




from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE			:      Tuple      			={
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE			:      List[Any]      			=[
          """Constraint""",
          """ConstraintListState""",
          """DisjunctiveConstraint""",
          """PhrasalConstraint""",
      ]
      __SCREAMING_SNAKE_CASE			:      Any      			=[
          """BeamHypotheses""",
          """BeamScorer""",
          """BeamSearchScorer""",
          """ConstrainedBeamSearchScorer""",
      ]
      __SCREAMING_SNAKE_CASE			:      int      			=[
          """EpsilonLogitsWarper""",
          """EtaLogitsWarper""",
          """ForcedBOSTokenLogitsProcessor""",
          """ForcedEOSTokenLogitsProcessor""",
          """HammingDiversityLogitsProcessor""",
          """InfNanRemoveLogitsProcessor""",
          """LogitsProcessor""",
          """LogitsProcessorList""",
          """LogitsWarper""",
          """MinLengthLogitsProcessor""",
          """MinNewTokensLengthLogitsProcessor""",
          """NoBadWordsLogitsProcessor""",
          """NoRepeatNGramLogitsProcessor""",
          """PrefixConstrainedLogitsProcessor""",
          """RepetitionPenaltyLogitsProcessor""",
          """SequenceBiasLogitsProcessor""",
          """EncoderRepetitionPenaltyLogitsProcessor""",
          """TemperatureLogitsWarper""",
          """TopKLogitsWarper""",
          """TopPLogitsWarper""",
          """TypicalLogitsWarper""",
          """EncoderNoRepeatNGramLogitsProcessor""",
          """ExponentialDecayLengthPenalty""",
          """LogitNormalization""",
      ]
      __SCREAMING_SNAKE_CASE			:      Any      			=[
          """MaxNewTokensCriteria""",
          """MaxLengthCriteria""",
          """MaxTimeCriteria""",
          """StoppingCriteria""",
          """StoppingCriteriaList""",
          """validate_stopping_criteria""",
      ]
      __SCREAMING_SNAKE_CASE			:      Tuple      			=[
          """GenerationMixin""",
          """top_k_top_p_filtering""",
          """GreedySearchEncoderDecoderOutput""",
          """GreedySearchDecoderOnlyOutput""",
          """SampleEncoderDecoderOutput""",
          """SampleDecoderOnlyOutput""",
          """BeamSearchEncoderDecoderOutput""",
          """BeamSearchDecoderOnlyOutput""",
          """BeamSampleEncoderDecoderOutput""",
          """BeamSampleDecoderOnlyOutput""",
          """ContrastiveSearchEncoderDecoderOutput""",
          """ContrastiveSearchDecoderOnlyOutput""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE			:      List[str]      			=[
          """TFForcedBOSTokenLogitsProcessor""",
          """TFForcedEOSTokenLogitsProcessor""",
          """TFLogitsProcessor""",
          """TFLogitsProcessorList""",
          """TFLogitsWarper""",
          """TFMinLengthLogitsProcessor""",
          """TFNoBadWordsLogitsProcessor""",
          """TFNoRepeatNGramLogitsProcessor""",
          """TFRepetitionPenaltyLogitsProcessor""",
          """TFTemperatureLogitsWarper""",
          """TFTopKLogitsWarper""",
          """TFTopPLogitsWarper""",
          """TFForceTokensLogitsProcessor""",
          """TFSuppressTokensAtBeginLogitsProcessor""",
          """TFSuppressTokensLogitsProcessor""",
      ]
      __SCREAMING_SNAKE_CASE			:      List[str]      			=[
          """TFGenerationMixin""",
          """tf_top_k_top_p_filtering""",
          """TFGreedySearchDecoderOnlyOutput""",
          """TFGreedySearchEncoderDecoderOutput""",
          """TFSampleEncoderDecoderOutput""",
          """TFSampleDecoderOnlyOutput""",
          """TFBeamSearchEncoderDecoderOutput""",
          """TFBeamSearchDecoderOnlyOutput""",
          """TFBeamSampleEncoderDecoderOutput""",
          """TFBeamSampleDecoderOnlyOutput""",
          """TFContrastiveSearchEncoderDecoderOutput""",
          """TFContrastiveSearchDecoderOnlyOutput""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE			:      Optional[int]      			=[
          """FlaxForcedBOSTokenLogitsProcessor""",
          """FlaxForcedEOSTokenLogitsProcessor""",
          """FlaxLogitsProcessor""",
          """FlaxLogitsProcessorList""",
          """FlaxLogitsWarper""",
          """FlaxMinLengthLogitsProcessor""",
          """FlaxTemperatureLogitsWarper""",
          """FlaxTopKLogitsWarper""",
          """FlaxTopPLogitsWarper""",
      ]
      __SCREAMING_SNAKE_CASE			:      int      			=[
          """FlaxGenerationMixin""",
          """FlaxGreedySearchOutput""",
          """FlaxSampleOutput""",
          """FlaxBeamSearchOutput""",
      ]

if TYPE_CHECKING:
      from .configuration_utils import GenerationConfig
      from .streamers import TextIteratorStreamer, TextStreamer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
            from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
            from .logits_process import (
                EncoderNoRepeatNGramLogitsProcessor,
                EncoderRepetitionPenaltyLogitsProcessor,
                EpsilonLogitsWarper,
                EtaLogitsWarper,
                ExponentialDecayLengthPenalty,
                ForcedBOSTokenLogitsProcessor,
                ForcedEOSTokenLogitsProcessor,
                HammingDiversityLogitsProcessor,
                InfNanRemoveLogitsProcessor,
                LogitNormalization,
                LogitsProcessor,
                LogitsProcessorList,
                LogitsWarper,
                MinLengthLogitsProcessor,
                MinNewTokensLengthLogitsProcessor,
                NoBadWordsLogitsProcessor,
                NoRepeatNGramLogitsProcessor,
                PrefixConstrainedLogitsProcessor,
                RepetitionPenaltyLogitsProcessor,
                SequenceBiasLogitsProcessor,
                TemperatureLogitsWarper,
                TopKLogitsWarper,
                TopPLogitsWarper,
                TypicalLogitsWarper,
            )
            from .stopping_criteria import (
                MaxLengthCriteria,
                MaxNewTokensCriteria,
                MaxTimeCriteria,
                StoppingCriteria,
                StoppingCriteriaList,
                validate_stopping_criteria,
            )
            from .utils import (
                BeamSampleDecoderOnlyOutput,
                BeamSampleEncoderDecoderOutput,
                BeamSearchDecoderOnlyOutput,
                BeamSearchEncoderDecoderOutput,
                ContrastiveSearchDecoderOnlyOutput,
                ContrastiveSearchEncoderDecoderOutput,
                GenerationMixin,
                GreedySearchDecoderOnlyOutput,
                GreedySearchEncoderDecoderOutput,
                SampleDecoderOnlyOutput,
                SampleEncoderDecoderOutput,
                top_k_top_p_filtering,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tf_logits_process import (
                TFForcedBOSTokenLogitsProcessor,
                TFForcedEOSTokenLogitsProcessor,
                TFForceTokensLogitsProcessor,
                TFLogitsProcessor,
                TFLogitsProcessorList,
                TFLogitsWarper,
                TFMinLengthLogitsProcessor,
                TFNoBadWordsLogitsProcessor,
                TFNoRepeatNGramLogitsProcessor,
                TFRepetitionPenaltyLogitsProcessor,
                TFSuppressTokensAtBeginLogitsProcessor,
                TFSuppressTokensLogitsProcessor,
                TFTemperatureLogitsWarper,
                TFTopKLogitsWarper,
                TFTopPLogitsWarper,
            )
            from .tf_utils import (
                TFBeamSampleDecoderOnlyOutput,
                TFBeamSampleEncoderDecoderOutput,
                TFBeamSearchDecoderOnlyOutput,
                TFBeamSearchEncoderDecoderOutput,
                TFContrastiveSearchDecoderOnlyOutput,
                TFContrastiveSearchEncoderDecoderOutput,
                TFGenerationMixin,
                TFGreedySearchDecoderOnlyOutput,
                TFGreedySearchEncoderDecoderOutput,
                TFSampleDecoderOnlyOutput,
                TFSampleEncoderDecoderOutput,
                tf_top_k_top_p_filtering,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .flax_logits_process import (
                FlaxForcedBOSTokenLogitsProcessor,
                FlaxForcedEOSTokenLogitsProcessor,
                FlaxLogitsProcessor,
                FlaxLogitsProcessorList,
                FlaxLogitsWarper,
                FlaxMinLengthLogitsProcessor,
                FlaxTemperatureLogitsWarper,
                FlaxTopKLogitsWarper,
                FlaxTopPLogitsWarper,
            )
            from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
      import sys

      __SCREAMING_SNAKE_CASE			:      Dict      			=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=get_logger(__name__)

def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				List[dict]   , lowerCamelCase:				list   , lowerCamelCase:				list   , lowerCamelCase:				int   , lowerCamelCase:				GradScaler = None   , )    ->	Any:


      '''simple docstring'''




      for i, state in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Model weights saved in {output_model_file}"""  )
      # Optimizer states
      for i, opt in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = opt.state_dict()
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Optimizer state saved in {output_optimizer_file}"""  )
      # Scheduler states
      for i, scheduler in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = scheduler.state_dict()
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Scheduler state saved in {output_scheduler_file}"""  )
      # GradScaler state
      if scaler is not None:
            lowerCamelCase__					     = scaler.state_dict()
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            torch.save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""  )
      # Random number generator states
      lowerCamelCase__					     = {}
      lowerCamelCase__					     = f"""{RNG_STATE_NAME}_{process_index}.pkl"""
      lowerCamelCase__					     = random.getstate()
      lowerCamelCase__					     = np.random.get_state()
      lowerCamelCase__					     = torch.get_rng_state()
      if is_xpu_available():
            lowerCamelCase__					     = torch.xpu.get_rng_state_all()
      else:
            lowerCamelCase__					     = torch.cuda.get_rng_state_all()
      if is_tpu_available():
            lowerCamelCase__					     = xm.get_rng_state()
      lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
      torch.save(lowerCamelCase   , lowerCamelCase  )
      logger.info(f"""Random states saved in {output_states_file}"""  )
      return output_dir

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Dict   , lowerCamelCase:				Union[str, Any]   , lowerCamelCase:				Any   , lowerCamelCase:				Any   , lowerCamelCase:				Any=None   , lowerCamelCase:				Dict=None   , **lowerCamelCase:				Optional[Any]   , )    ->	Any:


      '''simple docstring'''




      if map_location not in [None, "cpu", "on_device"]:
            raise TypeError(
                'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'  )
      if map_location is None:
            lowerCamelCase__					     = 'cpu'
      elif map_location == "on_device":
            lowerCamelCase__					     = PartialState().device
      # Model states
      for i, model in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            models[i].load_state_dict(torch.load(lowerCamelCase   , map_location=lowerCamelCase  )   , **lowerCamelCase  )
      logger.info('All model weights loaded successfully'  )

      # Optimizer states
      for i, opt in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            lowerCamelCase__					     = torch.load(lowerCamelCase   , map_location=lowerCamelCase  )
            optimizers[i].load_state_dict(lowerCamelCase  )
      logger.info('All optimizer states loaded successfully'  )

      # Scheduler states
      for i, scheduler in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            scheduler.load_state_dict(torch.load(lowerCamelCase  )  )
      logger.info('All scheduler states loaded successfully'  )

      # GradScaler state
      if scaler is not None:
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            scaler.load_state_dict(torch.load(lowerCamelCase  )  )
            logger.info('GradScaler state loaded successfully'  )

      # Random states
      try:
            lowerCamelCase__					     = torch.load(os.path.join(lowerCamelCase   , f"""{RNG_STATE_NAME}_{process_index}.pkl"""  )  )
            random.setstate(states['random_state']  )
            np.random.set_state(states['numpy_random_seed']  )
            torch.set_rng_state(states['torch_manual_seed']  )
            if is_xpu_available():
                  torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']  )
            else:
                  torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']  )
            if is_tpu_available():
                  xm.set_rng_state(states['xm_seed']  )
            logger.info('All random states loaded successfully'  )
      except Exception:
            logger.info('Could not load random states'  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict   , lowerCamelCase:				Any   , lowerCamelCase:				int = 0  )    ->	Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__					     = Path(lowerCamelCase  ) / f"""custom_checkpoint_{index}.pkl"""
      logger.info(f"""Saving the state of {get_pretty_name(lowerCamelCase  )} to {save_location}"""  )
      torch.save(obj.state_dict()   , lowerCamelCase  )


def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				int = 0  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = f"""{path}/custom_checkpoint_{index}.pkl"""
      logger.info(f"""Loading the state of {get_pretty_name(lowerCamelCase  )} from {load_location}"""  )
      obj.load_state_dict(torch.load(lowerCamelCase   , map_location='cpu'  )  )