






'''simple docstring'''





from typing import TYPE_CHECKING

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


a_  = {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_  = [
							    'Constraint',
							    'ConstraintListState',
							    'DisjunctiveConstraint',
							    'PhrasalConstraint',
							]
							a_  = [
							    'BeamHypotheses',
							    'BeamScorer',
							    'BeamSearchScorer',
							    'ConstrainedBeamSearchScorer',
							]
							a_  = [
							    'EpsilonLogitsWarper',
							    'EtaLogitsWarper',
							    'ForcedBOSTokenLogitsProcessor',
							    'ForcedEOSTokenLogitsProcessor',
							    'HammingDiversityLogitsProcessor',
							    'InfNanRemoveLogitsProcessor',
							    'LogitsProcessor',
							    'LogitsProcessorList',
							    'LogitsWarper',
							    'MinLengthLogitsProcessor',
							    'MinNewTokensLengthLogitsProcessor',
							    'NoBadWordsLogitsProcessor',
							    'NoRepeatNGramLogitsProcessor',
							    'PrefixConstrainedLogitsProcessor',
							    'RepetitionPenaltyLogitsProcessor',
							    'SequenceBiasLogitsProcessor',
							    'EncoderRepetitionPenaltyLogitsProcessor',
							    'TemperatureLogitsWarper',
							    'TopKLogitsWarper',
							    'TopPLogitsWarper',
							    'TypicalLogitsWarper',
							    'EncoderNoRepeatNGramLogitsProcessor',
							    'ExponentialDecayLengthPenalty',
							    'LogitNormalization',
							]
							a_  = [
							    'MaxNewTokensCriteria',
							    'MaxLengthCriteria',
							    'MaxTimeCriteria',
							    'StoppingCriteria',
							    'StoppingCriteriaList',
							    'validate_stopping_criteria',
							]
							a_  = [
							    '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:
							a_  = [
							    'TFForcedBOSTokenLogitsProcessor',
							    'TFForcedEOSTokenLogitsProcessor',
							    'TFLogitsProcessor',
							    'TFLogitsProcessorList',
							    'TFLogitsWarper',
							    'TFMinLengthLogitsProcessor',
							    'TFNoBadWordsLogitsProcessor',
							    'TFNoRepeatNGramLogitsProcessor',
							    'TFRepetitionPenaltyLogitsProcessor',
							    'TFTemperatureLogitsWarper',
							    'TFTopKLogitsWarper',
							    'TFTopPLogitsWarper',
							    'TFForceTokensLogitsProcessor',
							    'TFSuppressTokensAtBeginLogitsProcessor',
							    'TFSuppressTokensLogitsProcessor',
							]
							a_  = [
							    '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:
							a_  = [
							    'FlaxForcedBOSTokenLogitsProcessor',
							    'FlaxForcedEOSTokenLogitsProcessor',
							    'FlaxLogitsProcessor',
							    'FlaxLogitsProcessorList',
							    'FlaxLogitsWarper',
							    'FlaxMinLengthLogitsProcessor',
							    'FlaxTemperatureLogitsWarper',
							    'FlaxTopKLogitsWarper',
							    'FlaxTopPLogitsWarper',
							]
							a_  = [
							    '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

							a_  = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



