



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase							 =					logging.getLogger(__name__)

@dataclass
class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[float]         =					field(
							    default=0.0			,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""})
							UpperCamelCase__				:      bool         =					field(default=SCREAMING_SNAKE_CASE			,metadata={"""help""": """Whether to SortishSamler or not."""})
							UpperCamelCase__				:      bool         =					field(
							    default=SCREAMING_SNAKE_CASE			,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""})
							UpperCamelCase__				:      bool         =					field(default=SCREAMING_SNAKE_CASE			,metadata={"""help""": """whether to use adafactor"""})
							UpperCamelCase__				:      Optional[float]         =					field(
							    default=SCREAMING_SNAKE_CASE			,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""})
							UpperCamelCase__				:      Optional[float]         =					field(
							    default=SCREAMING_SNAKE_CASE			,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""})
							UpperCamelCase__				:      Optional[float]         =					field(default=SCREAMING_SNAKE_CASE			,metadata={"""help""": """Dropout probability. Goes into model.config."""})
							UpperCamelCase__				:      Optional[float]         =					field(
							    default=SCREAMING_SNAKE_CASE			,metadata={"""help""": """Attention dropout probability. Goes into model.config."""})
							UpperCamelCase__				:      Optional[str]         =					field(
							    default="""linear"""			,metadata={"""help""": F"""Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys())}"""}			,)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase							 =					{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					["""XLMRobertaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					["""XLMRobertaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMRobertaForCausalLM""",
	    """XLMRobertaForMaskedLM""",
	    """XLMRobertaForMultipleChoice""",
	    """XLMRobertaForQuestionAnswering""",
	    """XLMRobertaForSequenceClassification""",
	    """XLMRobertaForTokenClassification""",
	    """XLMRobertaModel""",
	    """XLMRobertaPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMRobertaForCausalLM""",
	    """TFXLMRobertaForMaskedLM""",
	    """TFXLMRobertaForMultipleChoice""",
	    """TFXLMRobertaForQuestionAnswering""",
	    """TFXLMRobertaForSequenceClassification""",
	    """TFXLMRobertaForTokenClassification""",
	    """TFXLMRobertaModel""",
	    """TFXLMRobertaPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FlaxXLMRobertaForMaskedLM""",
	    """FlaxXLMRobertaForCausalLM""",
	    """FlaxXLMRobertaForMultipleChoice""",
	    """FlaxXLMRobertaForQuestionAnswering""",
	    """FlaxXLMRobertaForSequenceClassification""",
	    """FlaxXLMRobertaForTokenClassification""",
	    """FlaxXLMRobertaModel""",
	    """FlaxXLMRobertaPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xlm_roberta import (
	    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XLMRobertaConfig,
	    XLMRobertaOnnxConfig,
	)

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta import XLMRobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMRobertaForCausalLM,
		    XLMRobertaForMaskedLM,
		    XLMRobertaForMultipleChoice,
		    XLMRobertaForQuestionAnswering,
		    XLMRobertaForSequenceClassification,
		    XLMRobertaForTokenClassification,
		    XLMRobertaModel,
		    XLMRobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm_roberta import (
		    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMRobertaForCausalLM,
		    TFXLMRobertaForMaskedLM,
		    TFXLMRobertaForMultipleChoice,
		    TFXLMRobertaForQuestionAnswering,
		    TFXLMRobertaForSequenceClassification,
		    TFXLMRobertaForTokenClassification,
		    TFXLMRobertaModel,
		    TFXLMRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xlm_roberta import (
		    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxXLMRobertaForCausalLM,
		    FlaxXLMRobertaForMaskedLM,
		    FlaxXLMRobertaForMultipleChoice,
		    FlaxXLMRobertaForQuestionAnswering,
		    FlaxXLMRobertaForSequenceClassification,
		    FlaxXLMRobertaForTokenClassification,
		    FlaxXLMRobertaModel,
		    FlaxXLMRobertaPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase							 =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

