


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,
)


__snake_case      = {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

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

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

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

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

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__snake_case      = [
			    "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

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







'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
