



import gc
import unittest

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

from .test_pipelines_common import ANY

@is_pipeline_test
class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       MODEL_FOR_MASKED_LM_MAPPING
			UpperCamelCase									=       TF_MODEL_FOR_MASKED_LM_MAPPING




			def  snake_case__     (   self							: Dict ):
					'''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  snake_case__     (   self							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	pipeline(task='''fill-mask'''       ,       model='''sshleifer/tiny-distilroberta-base'''       ,       top_k=2       ,       framework='''tf''' )
					__UpperCAmelCase    :     Optional[int]													=	unmasker('''My name is <mask>''' )
					self.assertEqual(
					    nested_simplify(a_       ,       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'''},
					    ]       ,       )

					__UpperCAmelCase    :     Optional[int]													=	unmasker('''The largest city in France is <mask>''' )
					self.assertEqual(
					    nested_simplify(a_       ,       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''',
					        },
					    ]       ,       )

					__UpperCAmelCase    :     Union[str, Any]													=	unmasker('''My name is <mask>'''       ,       targets=[''' Patrick''', ''' Clara''', ''' Teven''']       ,       top_k=3 )
					self.assertEqual(
					    nested_simplify(a_       ,       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  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





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

					__UpperCAmelCase    :     int													=	unmasker('''My name is <mask>''' )
					self.assertEqual(
					    nested_simplify(a_       ,       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'''},
					    ]       ,       )

					__UpperCAmelCase    :     int													=	unmasker('''The largest city in France is <mask>''' )
					self.assertEqual(
					    nested_simplify(a_       ,       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'''},
					    ]       ,       )

					__UpperCAmelCase    :     Dict													=	unmasker('''My name is <mask>'''       ,       targets=[''' Patrick''', ''' Clara''', ''' Teven''']       ,       top_k=3 )
					self.assertEqual(
					    nested_simplify(a_       ,       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'''},
					    ]       ,       )

					__UpperCAmelCase    :     Union[str, Any]													=	unmasker('''My name is <mask> <mask>'''       ,       top_k=2 )

					self.assertEqual(
					    nested_simplify(a_       ,       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  snake_case__     (   self							: str ):
					'''simple docstring'''





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

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

					__UpperCAmelCase    :     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(a_       ,       a_ )




			@slow
			@require_torch
			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	pipeline(task='''fill-mask'''       ,       model='''distilroberta-base'''       ,       top_k=2       ,       framework='''pt''' )
					self.run_large_test(a_ )




			@slow
			@require_tf
			def  snake_case__     (   self							: int ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	unmasker('''My name is <mask>''' )
					self.assertEqual(
					    nested_simplify(a_ )       ,       [
					        {'''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'''},
					    ]       ,       )
					__UpperCAmelCase    :     Tuple													=	unmasker('''The largest city in France is <mask>''' )
					self.assertEqual(
					    nested_simplify(a_ )       ,       [
					        {
					            '''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''',
					        },
					    ]       ,       )

					__UpperCAmelCase    :     str													=	unmasker('''My name is <mask>'''       ,       targets=[''' Patrick''', ''' Clara''', ''' Teven''']       ,       top_k=3 )
					self.assertEqual(
					    nested_simplify(a_ )       ,       [
					        {'''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  snake_case__     (   self							: Dict ):
					'''simple docstring'''





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




			@require_tf
			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: List[str]       ,       a_							: Optional[Any]       ,       a_							: List[str]       ,       a_							: Optional[Any] ):
					'''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    :     Union[str, Any]													=	FillMaskPipeline(model=a_       ,       tokenizer=a_ )
					__UpperCAmelCase    :     Tuple													=	[
					    F'This is another {tokenizer.mask_token} test',
					]
					return fill_masker, examples




			def  snake_case__     (   self							: Dict       ,       a_							: int       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	fill_masker.tokenizer
					__UpperCAmelCase    :     int													=	fill_masker.model

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

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

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

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

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




			def  snake_case__     (   self							: List[str]       ,       a_							: Optional[Any]       ,       a_							: Optional[Any] ):
					'''simple docstring'''





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

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

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

					# Raises with invalid
					with self.assertRaises(a_ ):
							__UpperCAmelCase    :     Any													=	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(a_ ):
									__UpperCAmelCase    :     Dict													=	fill_masker(F'This is a {tokenizer.mask_token}'       ,       targets=[''''''] )
							with self.assertRaises(a_ ):
									__UpperCAmelCase    :     Tuple													=	fill_masker(F'This is a {tokenizer.mask_token}'       ,       targets='''''' )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: List[str]       ,       a_							: str ):
					'''simple docstring'''





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

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




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	tokenizer.get_vocab()
					__UpperCAmelCase    :     str													=	FillMaskPipeline(model=a_       ,       tokenizer=a_ )

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

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




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: List[Any]       ,       a_							: Optional[int] ):
					'''simple docstring'''





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

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




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: Optional[Any]       ,       a_							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	FillMaskPipeline(model=a_       ,       tokenizer=a_ )

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










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__A   			=getLogger(__name__)


__A   			="cuda" if torch.cuda.is_available() else "cpu"

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					str = DEFAULT_DEVICE ,   _UpperCAmelCase							:					Optional[Any]=False ,   _UpperCAmelCase							:					Optional[int]="summarization" ,   _UpperCAmelCase							:					Dict=None ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	Path(_UpperCAmelCase						).open('''w''' ,   encoding='''utf-8'''						)
		__UpperCAmelCase    :     Optional[int]													=	str(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).to(_UpperCAmelCase						)
		if fpaa:
				__UpperCAmelCase    :     Dict													=	model.half()

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		__UpperCAmelCase    :     Dict													=	time.time()
		# update config with task specific params
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)
		if prefix is None:
				__UpperCAmelCase    :     List[Any]													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		for examples_chunk in tqdm(list(chunks(_UpperCAmelCase ,   _UpperCAmelCase						)						)						):
				__UpperCAmelCase    :     Any													=	[prefix + text for text in examples_chunk]
				__UpperCAmelCase    :     Union[str, Any]													=	tokenizer(_UpperCAmelCase ,   return_tensors='''pt''' ,   truncation=_UpperCAmelCase ,   padding='''longest'''						).to(_UpperCAmelCase						)
				__UpperCAmelCase    :     Dict													=	model.generate(
				    input_ids=batch.input_ids ,   attention_mask=batch.attention_mask ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     List[Any]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				for hypothesis in dec:
						fout.write(hypothesis + '''\n'''						)
						fout.flush()
		fout.close()
		__UpperCAmelCase    :     Dict													=	int(time.time() - start_time						)  # seconds
		__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,   4						)}

def        a						(      ):







		'''simple docstring'''





		return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)

def        a						(      _UpperCAmelCase							:					Any=True						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	argparse.ArgumentParser()
		parser.add_argument('''model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
		parser.add_argument('''input_path''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument('''save_path''' ,   type=_UpperCAmelCase ,   help='''where to save summaries'''						)
		parser.add_argument('''--reference_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''like cnn_dm/test.target'''						)
		parser.add_argument('''--score_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default='''metrics.json''' ,   help='''where to save metrics'''						)
		parser.add_argument('''--device''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''cuda, cuda:1, cpu etc.'''						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--dump-args''' ,   action='''store_true''' ,   help='''print the custom hparams with the results'''						)
		parser.add_argument(
		    '''--info''' ,   nargs='''?''' ,   type=_UpperCAmelCase ,   const=datetime_now() ,   help=(
		        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
		        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
		    ) ,   )
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	parser.parse_known_args()
		__UpperCAmelCase    :     Optional[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if parsed_args and verbose:
				print(f'parsed the following generate kwargs: {parsed_args}'						)
		__UpperCAmelCase    :     Optional[int]													=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
		if args.n_obs > 0:
				__UpperCAmelCase    :     Optional[int]													=	examples[: args.n_obs]
		Path(args.save_path						).parent.mkdir(exist_ok=_UpperCAmelCase						)

		if args.reference_path is None and Path(args.score_path						).exists():
				warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'						)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

		__UpperCAmelCase    :     int													=	generate_summaries_or_translations(
		    _UpperCAmelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_UpperCAmelCase ,   )

		if args.reference_path is None:
				return {}

		# Compute scores
		__UpperCAmelCase    :     str													=	calculate_bleu if '''translation''' in args.task else calculate_rouge
		__UpperCAmelCase    :     Tuple													=	[x.rstrip() for x in open(args.save_path						).readlines()]
		__UpperCAmelCase    :     int													=	[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_UpperCAmelCase						)]
		__UpperCAmelCase    :     dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
		scores.update(_UpperCAmelCase						)

		if args.dump_args:
				scores.update(_UpperCAmelCase						)
		if args.info:
				__UpperCAmelCase    :     Dict													=	args.info

		if verbose:
				print(_UpperCAmelCase						)

		if args.score_path is not None:
				json.dump(_UpperCAmelCase ,   open(args.score_path ,   '''w'''						)						)

		return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)





