



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Tuple									=  'mgp-str'



					def __init__(			self    ,	lowerCAmelCase=[32, 128]    ,	lowerCAmelCase=4    ,	lowerCAmelCase=3    ,	lowerCAmelCase=27    ,	lowerCAmelCase=38    ,	lowerCAmelCase=5_0257    ,	lowerCAmelCase=3_0522    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=4.0    ,	lowerCAmelCase=True    ,	lowerCAmelCase=False    ,	lowerCAmelCase=1e-5    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=False    ,	lowerCAmelCase=0.02    ,	**lowerCAmelCase    ,	):
							super().__init__(**lowerCAmelCase     )

							UpperCAmelCase_						=				image_size
							UpperCAmelCase_						=				patch_size
							UpperCAmelCase_						=				num_channels
							UpperCAmelCase_						=				max_token_length
							UpperCAmelCase_						=				num_character_labels
							UpperCAmelCase_						=				num_bpe_labels
							UpperCAmelCase_						=				num_wordpiece_labels
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				mlp_ratio
							UpperCAmelCase_						=				distilled
							UpperCAmelCase_						=				layer_norm_eps
							UpperCAmelCase_						=				drop_rate
							UpperCAmelCase_						=				qkv_bias
							UpperCAmelCase_						=				attn_drop_rate
							UpperCAmelCase_						=				drop_path_rate
							UpperCAmelCase_						=				output_aa_attentions
							UpperCAmelCase_						=				initializer_range






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE     				=						{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FNetForMaskedLM",
			    "FNetForMultipleChoice",
			    "FNetForNextSentencePrediction",
			    "FNetForPreTraining",
			    "FNetForQuestionAnswering",
			    "FNetForSequenceClassification",
			    "FNetForTokenClassification",
			    "FNetLayer",
			    "FNetModel",
			    "FNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

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

