



'''simple docstring'''



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class _UpperCamelCase		(     tf.keras.layers.Layer		):





				'''simple docstring'''





				def __init__(							self  :  int  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Tuple = None  ,       _lowerCAmelCase  :  str = None):







											'''simple docstring'''

											super().__init__()
											__lowercase       	=pad_token_id
											__lowercase       	=max_length
											__lowercase       	=vocab
											__lowercase       	=merges
											__lowercase       	=BytePairTokenizer(UpperCamelCase__  ,       UpperCamelCase__  ,       sequence_length=UpperCamelCase__)

				@classmethod
				def 				__lowerCamelCase     (							cls  :  Optional[Any]  ,       _lowerCAmelCase  :  Tuple  ,       *_lowerCAmelCase  :  Union[str, Any]  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=[' '.join(UpperCamelCase__) for m in tokenizer.bpe_ranks.keys()]
											__lowercase       	=tokenizer.get_vocab()
											return cls(UpperCamelCase__  ,       UpperCamelCase__  ,       *UpperCamelCase__  ,       **UpperCamelCase__)

				@classmethod
				def 				__lowerCamelCase     (							cls  :  int  ,       _lowerCAmelCase  :  Optional[Any]  ,       *_lowerCAmelCase  :  Optional[int]  ,       **_lowerCAmelCase  :  Any):







											'''simple docstring'''

											__lowercase       	=GPTaTokenizer.from_pretrained(UpperCamelCase__  ,       *UpperCamelCase__  ,       **UpperCamelCase__)
											return cls.from_tokenizer(UpperCamelCase__  ,       *UpperCamelCase__  ,       **UpperCamelCase__)

				@classmethod
				def 				__lowerCamelCase     (							cls  :  Tuple  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return cls(**UpperCamelCase__)

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											return {
											    "vocab": self.vocab,
											    "merges": self.merges,
											    "max_length": self.max_length,
											    "pad_token_id": self.pad_token_id,
											}

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  List[str] = None):







											'''simple docstring'''

											__lowercase       	=self.tf_tokenizer(UpperCamelCase__)
											__lowercase       	=tf.ones_like(UpperCamelCase__)

											if self.pad_token_id is not None:
																		# pad the tokens up to max length
																		__lowercase       	=max_length if max_length is not None else self.max_length

																		if max_length is not None:
																									__lowercase	,				__lowercase       	=pad_model_inputs(
																									    UpperCamelCase__  ,       max_seq_length=UpperCamelCase__  ,       pad_value=self.pad_token_id)

											return {"attention_mask": attention_mask, "input_ids": input_ids}








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase	 =       {
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       ["""Mask2FormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       [
		    """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Mask2FormerForUniversalSegmentation""",
		    """Mask2FormerModel""",
		    """Mask2FormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_maskaformer import MaskaFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_maskaformer import (
				    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaskaFormerForUniversalSegmentation,
				    MaskaFormerModel,
				    MaskaFormerPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)



