





import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase							:Optional[int]							=	logging.get_logger(__name__)







def    UpperCAmelCase				(  _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       tf.image.resize(_SCREAMING_SNAKE_CASE      , (346, 346)  )
						SCREAMING_SNAKE_CASE__  :					Any =       tf.image.crop_to_bounding_box(_SCREAMING_SNAKE_CASE      , (346 - 289) // 2      , (346 - 289) // 2      , 289      , 289  )
						return image







def    UpperCAmelCase				(  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       EfficientNetConfig.from_pretrained("google/efficientnet-b7"  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       289
						SCREAMING_SNAKE_CASE__  :					Any =       640
						SCREAMING_SNAKE_CASE__  :					Dict =       {"0": "LABEL_0", "1": "LABEL_1"}
						SCREAMING_SNAKE_CASE__  :					Dict =       {"LABEL_0": 0, "LABEL_1": 1}
						SCREAMING_SNAKE_CASE__  :					str =       []

						SCREAMING_SNAKE_CASE__  :					Dict =       BertConfig()
						SCREAMING_SNAKE_CASE__  :					Any =       AlignConfig.from_text_vision_configs(
						    text_config=_SCREAMING_SNAKE_CASE      , vision_config=_SCREAMING_SNAKE_CASE      , projection_dim=640  )
						return config







def    UpperCAmelCase				(  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       "http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE__  :					Tuple =       Image.open(requests.get(_SCREAMING_SNAKE_CASE      , stream=_SCREAMING_SNAKE_CASE  ).raw  )
						return im







def    UpperCAmelCase				(  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       EfficientNetImageProcessor(
						    do_center_crop=_SCREAMING_SNAKE_CASE      , rescale_factor=1 / 1_2_7.5      , rescale_offset=_SCREAMING_SNAKE_CASE      , do_normalize=_SCREAMING_SNAKE_CASE      , include_top=_SCREAMING_SNAKE_CASE      , resample=Image.BILINEAR      , )
						SCREAMING_SNAKE_CASE__  :					str =       BertTokenizer.from_pretrained("bert-base-uncased"  )
						SCREAMING_SNAKE_CASE__  :					str =       64
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       AlignProcessor(image_processor=_SCREAMING_SNAKE_CASE      , tokenizer=_SCREAMING_SNAKE_CASE  )
						return processor







def    UpperCAmelCase				(  _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       [v.split("_"  )[0].split("block"  )[1] for v in original_param_names if v.startswith("block"  )]
						SCREAMING_SNAKE_CASE__  :					Tuple =       list(set(_SCREAMING_SNAKE_CASE  )  )
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       sorted(_SCREAMING_SNAKE_CASE  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       len(_SCREAMING_SNAKE_CASE  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       {b: str(_SCREAMING_SNAKE_CASE  ) for b, i in zip(_SCREAMING_SNAKE_CASE      , range(_SCREAMING_SNAKE_CASE  )  )}

						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       []
						rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")  )
						rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")  )
						rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")  )
						rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")  )
						rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")  )

						for b in block_names:
												SCREAMING_SNAKE_CASE__  :					List[str] =       block_name_mapping[b]
												rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")  )
												rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")  )
												rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")  )
												rename_keys.append(
												    (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")  )
												rename_keys.append(
												    (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")  )
												rename_keys.append(
												    (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")  )
												rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")  )
												rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")  )
												rename_keys.append(
												    (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")  )
												rename_keys.append(
												    (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")  )

												rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")  )
												rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")  )
												rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")  )
												rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")  )
												rename_keys.append(
												    (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")  )
												rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")  )
												rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")  )
												rename_keys.append(
												    (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")  )
												rename_keys.append(
												    (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")  )

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		SCREAMING_SNAKE_CASE__  :					Any =       "vision_model." + item[1]

																		 # BERT text encoder
						SCREAMING_SNAKE_CASE__  :					Any =       []
						SCREAMING_SNAKE_CASE__  :					Dict =       "tf_bert_model/bert"
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       "text_model"
						for i in range(12  ):
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.query.weight""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.query.bias""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.key.weight""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.key.bias""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.value.weight""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.value.bias""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
												        f"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
												    )  )
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
												        f"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
												    )  )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", f"""{new}.encoder.layer.{i}.output.dense.weight""")  )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/dense/bias:0""", f"""{new}.encoder.layer.{i}.output.dense.bias""")  )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")  )
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")  )

						rename_keys.append((f"""{old}/embeddings/word_embeddings/weight:0""", f"""{new}.embeddings.word_embeddings.weight""")  )
						rename_keys.append(
						    (f"""{old}/embeddings/position_embeddings/embeddings:0""", f"""{new}.embeddings.position_embeddings.weight""")  )
						rename_keys.append(
						    (f"""{old}/embeddings/token_type_embeddings/embeddings:0""", f"""{new}.embeddings.token_type_embeddings.weight""")  )
						rename_keys.append((f"""{old}/embeddings/LayerNorm/gamma:0""", f"""{new}.embeddings.LayerNorm.weight""")  )
						rename_keys.append((f"""{old}/embeddings/LayerNorm/beta:0""", f"""{new}.embeddings.LayerNorm.bias""")  )

						rename_keys.append((f"""{old}/pooler/dense/kernel:0""", f"""{new}.pooler.dense.weight""")  )
						rename_keys.append((f"""{old}/pooler/dense/bias:0""", f"""{new}.pooler.dense.bias""")  )
						rename_keys.append(("dense/kernel:0", "text_projection.weight")  )
						rename_keys.append(("dense/bias:0", "text_projection.bias")  )
						rename_keys.append(("dense/bias:0", "text_projection.bias")  )
						rename_keys.append(("temperature:0", "temperature")  )

						for item in rename_keys:
												if item[0] in original_param_names:
																		SCREAMING_SNAKE_CASE__  :					List[str] =       item[1]
						return key_mapping







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						list(hf_params.keys()  )

						for key, value in tf_params.items():
												if key not in key_mapping:
																		continue

												SCREAMING_SNAKE_CASE__  :					Dict =       key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		SCREAMING_SNAKE_CASE__  :					int =       torch.from_numpy(_SCREAMING_SNAKE_CASE  ).permute(3      , 2      , 0      , 1  )
												elif "embeddings" in key:
																		SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.from_numpy(_SCREAMING_SNAKE_CASE  )
												elif "depthwise_kernel" in key:
																		SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.from_numpy(_SCREAMING_SNAKE_CASE  ).permute(2      , 3      , 0      , 1  )
												elif "kernel" in key:
																		SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE  )  )
												elif "temperature" in key:
																		SCREAMING_SNAKE_CASE__  :					int =       value
												elif "bn/gamma" or "bn/beta" in key:
																		SCREAMING_SNAKE_CASE__  :					str =       torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE  )  ).squeeze()
												else:
																		SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.from_numpy(_SCREAMING_SNAKE_CASE  )

												# Replace HF parameters with original TF model parameters
												hf_params[hf_key].copy_(_SCREAMING_SNAKE_CASE  )







@torch.no_grad()
def    UpperCAmelCase				(  _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       64
						SCREAMING_SNAKE_CASE__  :					Dict =       Tokenizer(_SCREAMING_SNAKE_CASE  )
						SCREAMING_SNAKE_CASE__  :					str =       align.Align("efficientnet-b7"      , "bert-base"      , 640      , _SCREAMING_SNAKE_CASE      , tok.get_vocab_size()  )
						original_model.compile()
						original_model.load_weights(_SCREAMING_SNAKE_CASE  )

						SCREAMING_SNAKE_CASE__  :					Any =       original_model.trainable_variables
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       original_model.non_trainable_variables
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												SCREAMING_SNAKE_CASE__  :					Any =       param.numpy()
						SCREAMING_SNAKE_CASE__  :					int =       list(tf_params.keys()  )

						# Load HuggingFace model
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_align_config()
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       AlignModel(_SCREAMING_SNAKE_CASE  ).eval()
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print("Converting parameters..."  )
						SCREAMING_SNAKE_CASE__  :					int =       rename_keys(_SCREAMING_SNAKE_CASE  )
						replace_params(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE  )

						# Initialize processor
						SCREAMING_SNAKE_CASE__  :					Any =       get_processor()
						SCREAMING_SNAKE_CASE__  :					List[Any] =       processor(
						    images=prepare_img()      , text="A picture of a cat"      , padding="max_length"      , max_length=64      , return_tensors="pt"  )

						# HF model inference
						hf_model.eval()
						with torch.no_grad():
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       hf_model(**_SCREAMING_SNAKE_CASE  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       outputs.image_embeds.detach().numpy()
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       outputs.text_embeds.detach().numpy()

						# Original model inference
						SCREAMING_SNAKE_CASE__  :					str =       False
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       EfficientNetImageProcessor(
						    do_center_crop=_SCREAMING_SNAKE_CASE      , do_rescale=_SCREAMING_SNAKE_CASE      , do_normalize=_SCREAMING_SNAKE_CASE      , include_top=_SCREAMING_SNAKE_CASE      , resample=Image.BILINEAR      , )
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       tf_image_processor(images=prepare_img()      , return_tensors="tf"      , data_format="channels_last"  )["pixel_values"]
						SCREAMING_SNAKE_CASE__  :					int =       tok(tf.constant(["A picture of a cat"]  )  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       original_model.image_encoder(_SCREAMING_SNAKE_CASE      , training=_SCREAMING_SNAKE_CASE  )
						SCREAMING_SNAKE_CASE__  :					int =       original_model.text_encoder(_SCREAMING_SNAKE_CASE      , training=_SCREAMING_SNAKE_CASE  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       tf.nn.la_normalize(_SCREAMING_SNAKE_CASE      , axis=-1  )
						SCREAMING_SNAKE_CASE__  :					int =       tf.nn.la_normalize(_SCREAMING_SNAKE_CASE      , axis=-1  )

						# Check whether original and HF model outputs match  -> np.allclose
						if not np.allclose(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=1E-3  ):
												raise ValueError("The predicted image features are not the same."  )
						if not np.allclose(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=1E-3  ):
												raise ValueError("The predicted text features are not the same."  )
						print("Model outputs match!"  )

						if save_model:
												# Create folder to save model
												if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
																		os.mkdir(_SCREAMING_SNAKE_CASE  )
												# Save converted model and image processor
												hf_model.save_pretrained(_SCREAMING_SNAKE_CASE  )
												processor.save_pretrained(_SCREAMING_SNAKE_CASE  )

						if push_to_hub:
												# Push model and image processor to hub
												print("Pushing converted ALIGN to the hub..."  )
												processor.push_to_hub("align-base"  )
												hf_model.push_to_hub("align-base"  )


if __name__ == "__main__":
				__lowercase							:Any							=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_path",
				    default="./weights/model-weights",
				    type=str,
				    help="Path to the pretrained TF ALIGN checkpoint.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default="hf_model",
				    type=str,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument("--save_model", action="store_true", help="Save model to local")
				parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

				__lowercase							:List[str]							=	parser.parse_args()
				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


a  :						List[Any]					       =							logging.get_logger(__name__)

a  :						List[Any]					       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a  :						Any					       =							{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

a  :						str					       =							{
    '''microsoft/mpnet-base''': 512,
}

a  :						Union[str, Any]					       =							{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						VOCAB_FILES_NAMES
    __lowercase    :   Optional[int]											=						PRETRAINED_VOCAB_FILES_MAP
    __lowercase    :   Dict											=						PRETRAINED_INIT_CONFIGURATION
    __lowercase    :   Optional[Any]											=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase    :   Dict											=						MPNetTokenizer
    __lowercase    :   Dict											=						['input_ids', 'attention_mask']


    def __init__(      self   ,			__lowercase=None   ,			__lowercase=None   ,			__lowercase=True   ,			__lowercase="<s>"   ,			__lowercase="</s>"   ,			__lowercase="</s>"   ,			__lowercase="<s>"   ,			__lowercase="[UNK]"   ,			__lowercase="<pad>"   ,			__lowercase="<mask>"   ,			__lowercase=True   ,			__lowercase=None   ,			**__lowercase   ,			):
     super().__init__(
         __lowercase   ,			tokenizer_file=__lowercase   ,			do_lower_case=__lowercase   ,			bos_token=__lowercase   ,			eos_token=__lowercase   ,			sep_token=__lowercase   ,			cls_token=__lowercase   ,			unk_token=__lowercase   ,			pad_token=__lowercase   ,			mask_token=__lowercase   ,			tokenize_chinese_chars=__lowercase   ,			strip_accents=__lowercase   ,			**__lowercase   ,			)

     UpperCAmelCase__ 						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("""lowercase"""   ,			__lowercase			) != do_lower_case
         or pre_tok_state.get("""strip_accents"""   ,			__lowercase			) != strip_accents
     ):
      UpperCAmelCase__ 						=				getattr(__lowercase   ,			pre_tok_state.pop("""type"""			)			)
      UpperCAmelCase__ 						=				do_lower_case
      UpperCAmelCase__ 						=				strip_accents
      UpperCAmelCase__ 						=				pre_tok_class(**__lowercase			)

     UpperCAmelCase__ 						=				do_lower_case


    @property
    def 		A__      (      self			):
     if self._mask_token is None:
      if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""			)
      return None
     return str(self._mask_token			)


    @mask_token.setter
    def 		A__      (      self   ,			__lowercase			):
     UpperCAmelCase__ 						=				AddedToken(__lowercase   ,			lstrip=__lowercase   ,			rstrip=__lowercase			) if isinstance(__lowercase   ,			__lowercase			) else value
     UpperCAmelCase__ 						=				value


    def 		A__      (      self   ,			__lowercase   ,			__lowercase=None			):
     UpperCAmelCase__ 						=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				[self.sep_token_id]
     UpperCAmelCase__ 						=				[self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep			) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				self._tokenizer.model.save(__lowercase   ,			name=__lowercase			)
     return tuple(__lowercase			)



