





"""simple docstring"""


import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

lowerCamelCase__						= {
    "bart": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "bert": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-base-cased-finetuned-mrpc": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "dpr": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "gpt2": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlnet": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm-roberta": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "transfo-xl": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "openai-gpt": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "roberta": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "layoutlm": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "roberta-large-mnli": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "camembert": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "flaubert": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert-base-distilled-squad": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert-visual-feature-encoder": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "ctrl": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "albert": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "t5": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "electra": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "wav2vec2": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_=False		,lowercase_=True      )       ->		Tuple:




      """simple docstring"""

      if model_type not in MODEL_CLASSES:
            raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()      )}.'''      )

      _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							MODEL_CLASSES[model_type]

      # Initialise TF model
      if config_file in aws_config_map:
            _UpperCamelCase   :						List[Any]    =							cached_file(lowercase_		,lowercase_		,force_download=not use_cached_models      )
      _UpperCamelCase   :						List[str]    =							config_class.from_json_file(lowercase_      )
      _UpperCamelCase   :						Any    =							True
      _UpperCamelCase   :						Tuple    =							True
      print(F'''Building TensorFlow model from configuration: {config}'''      )
      _UpperCamelCase   :						str    =							model_class(lowercase_      )

      # Load weights from tf checkpoint
      if pytorch_checkpoint_path in aws_config_map.keys():
            _UpperCamelCase   :						Optional[int]    =							cached_file(
                lowercase_		,lowercase_		,force_download=not use_cached_models      )
      # Load PyTorch checkpoint in tf2 model:
      _UpperCamelCase   :						Dict    =							load_pytorch_checkpoint_in_tfa_model(lowercase_		,lowercase_      )

      if compare_with_pt_model:
            _UpperCamelCase   :						List[str]    =							tf_model(tf_model.dummy_inputs		,training=lowercase_      )  # build the network

            _UpperCamelCase   :						List[Any]    =							torch.load(lowercase_		,map_location="cpu"      )
            _UpperCamelCase   :						List[str]    =							pt_model_class.from_pretrained(
                pretrained_model_name_or_path=lowercase_		,config=lowercase_		,state_dict=lowercase_      )

            with torch.no_grad():
                  _UpperCamelCase   :						List[Any]    =							pt_model(**pt_model.dummy_inputs      )

            _UpperCamelCase   :						Dict    =							pto[0].numpy()
            _UpperCamelCase   :						Optional[Any]    =							tfo[0].numpy()
            _UpperCamelCase   :						Dict    =							np.amax(np.abs(np_pt - np_tf      )      )
            print(F'''Max absolute difference between models outputs {diff}'''      )
            assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

      # Save pytorch-model
      print(F'''Save TensorFlow model to {tf_dump_path}'''      )
      tf_model.save_weights(lowercase_		,save_format="h5"      )




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_=False		,lowercase_=False		,lowercase_=False		,lowercase_=False		,)       ->		Optional[int]:




      """simple docstring"""

      if args_model_type is None:
            _UpperCamelCase   :						str    =							list(MODEL_CLASSES.keys()      )
      else:
            _UpperCamelCase   :						Optional[int]    =							[args_model_type]

      for j, model_type in enumerate(lowercase_		,start=1      ):
            print("=" * 100      )
            print(F''' Converting model type {j}/{len(lowercase_      )}: {model_type}'''      )
            print("=" * 100      )
            if model_type not in MODEL_CLASSES:
                  raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()      )}.'''      )

            _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Optional[int]    =							MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  _UpperCamelCase   :						int    =							list(aws_model_maps.keys()      )
            if config_shortcut_names_or_path is None:
                  _UpperCamelCase   :						int    =							model_shortcut_names_or_path

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(lowercase_		,lowercase_      )		,start=1      ):
                  print("-" * 100      )
                  if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                        if not only_convert_finetuned_models:
                              print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''      )
                              continue
                        _UpperCamelCase   :						List[Any]    =							model_shortcut_name
                  elif only_convert_finetuned_models:
                        print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''      )
                        continue
                  print(
                      F'''    Converting checkpoint {i}/{len(lowercase_      )}: {model_shortcut_name} - model_type {model_type}'''      )
                  print("-" * 100      )

                  if config_shortcut_name in aws_config_map:
                        _UpperCamelCase   :						Dict    =							cached_file(lowercase_		,lowercase_		,force_download=not use_cached_models      )
                  else:
                        _UpperCamelCase   :						List[Any]    =							config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        _UpperCamelCase   :						Tuple    =							cached_file(lowercase_		,lowercase_		,force_download=not use_cached_models      )
                  else:
                        _UpperCamelCase   :						Optional[Any]    =							model_shortcut_name

                  if os.path.isfile(lowercase_      ):
                        _UpperCamelCase   :						Any    =							"converted_model"

                  convert_pt_checkpoint_to_tf(
                      model_type=lowercase_		,pytorch_checkpoint_path=lowercase_		,config_file=lowercase_		,tf_dump_path=os.path.join(lowercase_		,model_shortcut_name + "-tf_model.h5"      )		,compare_with_pt_model=lowercase_		,)
                  if remove_cached_files:
                        os.remove(lowercase_      )
                        os.remove(lowercase_      )


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
  )
  parser.add_argument(
      "--model_type",
      default=None,
      type=str,
      help=(
          f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
          "convert all the models from AWS."
      ),
  )
  parser.add_argument(
      "--pytorch_checkpoint_path",
      default=None,
      type=str,
      help=(
          "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
          "If not given, will download and convert all the checkpoints from AWS."
      ),
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      help=(
          "The config json file corresponding to the pre-trained model. \n"
          "This specifies the model architecture. If not given and "
          "--pytorch_checkpoint_path is not given or is a shortcut name "
          "use the configuration associated to the shortcut name on the AWS"
      ),
  )
  parser.add_argument(
      "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
  )
  parser.add_argument(
      "--use_cached_models",
      action="store_true",
      help="Use cached models if possible instead of updating to latest checkpoint versions.",
  )
  parser.add_argument(
      "--remove_cached_files",
      action="store_true",
      help="Remove pytorch models after conversion (save memory when converting in batches).",
  )
  parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
  lowerCamelCase__						= parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )







"""simple docstring"""


import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      Adafactor,
      AdamW,
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_inverse_sqrt_schedule,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						str    =							[]
      for _ in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
      return lrs




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[]
      for step in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        _UpperCamelCase   :						List[str]    =							os.path.join(lowercase_		,"schedule.bin"      )
                        torch.save(scheduler.state_dict()		,lowercase_      )

                        _UpperCamelCase   :						Optional[int]    =							torch.load(lowercase_      )
                        scheduler.load_state_dict(lowercase_      )
      return lrs



@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Dict       , __a :							Any       , __a :							List[str]						)		->					List[str]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					str:
        _UpperCamelCase   :						Optional[int]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						Any    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							AdamW(params=[w]       , lr=2e-1       , weight_decay=0.0						)
        for _ in range(100						):
              _UpperCamelCase   :						List[Any]    =							criterion(__a       , __a						)
              loss.backward()
              optimizer.step()
              w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
              w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()       , [0.4, 0.2, -0.5]       , tol=1e-2						)


  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						Any    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						List[str]    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							Adafactor(
            params=[w]       , lr=1e-2       , eps=(1e-3_0, 1e-3)       , clip_threshold=1.0       , decay_rate=-0.8       , betaa=__a       , weight_decay=0.0       , relative_step=__a       , scale_parameter=__a       , warmup_init=__a       , )
        for _ in range(1000						):
              _UpperCamelCase   :						Optional[Any]    =							criterion(__a       , __a						)
              loss.backward()
              optimizer.step()
              w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
              w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()       , [0.4, 0.2, -0.5]       , tol=1e-2						)





@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       nn.Linear(50		,	50 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       AdamW(m.parameters()		,	lr=10.0 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[str]						  =       10

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Tuple       , __a :							List[Any]       , __a :							Dict       , __a :							str=None						)		->					List[Any]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a       , msg=__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					List[Any]:
        _UpperCamelCase   :						Union[str, Any]    =							{"num_warmup_steps": 2, "num_training_steps": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        _UpperCamelCase   :						Optional[Any]    =							{
            get_constant_schedule: ({}, [10.0] * self.num_steps),
            get_constant_schedule_with_warmup: (
                {"num_warmup_steps": 4},
                [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
            ),
            get_linear_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
            ),
            get_cosine_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
            ),
            get_cosine_with_hard_restarts_schedule_with_warmup: (
                {**common_kwargs, "num_cycles": 2},
                [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
            ),
            get_polynomial_decay_schedule_with_warmup: (
                {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
                [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
            ),
            get_inverse_sqrt_schedule: (
                {"num_warmup_steps": 2},
                [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
            ),
        }

        for scheduler_func, data in scheds.items():
              _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							data

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              self.assertEqual(len([scheduler.get_lr()[0]]						)       , 1						)
              _UpperCamelCase   :						Optional[int]    =							unwrap_schedule(__a       , self.num_steps						)
              self.assertListAlmostEqual(
                  __a       , __a       , tol=1e-2       , msg=F'''failed for {scheduler_func} in normal scheduler'''       , )

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              if scheduler_func.__name__ != "get_constant_schedule":
                    LambdaScheduleWrapper.wrap_scheduler(__a						)  # wrap to test picklability of the schedule
              _UpperCamelCase   :						int    =							unwrap_and_save_reload_schedule(__a       , self.num_steps						)
              self.assertListEqual(__a       , __a       , msg=F'''failed for {scheduler_func} in save and reload'''						)





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							List[Any]       , __a :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						Any    =							fn

  def __call__(		self :							Tuple       , *__a :							Optional[Any]       , **__a :							str						)		->					Any:
        return self.fn(*__a       , **__a						)


  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Any						)		->					Tuple:
        _UpperCamelCase   :						Any    =							list(map(self       , scheduler.lr_lambdas						)						)
