





"""simple docstring"""





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_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   ['MBartTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   ['MBartTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   [
							    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MBartForCausalLM',
							    'MBartForConditionalGeneration',
							    'MBartForQuestionAnswering',
							    'MBartForSequenceClassification',
							    'MBartModel',
							    'MBartPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   [
							    'TFMBartForConditionalGeneration',
							    'TFMBartModel',
							    'TFMBartPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   [
							    'FlaxMBartForConditionalGeneration',
							    'FlaxMBartForQuestionAnswering',
							    'FlaxMBartForSequenceClassification',
							    'FlaxMBartModel',
							    'FlaxMBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

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











"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be trained."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""./"""							,	metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path of training dataset."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size for training."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size for evaluation."""} )
				__snake_case :							Optional[float]									=		field(default=0.1							,	metadata={"""help""": """Value of weight decay."""} )
				__snake_case :							Optional[int]									=		field(
				    default=1_00_00							,	metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""} )
				__snake_case :							Optional[float]									=		field(default=2e-4							,	metadata={"""help""": """Learning rate fo training."""} )
				__snake_case :							Optional[str]									=		field(default="""cosine"""							,	metadata={"""help""": """Learning rate."""} )
				__snake_case :							Optional[int]									=		field(
				    default=7_50							,	metadata={"""help""": """Number of warmup steps in the learning rate schedule."""} )
				__snake_case :							Optional[int]									=		field(
				    default=16							,	metadata={"""help""": """Number of gradient accumulation steps."""} )
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""} )
				__snake_case :							Optional[int]									=		field(default=5_00_00							,	metadata={"""help""": """Maximum number of training steps."""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=10_24							,	metadata={"""help""": """Sequence lengths used for training."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Training seed."""} )
				__snake_case :							Optional[int]									=		field(
				    default=10_24							,	metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """States path if the training should continue from a checkpoint folder."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """If True the data is pretokenized."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size used for evaluation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=10_24							,	metadata={"""help""": """Length of sequences to be evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=snake_case__							,	metadata={"""help""": """Number of workers used for code evaluation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,	)
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Sample from the language model's output distribution."""} )
				__snake_case :							Optional[float]									=		field(default=0.2							,	metadata={"""help""": """Sampling temperature used for generation."""} )
				__snake_case :							Optional[int]									=		field(default=2_56							,	metadata={"""help""": """Maximum number of newly generated tokens."""} )
				__snake_case :							Optional[int]									=		field(default=0							,	metadata={"""help""": """Top-k parameter used for generation."""} )
				__snake_case :							Optional[float]									=		field(default=0.95							,	metadata={"""help""": """Top-p parameter used for nucleus sampling."""} )
				__snake_case :							Optional[int]									=		field(default=10							,	metadata={"""help""": """Number of generations to run in parallel."""} )
				__snake_case :							Optional[int]									=		field(
				    default=2_00							,	metadata={"""help""": """Number of completions to generate for each sample."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""eval_results.json"""							,	metadata={"""help""": """Random seed used for evaluation."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""0"""							,	metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={
				        """help""": (
				            """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
				            """ number corresponds to which GPU device id to run on."""
				        )
				    }							,	)

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={
				        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
				    }							,	)
				__snake_case :							Optional[str]									=		field(
				    default="""transformersbook/codeparrot"""							,	metadata={"""help""": """Folder or name of dataset to process."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot-clean"""							,	metadata={"""help""": """Folder to save processed processed dataset."""} )
				__snake_case :							Optional[int]									=		field(
				    default=10_00_00							,	metadata={"""help""": """Number of files to save per JSON output file."""} )
				__snake_case :							Optional[str]									=		field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""} )
				__snake_case :							Optional[float]									=		field(
				    default=10_00							,	metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=1_00							,	metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.25							,	metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=1.5							,	metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.7							,	metadata={"""help""": """Probability for filtering config, test and uncommon files."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""}							,	)
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """If True, near-duplicate samples are removed."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.85							,	metadata={"""help""": """Jaccard threshold for near-duplicate samples."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""gpt2"""							,	metadata={"""help""": """Base tokenizer to build new tokenizer from."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""transformersbook/codeparrot-train"""							,	metadata={"""help""": """Dataset to train tokenizer on."""} )
				__snake_case :							Optional[str]									=		field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""} )
				__snake_case :							Optional[int]									=		field(default=20_00_00							,	metadata={"""help""": """Number of examples to train tokenizer on."""} )
				__snake_case :							Optional[int]									=		field(
				    default=3_27_68							,	metadata={"""help""": """Number of examples to train the tokenizer on."""} )
				__snake_case :							Optional[str]									=		field(default="""codeparrot"""							,	metadata={"""help""": """Name of new tokenizer."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """Push saved tokenizer to the hub."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path to the dataset to pretokenize."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""tokenized-codeparrot-train"""							,	metadata={"""help""": """Repo name of the pretokenized data."""} )
				__snake_case :							Optional[int]									=		field(default=snake_case__							,	metadata={"""help""": """Number of workers used for code evaluation."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""gpt2-large"""							,	metadata={"""help""": """Configuration to use for model initialization."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Tokenizer attached to model."""} )
				__snake_case :							Optional[str]									=		field(default="""codeparrot"""							,	metadata={"""help""": """Name of the created model."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """Push saved tokenizer to the hub."""} )




