



'''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        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						MODEL_FOR_MASKED_LM_MAPPING
    lowercase   =						TF_MODEL_FOR_MASKED_LM_MAPPING




    def lowerCamelCase			(       self   :	int		):
          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   :	Union[str, Any]		):
          snake_case__    :       int       		= pipeline(task="""fill-mask"""					,	model="""sshleifer/tiny-distilroberta-base"""					,	top_k=2					,	framework="""tf"""		)
          snake_case__    :       Optional[Any]       		= unmasker("""My name is <mask>"""		)
          self.assertEqual(
              nested_simplify(snake_case_					,	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"""},
              ]					,	)

          snake_case__    :       int       		= unmasker("""The largest city in France is <mask>"""		)
          self.assertEqual(
              nested_simplify(snake_case_					,	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""",
                  },
              ]					,	)

          snake_case__    :       Any       		= unmasker("""My name is <mask>"""					,	targets=[""" Patrick""", """ Clara""", """ Teven"""]					,	top_k=3		)
          self.assertEqual(
              nested_simplify(snake_case_					,	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 lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= pipeline(task="""fill-mask"""					,	model="""sshleifer/tiny-distilroberta-base"""					,	top_k=2					,	framework="""pt"""		)

          snake_case__    :       Any       		= unmasker("""My name is <mask>"""		)
          self.assertEqual(
              nested_simplify(snake_case_					,	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"""},
              ]					,	)

          snake_case__    :       str       		= unmasker("""The largest city in France is <mask>"""		)
          self.assertEqual(
              nested_simplify(snake_case_					,	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"""},
              ]					,	)

          snake_case__    :       List[str]       		= unmasker("""My name is <mask>"""					,	targets=[""" Patrick""", """ Clara""", """ Teven"""]					,	top_k=3		)
          self.assertEqual(
              nested_simplify(snake_case_					,	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"""},
              ]					,	)

          snake_case__    :       List[Any]       		= unmasker("""My name is <mask> <mask>"""					,	top_k=2		)

          self.assertEqual(
              nested_simplify(snake_case_					,	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 lowerCamelCase			(       self   :	str		):
          snake_case__    :       Tuple       		= pipeline("""fill-mask"""					,	model="""hf-internal-testing/tiny-random-distilbert"""					,	device=0					,	framework="""pt"""		)

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

          snake_case__    :       int       		= pipe("""Paris is the [MASK] of France."""		)
          # We actually don't care about the result, we just want to make sure
          # it works, meaning the float16 tensor got casted back to float32
          # for postprocessing.
          self.assertIsInstance(snake_case_					,	snake_case_		)




    @slow
    @require_torch
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       str       		= pipeline(task="""fill-mask"""					,	model="""distilroberta-base"""					,	top_k=2					,	framework="""pt"""		)
          self.run_large_test(snake_case_		)




    @slow
    @require_tf
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= pipeline(task="""fill-mask"""					,	model="""distilroberta-base"""					,	top_k=2					,	framework="""tf"""		)
          self.run_large_test(snake_case_		)




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	List[str]		):
          snake_case__    :       Tuple       		= unmasker("""My name is <mask>"""		)
          self.assertEqual(
              nested_simplify(snake_case_		)					,	[
                  {"""sequence""": """My name is John""", """score""": 0.008, """token""": 610, """token_str""": """ John"""},
                  {"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 1_573, """token_str""": """ Chris"""},
              ]					,	)
          snake_case__    :       Dict       		= unmasker("""The largest city in France is <mask>"""		)
          self.assertEqual(
              nested_simplify(snake_case_		)					,	[
                  {
                      """sequence""": """The largest city in France is Paris""",
                      """score""": 0.251,
                      """token""": 2_201,
                      """token_str""": """ Paris""",
                  },
                  {
                      """sequence""": """The largest city in France is Lyon""",
                      """score""": 0.214,
                      """token""": 12_790,
                      """token_str""": """ Lyon""",
                  },
              ]					,	)

          snake_case__    :       Tuple       		= unmasker("""My name is <mask>"""					,	targets=[""" Patrick""", """ Clara""", """ Teven"""]					,	top_k=3		)
          self.assertEqual(
              nested_simplify(snake_case_		)					,	[
                  {"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3_499, """token_str""": """ Patrick"""},
                  {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 13_606, """token_str""": """ Clara"""},
                  {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2_941, """token_str""": """ Te"""},
              ]					,	)




    @require_torch
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[Any]       		= pipeline(task="""fill-mask"""					,	model="""sshleifer/tiny-distilroberta-base"""					,	framework="""pt"""		)
          snake_case__    :       str       		= None
          snake_case__    :       str       		= None
          self.run_pipeline_test(snake_case_					,	[]		)




    @require_tf
    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       List[Any]       		= pipeline(task="""fill-mask"""					,	model="""sshleifer/tiny-distilroberta-base"""					,	framework="""tf"""		)
          snake_case__    :       Optional[int]       		= None
          snake_case__    :       Any       		= None
          self.run_pipeline_test(snake_case_					,	[]		)




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[Any]					,	snake_case_   :	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=snake_case_					,	tokenizer=snake_case_		)
          snake_case__    :       Optional[Any]       		= [
              f"This is another {tokenizer.mask_token} test",
          ]
          return fill_masker, examples




    def lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	Dict					,	snake_case_   :	Tuple		):
          snake_case__    :       List[str]       		= fill_masker.tokenizer
          snake_case__    :       Optional[Any]       		= fill_masker.model

          snake_case__    :       Tuple       		= fill_masker(
              f"This is a {tokenizer.mask_token}"					,	)
          self.assertEqual(
              snake_case_					,	[
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
              ]					,	)

          snake_case__    :       str       		= fill_masker([f"This is a {tokenizer.mask_token}"]		)
          self.assertEqual(
              snake_case_					,	[
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
              ]					,	)

          snake_case__    :       List[str]       		= fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."]		)
          self.assertEqual(
              snake_case_					,	[
                  [
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  ],
                  [
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  ],
              ]					,	)

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

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




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Dict					,	snake_case_   :	Dict		):
          snake_case__    :       Any       		= tokenizer.get_vocab()
          snake_case__    :       Any       		= sorted(vocab.keys()		)[:2]
          # Pipeline argument
          snake_case__    :       Any       		= FillMaskPipeline(model=snake_case_					,	tokenizer=snake_case_					,	targets=snake_case_		)
          snake_case__    :       List[Any]       		= fill_masker(f"This is a {tokenizer.mask_token}"		)
          self.assertEqual(
              snake_case_					,	[
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
              ]					,	)
          snake_case__    :       Dict       		= {vocab[el] for el in targets}
          self.assertEqual({el["""token"""] for el in outputs}					,	snake_case_		)
          snake_case__    :       int       		= [tokenizer.decode([x]		) for x in target_ids]
          self.assertEqual({el["""token_str"""] for el in outputs}					,	set(snake_case_		)		)

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

          # Score equivalence
          snake_case__    :       str       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	targets=snake_case_		)
          snake_case__    :       Union[str, Any]       		= [top_mask["""token_str"""] for top_mask in outputs]
          snake_case__    :       Optional[int]       		= [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(snake_case_		) == set(snake_case_		):
                snake_case__    :       Any       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	targets=snake_case_		)
                snake_case__    :       Any       		= [top_mask["""score"""] for top_mask in unmasked_targets]
                self.assertEqual(nested_simplify(snake_case_		)					,	nested_simplify(snake_case_		)		)

          # Raises with invalid
          with self.assertRaises(snake_case_		):
                snake_case__    :       str       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	targets=[]		)
          # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
          if "" not in tokenizer.get_vocab():
                with self.assertRaises(snake_case_		):
                      snake_case__    :       Tuple       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	targets=[""""""]		)
                with self.assertRaises(snake_case_		):
                      snake_case__    :       str       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	targets=""""""		)




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	int					,	snake_case_   :	str		):
          snake_case__    :       Tuple       		= FillMaskPipeline(model=snake_case_					,	tokenizer=snake_case_					,	top_k=2		)
          snake_case__    :       Union[str, Any]       		= fill_masker(f"This is a {tokenizer.mask_token}"		)
          self.assertEqual(
              snake_case_					,	[
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
              ]					,	)

          snake_case__    :       Optional[Any]       		= FillMaskPipeline(model=snake_case_					,	tokenizer=snake_case_		)
          snake_case__    :       Tuple       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	top_k=2		)
          self.assertEqual(
              snake_case_					,	[
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
              ]					,	)
          self.assertEqual(nested_simplify(snake_case_		)					,	nested_simplify(snake_case_		)		)




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]					,	snake_case_   :	List[Any]		):
          snake_case__    :       List[Any]       		= tokenizer.get_vocab()
          snake_case__    :       List[Any]       		= FillMaskPipeline(model=snake_case_					,	tokenizer=snake_case_		)

          # top_k=2, ntargets=3
          snake_case__    :       List[Any]       		= sorted(vocab.keys()		)[:3]
          snake_case__    :       Tuple       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	top_k=2					,	targets=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(snake_case_					,	key=lambda snake_case_		: x["score"]					,	reverse=snake_case_		)]
          # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
          if set(snake_case_		).issubset(snake_case_		):
                snake_case__    :       Optional[Any]       		= fill_masker(f"This is a {tokenizer.mask_token}"					,	top_k=3					,	targets=snake_case_		)
                # They should yield exactly the same result
                self.assertEqual(nested_simplify(snake_case_		)					,	nested_simplify(snake_case_		)		)




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Optional[Any]					,	snake_case_   :	List[str]		):
          snake_case__    :       str       		= FillMaskPipeline(model=snake_case_					,	tokenizer=snake_case_		)
          snake_case__    :       Any       		= tokenizer.get_vocab()
          # String duplicates + id duplicates
          snake_case__    :       Any       		= sorted(vocab.keys()		)[:3]
          snake_case__    :       Optional[Any]       		= [targets[0], targets[1], targets[0], targets[2], targets[1]]
          snake_case__    :       Union[str, Any]       		= fill_masker(f"My name is {tokenizer.mask_token}"					,	targets=snake_case_					,	top_k=10		)

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




    def lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	Dict					,	snake_case_   :	Tuple		):
          snake_case__    :       Optional[int]       		= FillMaskPipeline(model=snake_case_					,	tokenizer=snake_case_		)

          snake_case__    :       List[str]       		= fill_masker(
              f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"					,	top_k=2		)
          self.assertEqual(
              snake_case_					,	[
                  [
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  ],
                  [
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  ],
                  [
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                      {"""sequence""": ANY(snake_case_		), """score""": ANY(snake_case_		), """token""": ANY(snake_case_		), """token_str""": ANY(snake_case_		)},
                  ],
              ]					,	)





'''simple docstring'''





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	List[str]		):
          if isinstance(snake_case_					,	snake_case_		):
                # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
                # modified it, it will not be accepted here again, since `auto` values would have been overridden
                snake_case__    :       Optional[Any]       		= deepcopy(snake_case_		)
          elif os.path.exists(snake_case_		):
                with io.open(snake_case_					,	"""r"""					,	encoding="""utf-8"""		) as f:
                      snake_case__    :       Union[str, Any]       		= json.load(snake_case_		)
          else:
                try:
                      snake_case__    :       List[Any]       		= baseaa.urlsafe_baadecode(snake_case_		).decode("""utf-8"""		)
                      snake_case__    :       Optional[int]       		= json.loads(snake_case_		)
                except (UnicodeDecodeError, AttributeError, ValueError):
                      raise ValueError(
                          f"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"		)

          snake_case__    :       str       		= config

          self.set_stage_and_offload()




    def lowerCamelCase			(       self   :	int		):
          # zero stage - this is done as early as possible, before model is created, to allow
          # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
          # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
          snake_case__    :       Union[str, Any]       		= self.get_value("""zero_optimization.stage"""					,	-1		)

          # offload
          snake_case__    :       Optional[Any]       		= False
          if self.is_zeroa() or self.is_zeroa():
                snake_case__    :       int       		= set(["""cpu""", """nvme"""]		)
                snake_case__    :       Any       		= set(
                    [
                        self.get_value("""zero_optimization.offload_optimizer.device"""		),
                        self.get_value("""zero_optimization.offload_param.device"""		),
                    ]		)
                if len(offload_devices & offload_devices_valid		) > 0:
                      snake_case__    :       List[Any]       		= True




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       Optional[Any]       		= self.config

          # find the config node of interest if it exists
          snake_case__    :       List[Any]       		= ds_key_long.split("""."""		)
          snake_case__    :       Tuple       		= nodes.pop()
          for node in nodes:
                snake_case__    :       Optional[Any]       		= config.get(snake_case_		)
                if config is None:
                      return None, ds_key

          return config, ds_key




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	int					,	snake_case_   :	List[str]=None		):
          snake_case__		,							snake_case__    :       Dict       		= self.find_config_node(snake_case_		)
          if config is None:
                return default
          return config.get(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Optional[int]					,	snake_case_   :	int=False		):
          snake_case__    :       Dict       		= self.config

          # find the config node of interest if it exists
          snake_case__    :       str       		= ds_key_long.split("""."""		)
          for node in nodes:
                snake_case__    :       Union[str, Any]       		= config
                snake_case__    :       List[Any]       		= config.get(snake_case_		)
                if config is None:
                      if must_exist:
                            raise ValueError(f"Can't find {ds_key_long} entry in the config: {self.config}"		)
                      else:
                            return

        # if found remove it
          if parent_config is not None:
                parent_config.pop(snake_case_		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       Dict       		= self.get_value(snake_case_		)
          return False if value is None else bool(snake_case_		)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	Union[str, Any]		):
          snake_case__    :       Optional[int]       		= self.get_value(snake_case_		)
          return False if value is None else not bool(snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          return self._stage == 2




    def lowerCamelCase			(       self   :	Dict		):
          return self._stage == 3




    def lowerCamelCase			(       self   :	List[Any]		):
          return self._offload







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]		):
          snake_case__    :       Tuple       		= engine




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Optional[Any]					,	**snake_case_   :	int		):
          # runs backpropagation and handles mixed precision
          self.engine.backward(snake_case_					,	**snake_case_		)

          # Deepspeed's `engine.step` performs the following operations:
          # - gradient accumulation check
          # - gradient clipping
          # - optimizer step
          # - zero grad
          # - checking overflow
          # - lr_scheduler step (only if engine.lr_scheduler is not None)
          self.engine.step()
          # and this plugin overrides the above calls with no-ops when Accelerate runs under
          # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
          # training loop that works transparently under many training regimes.







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	Any		):
          super().__init__(snake_case_					,	device_placement=snake_case_					,	scaler=snake_case_		)
          snake_case__    :       Optional[int]       		= hasattr(self.optimizer					,	"""overflow"""		)




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	Optional[Any]=None		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




    def lowerCamelCase			(       self   :	List[Any]		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




    @property
    def lowerCamelCase			(       self   :	List[str]		):
          if self.__has_overflow__:
                return self.optimizer.overflow
          return False







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	Optional[Any]					,	snake_case_   :	Tuple		):
          super().__init__(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Optional[int]					,	snake_case_   :	Dict					,	snake_case_   :	int=0.001					,	snake_case_   :	Optional[Any]=0					,	**snake_case_   :	List[Any]		):
          snake_case__    :       int       		= params
          snake_case__    :       Optional[Any]       		= lr
          snake_case__    :       Any       		= weight_decay
          snake_case__    :       Optional[Any]       		= kwargs







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Dict					,	snake_case_   :	str					,	snake_case_   :	Optional[int]=None					,	snake_case_   :	Any=0					,	**snake_case_   :	List[str]		):
          snake_case__    :       List[Any]       		= optimizer
          snake_case__    :       List[Any]       		= total_num_steps
          snake_case__    :       List[Any]       		= warmup_num_steps
          snake_case__    :       Optional[Any]       		= kwargs
