args.freeze_embeds:
				freeze_embeds(__a     )
		if model_args.freeze_encoder:
				freeze_params(model.get_encoder()     )
				assert_all_frozen(model.get_encoder()     )

		SCREAMING_SNAKE_CASE_    			=			SeqaSeqDataset

		# Get datasets
		SCREAMING_SNAKE_CASE_    			=			(
		    dataset_class(
		        __a,				type_path='''train''',				data_dir=data_args.data_dir,				n_obs=data_args.n_train,				max_target_length=data_args.max_target_length,				max_source_length=data_args.max_source_length,				prefix=model.config.prefix or '''''',				)
		    if training_args.do_train
		    else None
		)
		SCREAMING_SNAKE_CASE_    			=			(
		    dataset_class(
		        __a,				type_path='''val''',				data_dir=data_args.data_dir,				n_obs=data_args.n_val,				max_target_length=data_args.val_max_target_length,				max_source_length=data_args.max_source_length,				prefix=model.config.prefix or '''''',				)
		    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
		    else None
		)
		SCREAMING_SNAKE_CASE_    			=			(
		    dataset_class(
		        __a,				type_path='''test''',				data_dir=data_args.data_dir,				n_obs=data_args.n_test,				max_target_length=data_args.test_max_target_length,				max_source_length=data_args.max_source_length,				prefix=model.config.prefix or '''''',				)
		    if training_args.do_predict
		    else None
		)

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE_    			=			(
		    build_compute_metrics_fn(data_args.task,				__a     ) if training_args.predict_with_generate else None
		)
		SCREAMING_SNAKE_CASE_    			=			SeqaSeqTrainer(
		    model=__a,				args=__a,				data_args=__a,				train_dataset=__a,				eval_dataset=__a,				data_collator=SeqaSeqDataCollator(
		        __a,				__a,				model.config.decoder_start_token_id,				training_args.tpu_num_cores     ),				compute_metrics=__a,				tokenizer=__a,				)

		SCREAMING_SNAKE_CASE_    			=			{}
		# Training
		if training_args.do_train:
				logger.info('''*** Train ***'''     )

				SCREAMING_SNAKE_CASE_    			=			trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
				SCREAMING_SNAKE_CASE_    			=			train_result.metrics
				SCREAMING_SNAKE_CASE_    			=			data_args.n_train

				trainer.save_model()  # this also saves the tokenizer

				if trainer.is_world_process_zero():
						handle_metrics('''train''',				__a,				training_args.output_dir     )
						all_metrics.update(__a     )

						# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
						trainer.state.save_to_json(os.path.join(training_args.output_dir,				'''trainer_state.json'''     )     )

						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						tokenizer.save_pretrained(training_args.output_dir     )

    # Evaluation
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''     )

				SCREAMING_SNAKE_CASE_    			=			trainer.evaluate(metric_key_prefix='''val'''     )
				SCREAMING_SNAKE_CASE_    			=			data_args.n_val
				SCREAMING_SNAKE_CASE_    			=			round(metrics['''val_loss'''],				4     )

				if trainer.is_world_process_zero():
						handle_metrics('''val''',				__a,				training_args.output_dir     )
						all_metrics.update(__a     )

		if training_args.do_predict:
				logger.info('''*** Predict ***'''     )

				SCREAMING_SNAKE_CASE_    			=			trainer.predict(test_dataset=__a,				metric_key_prefix='''test'''     )
				SCREAMING_SNAKE_CASE_    			=			test_output.metrics
				SCREAMING_SNAKE_CASE_    			=			data_args.n_test

				if trainer.is_world_process_zero():
						SCREAMING_SNAKE_CASE_    			=			round(metrics['''test_loss'''],				4     )
						handle_metrics('''test''',				__a,				training_args.output_dir     )
						all_metrics.update(__a     )

						if training_args.predict_with_generate:
								SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_decode(
								    test_output.predictions,				skip_special_tokens=__a,				clean_up_tokenization_spaces=__a     )
								SCREAMING_SNAKE_CASE_    			=			lmap(str.strip,				__a     )
								write_txt_file(__a,				os.path.join(training_args.output_dir,				'''test_generations.txt'''     )     )

		if trainer.is_world_process_zero():
				save_json(__a,				os.path.join(training_args.output_dir,				'''all_results.json'''     )     )

		return all_metrics



def 							_lowerCamelCase			(			__a     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()