



'''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 				snake_case (unittest.TestCase   ):
       lowerCAmelCase__  :List[Any]						   =					MODEL_FOR_MASKED_LM_MAPPING
       lowerCAmelCase__  :Union[str, Any]						   =					TF_MODEL_FOR_MASKED_LM_MAPPING



       def 							_a      (				self      )  ->							Optional[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 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						pipeline(task="fill-mask"     ,model="sshleifer/tiny-distilroberta-base"     ,top_k=2     ,framework="tf"      )
           lowercase__		       =						unmasker("My name is <mask>"      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_     ,decimals=6      )     ,[
                   {"sequence": "My name is grouped", "score": 2.1E-0_5, "token": 38_015, "token_str": " grouped"},
                   {"sequence": "My name is accuser", "score": 2.1E-0_5, "token": 25_506, "token_str": " accuser"},
               ]     ,)

           lowercase__		       =						unmasker("The largest city in France is <mask>"      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_     ,decimals=6      )     ,[
                   {
                       "sequence": "The largest city in France is grouped",
                       "score": 2.1E-0_5,
                       "token": 38_015,
                       "token_str": " grouped",
                   },
                   {
                       "sequence": "The largest city in France is accuser",
                       "score": 2.1E-0_5,
                       "token": 25_506,
                       "token_str": " accuser",
                   },
               ]     ,)

           lowercase__		       =						unmasker("My name is <mask>"     ,targets=[" Patrick", " Clara", " Teven"]     ,top_k=3      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_     ,decimals=6      )     ,[
                   {"sequence": "My name is Clara", "score": 2E-0_5, "token": 13_606, "token_str": " Clara"},
                   {"sequence": "My name is Patrick", "score": 2E-0_5, "token": 3_499, "token_str": " Patrick"},
                   {"sequence": "My name is Te", "score": 1.9E-0_5, "token": 2_941, "token_str": " Te"},
               ]     ,)



       @require_torch
       def 							_a      (				self      )  ->							str:
           lowercase__		       =						pipeline(task="fill-mask"     ,model="sshleifer/tiny-distilroberta-base"     ,top_k=2     ,framework="pt"      )

           lowercase__		       =						unmasker("My name is <mask>"      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_     ,decimals=6      )     ,[
                   {"sequence": "My name is Maul", "score": 2.2E-0_5, "token": 35_676, "token_str": " Maul"},
                   {"sequence": "My name isELS", "score": 2.2E-0_5, "token": 16_416, "token_str": "ELS"},
               ]     ,)

           lowercase__		       =						unmasker("The largest city in France is <mask>"      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_     ,decimals=6      )     ,[
                   {
                       "sequence": "The largest city in France is Maul",
                       "score": 2.2E-0_5,
                       "token": 35_676,
                       "token_str": " Maul",
                   },
                   {"sequence": "The largest city in France isELS", "score": 2.2E-0_5, "token": 16_416, "token_str": "ELS"},
               ]     ,)

           lowercase__		       =						unmasker("My name is <mask>"     ,targets=[" Patrick", " Clara", " Teven"]     ,top_k=3      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_     ,decimals=6      )     ,[
                   {"sequence": "My name is Patrick", "score": 2.1E-0_5, "token": 3_499, "token_str": " Patrick"},
                   {"sequence": "My name is Te", "score": 2E-0_5, "token": 2_941, "token_str": " Te"},
                   {"sequence": "My name is Clara", "score": 2E-0_5, "token": 13_606, "token_str": " Clara"},
               ]     ,)

           lowercase__		       =						unmasker("My name is <mask> <mask>"     ,top_k=2      )

           self.assertEqual(
               nested_simplify(UpperCAmelCase_     ,decimals=6      )     ,[
                   [
                       {
                           "score": 2.2E-0_5,
                           "token": 35_676,
                           "token_str": " Maul",
                           "sequence": "<s>My name is Maul<mask></s>",
                       },
                       {"score": 2.2E-0_5, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
                   ],
                   [
                       {
                           "score": 2.2E-0_5,
                           "token": 35_676,
                           "token_str": " Maul",
                           "sequence": "<s>My name is<mask> Maul</s>",
                       },
                       {"score": 2.2E-0_5, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
                   ],
               ]     ,)



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

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

           lowercase__		       =						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(UpperCAmelCase_     ,UpperCAmelCase_      )



       @slow
       @require_torch
       def 							_a      (				self      )  ->							int:
           lowercase__		       =						pipeline(task="fill-mask"     ,model="distilroberta-base"     ,top_k=2     ,framework="pt"      )
           self.run_large_test(UpperCAmelCase_      )



       @slow
       @require_tf
       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						pipeline(task="fill-mask"     ,model="distilroberta-base"     ,top_k=2     ,framework="tf"      )
           self.run_large_test(UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						unmasker("My name is <mask>"      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_      )     ,[
                   {"sequence": "My name is John", "score": 0.0_08, "token": 610, "token_str": " John"},
                   {"sequence": "My name is Chris", "score": 0.0_07, "token": 1_573, "token_str": " Chris"},
               ]     ,)
           lowercase__		       =						unmasker("The largest city in France is <mask>"      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_      )     ,[
                   {
                       "sequence": "The largest city in France is Paris",
                       "score": 0.2_51,
                       "token": 2_201,
                       "token_str": " Paris",
                   },
                   {
                       "sequence": "The largest city in France is Lyon",
                       "score": 0.2_14,
                       "token": 12_790,
                       "token_str": " Lyon",
                   },
               ]     ,)

           lowercase__		       =						unmasker("My name is <mask>"     ,targets=[" Patrick", " Clara", " Teven"]     ,top_k=3      )
           self.assertEqual(
               nested_simplify(UpperCAmelCase_      )     ,[
                   {"sequence": "My name is Patrick", "score": 0.0_05, "token": 3_499, "token_str": " Patrick"},
                   {"sequence": "My name is Clara", "score": 0.0_00, "token": 13_606, "token_str": " Clara"},
                   {"sequence": "My name is Te", "score": 0.0_00, "token": 2_941, "token_str": " Te"},
               ]     ,)



       @require_torch
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						pipeline(task="fill-mask"     ,model="sshleifer/tiny-distilroberta-base"     ,framework="pt"      )
           lowercase__		       =						None
           lowercase__		       =						None
           self.run_pipeline_test(UpperCAmelCase_     ,[]      )



       @require_tf
       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						pipeline(task="fill-mask"     ,model="sshleifer/tiny-distilroberta-base"     ,framework="tf"      )
           lowercase__		       =						None
           lowercase__		       =						None
           self.run_pipeline_test(UpperCAmelCase_     ,[]      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           if tokenizer is None or tokenizer.mask_token_id is None:
               self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"      )

           lowercase__		       =						FillMaskPipeline(model=UpperCAmelCase_     ,tokenizer=UpperCAmelCase_      )
           lowercase__		       =						[
               F'''This is another {tokenizer.mask_token} test''',
           ]
           return fill_masker, examples



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						fill_masker.tokenizer
           lowercase__		       =						fill_masker.model

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

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

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

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

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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
           lowercase__		       =						tokenizer.get_vocab()
           lowercase__		       =						sorted(vocab.keys()      )[:2]
           # Pipeline argument
           lowercase__		       =						FillMaskPipeline(model=UpperCAmelCase_     ,tokenizer=UpperCAmelCase_     ,targets=UpperCAmelCase_      )
           lowercase__		       =						fill_masker(F'''This is a {tokenizer.mask_token}'''      )
           self.assertEqual(
               UpperCAmelCase_     ,[
                   {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                   {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
               ]     ,)
           lowercase__		       =						{vocab[el] for el in targets}
           self.assertEqual({el["token"] for el in outputs}     ,UpperCAmelCase_      )
           lowercase__		       =						[tokenizer.decode([x]      ) for x in target_ids]
           self.assertEqual({el["token_str"] for el in outputs}     ,set(UpperCAmelCase_      )      )

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

           # Score equivalence
           lowercase__		       =						fill_masker(F'''This is a {tokenizer.mask_token}'''     ,targets=UpperCAmelCase_      )
           lowercase__		       =						[top_mask["token_str"] for top_mask in outputs]
           lowercase__		       =						[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(UpperCAmelCase_      ) == set(UpperCAmelCase_      ):
               lowercase__		       =						fill_masker(F'''This is a {tokenizer.mask_token}'''     ,targets=UpperCAmelCase_      )
               lowercase__		       =						[top_mask["score"] for top_mask in unmasked_targets]
               self.assertEqual(nested_simplify(UpperCAmelCase_      )     ,nested_simplify(UpperCAmelCase_      )      )

           # Raises with invalid
           with self.assertRaises(UpperCAmelCase_      ):
               lowercase__		       =						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(UpperCAmelCase_      ):
                   lowercase__		       =						fill_masker(F'''This is a {tokenizer.mask_token}'''     ,targets=[""]      )
               with self.assertRaises(UpperCAmelCase_      ):
                   lowercase__		       =						fill_masker(F'''This is a {tokenizer.mask_token}'''     ,targets=""      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
           lowercase__		       =						FillMaskPipeline(model=UpperCAmelCase_     ,tokenizer=UpperCAmelCase_     ,top_k=2      )
           lowercase__		       =						fill_masker(F'''This is a {tokenizer.mask_token}'''      )
           self.assertEqual(
               UpperCAmelCase_     ,[
                   {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                   {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
               ]     ,)

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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Any:
           lowercase__		       =						tokenizer.get_vocab()
           lowercase__		       =						FillMaskPipeline(model=UpperCAmelCase_     ,tokenizer=UpperCAmelCase_      )

           # top_k=2, ntargets=3
           lowercase__		       =						sorted(vocab.keys()      )[:3]
           lowercase__		       =						fill_masker(F'''This is a {tokenizer.mask_token}'''     ,top_k=2     ,targets=UpperCAmelCase_      )

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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						FillMaskPipeline(model=UpperCAmelCase_     ,tokenizer=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.get_vocab()
           # String duplicates + id duplicates
           lowercase__		       =						sorted(vocab.keys()      )[:3]
           lowercase__		       =						[targets[0], targets[1], targets[0], targets[2], targets[1]]
           lowercase__		       =						fill_masker(F'''My name is {tokenizer.mask_token}'''     ,targets=UpperCAmelCase_     ,top_k=10      )

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







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						FillMaskPipeline(model=UpperCAmelCase_     ,tokenizer=UpperCAmelCase_      )

           lowercase__		       =						fill_masker(
               F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''     ,top_k=2      )
           self.assertEqual(
               UpperCAmelCase_     ,[
                   [
                       {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                       {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                   ],
                   [
                       {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                       {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                   ],
                   [
                       {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                       {"sequence": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      ), "token": ANY(UpperCAmelCase_      ), "token_str": ANY(UpperCAmelCase_      )},
                   ],
               ]     ,)









'''simple docstring'''





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def        lowerCamelCase  (				_snake_case  :						str=None       ):





    '''simple docstring'''


    if subparsers is not None:
        lowercase__		       =						subparsers.add_parser("env"       )
    else:
        lowercase__		       =						argparse.ArgumentParser("Accelerate env command"       )

    parser.add_argument(
        "--config_file"     ,default=_snake_case     ,help="The config file to use for the default values in the launching script."       )

    if subparsers is not None:
        parser.set_defaults(func=_snake_case       )
    return parser




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''simple docstring'''


    lowercase__		       =						torch.__version__
    lowercase__		       =						torch.cuda.is_available()
    lowercase__		       =						is_xpu_available()
    lowercase__		       =						is_npu_available()

    lowercase__		       =						"Not found"
    # Get the default from the config file.
    if args.config_file is not None or os.path.isfile(_snake_case       ):
        lowercase__		       =						load_config_from_file(args.config_file       ).to_dict()

    lowercase__		       =						{
        "`Accelerate` version": version,
        "Platform": platform.platform(),
        "Python version": platform.python_version(),
        "Numpy version": np.__version__,
        "PyTorch version (GPU?)": f'''{pt_version} ({pt_cuda_available})''',
        "PyTorch XPU available": str(_snake_case       ),
        "PyTorch NPU available": str(_snake_case       ),
        "System RAM": f'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
    }
    if pt_cuda_available:
        lowercase__		       =						torch.cuda.get_device_name()

    print("\nCopy-and-paste the text below in your GitHub issue\n"       )
    print("\n".join([f'''- {prop}: {val}''' for prop, val in info.items()]       )       )

    print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"       )
    lowercase__		       =						(
        "\n".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]       )
        if isinstance(_snake_case     ,_snake_case       )
        else f'''\t{accelerate_config}'''
    )
    print(_snake_case       )

    lowercase__		       =						accelerate_config

    return info




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						env_command_parser()
    lowercase__		       =						parser.parse_args()
    env_command(_snake_case       )
    return 0


if __name__ == "__main__":
 raise SystemExit(main())




