





import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__						:			List[str]									={
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase__						:			Dict									=logging.get_logger(__name__)

class       __A     (						a    ):
				__A      =				"""mask2former"""
				__A      =				["""swin"""]
				__A      =				{"""hidden_size""": """hidden_dim"""}


				def __init__(		self   , UpperCAmelCase_ = None   , UpperCAmelCase_ = 256   , UpperCAmelCase_ = 256   , UpperCAmelCase_ = 256   , UpperCAmelCase_ = 1024   , UpperCAmelCase_ = "relu"   , UpperCAmelCase_ = 6   , UpperCAmelCase_ = 10   , UpperCAmelCase_ = 8   , UpperCAmelCase_ = 0.0   , UpperCAmelCase_ = 2048   , UpperCAmelCase_ = False   , UpperCAmelCase_ = False   , UpperCAmelCase_ = 4   , UpperCAmelCase_ = 255   , UpperCAmelCase_ = 100   , UpperCAmelCase_ = 0.1   , UpperCAmelCase_ = 2.0   , UpperCAmelCase_ = 5.0   , UpperCAmelCase_ = 5.0   , UpperCAmelCase_ = 12544   , UpperCAmelCase_ = 3.0   , UpperCAmelCase_ = 0.7_5   , UpperCAmelCase_ = 0.0_2   , UpperCAmelCase_ = 1.0   , UpperCAmelCase_ = True   , UpperCAmelCase_ = [4, 8, 16, 32]   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								if backbone_config is None:
												logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""							)
												lowerCamelCase	 =CONFIG_MAPPING["""swin"""](
												    image_size=224   , in_channels=3   , patch_size=4   , embed_dim=96   , depths=[2, 2, 18, 2]   , num_heads=[3, 6, 12, 24]   , window_size=7   , drop_path_rate=0.3   , use_absolute_embeddings=UpperCAmelCase_   , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   , )

								if isinstance(UpperCAmelCase_   , UpperCAmelCase_							):
												lowerCamelCase	 =backbone_config.pop("""model_type"""							)
												lowerCamelCase	 =CONFIG_MAPPING[backbone_model_type]
												lowerCamelCase	 =config_class.from_dict(UpperCAmelCase_							)

								# verify that the backbone is supported
								if backbone_config.model_type not in self.backbones_supported:
												logger.warning_once(
												    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
												    f"""Supported model types: {",".join(self.backbones_supported							)}"""							)

								lowerCamelCase	 =backbone_config
								lowerCamelCase	 =feature_size
								lowerCamelCase	 =mask_feature_size
								lowerCamelCase	 =hidden_dim
								lowerCamelCase	 =encoder_feedforward_dim
								lowerCamelCase	 =activation_function
								lowerCamelCase	 =encoder_layers
								lowerCamelCase	 =decoder_layers
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =dropout
								lowerCamelCase	 =dim_feedforward
								lowerCamelCase	 =pre_norm
								lowerCamelCase	 =enforce_input_projection
								lowerCamelCase	 =common_stride
								lowerCamelCase	 =ignore_value
								lowerCamelCase	 =num_queries
								lowerCamelCase	 =no_object_weight
								lowerCamelCase	 =class_weight
								lowerCamelCase	 =mask_weight
								lowerCamelCase	 =dice_weight
								lowerCamelCase	 =train_num_points
								lowerCamelCase	 =oversample_ratio
								lowerCamelCase	 =importance_sample_ratio
								lowerCamelCase	 =init_std
								lowerCamelCase	 =init_xavier_std
								lowerCamelCase	 =use_auxiliary_loss
								lowerCamelCase	 =feature_strides
								lowerCamelCase	 =output_auxiliary_logits
								lowerCamelCase	 =decoder_layers

								super().__init__(**UpperCAmelCase_							)


				@classmethod
				def _snake_case       (		cls   , UpperCAmelCase_   , **UpperCAmelCase_							):
								return cls(
								    backbone_config=UpperCAmelCase_   , **UpperCAmelCase_   , )


				def _snake_case       (		self							):
								lowerCamelCase	 =copy.deepcopy(self.__dict__							)
								lowerCamelCase	 =self.backbone_config.to_dict()
								lowerCamelCase	 =self.__class__.model_type
								return output












import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
							if is_torch_available():
														import torch
							if is_tf_available():
														import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase__						:			Optional[Any]									=logging.get_logger(__name__)

UpperCAmelCase__						:			Union[str, Any]									={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__						:			str									={
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__						:			Union[str, Any]									={
    '''Salesforce/codegen-350M-mono''': 20_48,
}

class       __A     (						a    ):
				__A      =				VOCAB_FILES_NAMES
				__A      =				PRETRAINED_VOCAB_FILES_MAP
				__A      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A      =				["""input_ids""", """attention_mask"""]
				__A      =				CodeGenTokenizer


				def __init__(		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_="<|endoftext|>"   , UpperCAmelCase_="<|endoftext|>"   , UpperCAmelCase_="<|endoftext|>"   , UpperCAmelCase_=False   , **UpperCAmelCase_   , ):
								super().__init__(
								    UpperCAmelCase_   , UpperCAmelCase_   , tokenizer_file=UpperCAmelCase_   , unk_token=UpperCAmelCase_   , bos_token=UpperCAmelCase_   , eos_token=UpperCAmelCase_   , add_prefix_space=UpperCAmelCase_   , **UpperCAmelCase_   , )

								if kwargs.pop("""add_bos_token"""   , UpperCAmelCase_							):
												lowerCamelCase	 =kwargs.pop("""name_or_path"""   , """"""							)
												raise ValueError(
												    """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
												    """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
												    f"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
												    f"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
												    """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
												    """ so that the fast tokenizer works correctly."""							)

								lowerCamelCase	 =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
								if pre_tok_state.get("""add_prefix_space"""   , UpperCAmelCase_							) != add_prefix_space:
												lowerCamelCase	 =getattr(UpperCAmelCase_   , pre_tok_state.pop("""type"""							)							)
												lowerCamelCase	 =add_prefix_space
												lowerCamelCase	 =pre_tok_class(**UpperCAmelCase_							)

								lowerCamelCase	 =add_prefix_space


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								lowerCamelCase	 =kwargs.get("""is_split_into_words"""   , UpperCAmelCase_							)
								assert self.add_prefix_space or not is_split_into_words, (
								    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								lowerCamelCase	 =kwargs.get("""is_split_into_words"""   , UpperCAmelCase_							)

								assert self.add_prefix_space or not is_split_into_words, (
								    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None							):
								lowerCamelCase	 =self._tokenizer.model.save(UpperCAmelCase_   , name=UpperCAmelCase_							)
								return tuple(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = False   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =super().decode(
								    token_ids=UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_   , clean_up_tokenization_spaces=UpperCAmelCase_   , **UpperCAmelCase_   , )

								if truncate_before_pattern is not None and len(UpperCAmelCase_							) > 0:
												lowerCamelCase	 =self.truncate(UpperCAmelCase_   , UpperCAmelCase_							)

								return decoded_text


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								def find_re(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
												lowerCamelCase	 =pattern.search(UpperCAmelCase_   , UpperCAmelCase_							)
												return m.start() if m else -1

								lowerCamelCase	 =[re.compile(UpperCAmelCase_   , re.MULTILINE							) for pattern in truncate_before_pattern]

								lowerCamelCase	 =list(re.finditer("""^print"""   , UpperCAmelCase_   , re.MULTILINE							)							)

								if len(UpperCAmelCase_							) > 1:
												lowerCamelCase	 =completion[: prints[1].start()]

								lowerCamelCase	 =list(re.finditer("""^def"""   , UpperCAmelCase_   , re.MULTILINE							)							)

								if len(UpperCAmelCase_							) > 1:
												lowerCamelCase	 =completion[: defs[1].start()]

								lowerCamelCase	 =0

								lowerCamelCase	 =[
								    pos for pos in [find_re(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							) for terminal in terminals] if pos != -1
								]

								if len(UpperCAmelCase_							) > 0:
												return completion[: min(UpperCAmelCase_							)]
								else:
												return completion





