
"""simple docstring"""






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()
__UpperCAmelCase 					=    logging.get_logger(__name__)






def 		_snake_case			(      lowercase__						:    Optional[Any]						)    ->							str:


			'''simple docstring'''





			lowerCAmelCase_       :Dict     	=    tf.image.resize(UpperCamelCase__ ,	(3_4_6, 3_4_6)						)
			lowerCAmelCase_       :Union[str, Any]     	=    tf.image.crop_to_bounding_box(UpperCamelCase__ ,	(3_4_6 - 2_8_9) // 2 ,	(3_4_6 - 2_8_9) // 2 ,	2_8_9 ,	2_8_9						)
			return image






def 		_snake_case			(      )    ->							Any:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""						)
			lowerCAmelCase_       :Any     	=    2_8_9
			lowerCAmelCase_       :Optional[Any]     	=    6_4_0
			lowerCAmelCase_       :Tuple     	=    {'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
			lowerCAmelCase_       :Optional[int]     	=    {'''LABEL_0''': 0, '''LABEL_1''': 1}
			lowerCAmelCase_       :Union[str, Any]     	=    []

			lowerCAmelCase_       :Dict     	=    BertConfig()
			lowerCAmelCase_       :Any     	=    AlignConfig.from_text_vision_configs(
			    text_config=UpperCamelCase__ ,	vision_config=UpperCamelCase__ ,	projection_dim=6_4_0						)
			return config






def 		_snake_case			(      )    ->							str:


			'''simple docstring'''





			lowerCAmelCase_       :Union[str, Any]     	=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCAmelCase_       :List[str]     	=    Image.open(requests.get(UpperCamelCase__ ,	stream=UpperCamelCase__						).raw						)
			return im






def 		_snake_case			(      )    ->							List[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Union[str, Any]     	=    EfficientNetImageProcessor(
			    do_center_crop=UpperCamelCase__ ,	rescale_factor=1 / 1_2_7.5 ,	rescale_offset=UpperCamelCase__ ,	do_normalize=UpperCamelCase__ ,	include_top=UpperCamelCase__ ,	resample=Image.BILINEAR ,	)
			lowerCAmelCase_       :Dict     	=    BertTokenizer.from_pretrained("""bert-base-uncased"""						)
			lowerCAmelCase_       :int     	=    6_4
			lowerCAmelCase_       :Union[str, Any]     	=    AlignProcessor(image_processor=UpperCamelCase__ ,	tokenizer=UpperCamelCase__						)
			return processor






def 		_snake_case			(      lowercase__						:    Optional[Any]						)    ->							List[Any]:


			'''simple docstring'''





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

			lowerCAmelCase_       :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:
						lowerCAmelCase_       :Dict     	=    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""")						)

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

    # BERT text encoder
			lowerCAmelCase_       :List[str]     	=    []
			lowerCAmelCase_       :Optional[Any]     	=    '''tf_bert_model/bert'''
			lowerCAmelCase_       :List[str]     	=    '''text_model'''
			for i in range(1_2						):
						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:
									lowerCAmelCase_       :Dict     	=    item[1]
			return key_mapping






def 		_snake_case			(      lowercase__						:    List[str] ,	lowercase__						:    Tuple ,	lowercase__						:    Dict						)    ->							str:


			'''simple docstring'''





			list(hf_params.keys()						)

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

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

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






@torch.no_grad()
def 		_snake_case			(      lowercase__						:    Tuple ,	lowercase__						:    List[str] ,	lowercase__						:    Dict ,	lowercase__						:    Union[str, Any]						)    ->							Dict:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    6_4
			lowerCAmelCase_       :int     	=    Tokenizer(UpperCamelCase__						)
			lowerCAmelCase_       :Dict     	=    align.Align("""efficientnet-b7""" ,	"""bert-base""" ,	6_4_0 ,	UpperCamelCase__ ,	tok.get_vocab_size()						)
			original_model.compile()
			original_model.load_weights(UpperCamelCase__						)

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

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

			# Create src-to-dst parameter name mapping dictionary
			print("""Converting parameters..."""						)
			lowerCAmelCase_       :Optional[int]     	=    rename_keys(UpperCamelCase__						)
			replace_params(UpperCamelCase__ ,	UpperCamelCase__ ,	UpperCamelCase__						)

			# Initialize processor
			lowerCAmelCase_       :str     	=    get_processor()
			lowerCAmelCase_       :int     	=    processor(
			    images=prepare_img() ,	text="""A picture of a cat""" ,	padding="""max_length""" ,	max_length=6_4 ,	return_tensors="""pt"""						)

			# HF model inference
			hf_model.eval()
			with torch.no_grad():
						lowerCAmelCase_       :Any     	=    hf_model(**UpperCamelCase__						)

			lowerCAmelCase_       :Union[str, Any]     	=    outputs.image_embeds.detach().numpy()
			lowerCAmelCase_       :List[str]     	=    outputs.text_embeds.detach().numpy()

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

			lowerCAmelCase_       :Any     	=    original_model.image_encoder(UpperCamelCase__ ,	training=UpperCamelCase__						)
			lowerCAmelCase_       :Optional[Any]     	=    original_model.text_encoder(UpperCamelCase__ ,	training=UpperCamelCase__						)

			lowerCAmelCase_       :int     	=    tf.nn.la_normalize(UpperCamelCase__ ,	axis=-1						)
			lowerCAmelCase_       :List[str]     	=    tf.nn.la_normalize(UpperCamelCase__ ,	axis=-1						)

			# Check whether original and HF model outputs match  -> np.allclose
			if not np.allclose(UpperCamelCase__ ,	UpperCamelCase__ ,	atol=1E-3						):
						raise ValueError("""The predicted image features are not the same."""						)
			if not np.allclose(UpperCamelCase__ ,	UpperCamelCase__ ,	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(UpperCamelCase__						):
									os.mkdir(UpperCamelCase__						)
						# Save converted model and image processor
						hf_model.save_pretrained(UpperCamelCase__						)
						processor.save_pretrained(UpperCamelCase__						)

			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__":
				__UpperCAmelCase 					=    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')

				__UpperCAmelCase 					=    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
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_     			=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

snake_case_     			=						{
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							Tuple				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							List[Any]				=    bs[:]
    A							:							Tuple				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Optional[int]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			int		)		->	str:
    A							:							int				=    set()
    A							:							Any				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							Optional[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       A							:							Optional[int]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else mask_token

       super().__init__(
           errors=_UpperCAmelCase     , bos_token=_UpperCAmelCase     , eos_token=_UpperCAmelCase     , unk_token=_UpperCAmelCase     , sep_token=_UpperCAmelCase     , cls_token=_UpperCAmelCase     , pad_token=_UpperCAmelCase     , mask_token=_UpperCAmelCase     , add_prefix_space=_UpperCAmelCase     , **_UpperCAmelCase     , )

       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as vocab_handle:
           A							:							int				=    json.load(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    {v: k for k, v in self.encoder.items()}
       A							:							Tuple				=    errors  # how to handle errors in decoding
       A							:							List[Any]				=    bytes_to_unicode()
       A							:							List[Any]				=    {v: k for k, v in self.byte_encoder.items()}
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as merges_handle:
           A							:							Optional[int]				=    merges_handle.read().split('''\n'''       )[1:-1]
       A							:							int				=    [tuple(merge.split()       ) for merge in bpe_merges]
       A							:							Union[str, Any]				=    dict(zip(_UpperCAmelCase     , range(len(_UpperCAmelCase       )       )       )       )
       A							:							List[str]				=    {}
       A							:							List[Any]				=    add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       A							:							Any				=    re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )


   @property
   def        snake_case					(			self       ):
       return len(self.encoder       )


   def        snake_case					(			self       ):
       return dict(self.encoder     , **self.added_tokens_encoder       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Union[str, Any]				=    tuple(_UpperCAmelCase       )
       A							:							Tuple				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

       while True:
           A							:							Any				=    min(_UpperCAmelCase     , key=lambda _UpperCAmelCase       : self.bpe_ranks.get(_UpperCAmelCase     , float('''inf'''       )       )       )
           if bigram not in self.bpe_ranks:
               break
           A,						A							:							Optional[int]				=    bigram
           A							:							str				=    []
           A							:							List[Any]				=    0
           while i < len(_UpperCAmelCase       ):
               try:
                   A							:							Any				=    word.index(_UpperCAmelCase     , _UpperCAmelCase       )
               except ValueError:
                   new_word.extend(word[i:]       )
                   break
               else:
                   new_word.extend(word[i:j]       )
                   A							:							Tuple				=    j

               if word[i] == first and i < len(_UpperCAmelCase       ) - 1 and word[i + 1] == second:
                   new_word.append(first + second       )
                   i += 2
               else:
                   new_word.append(word[i]       )
                   i += 1
           A							:							Optional[Any]				=    tuple(_UpperCAmelCase       )
           A							:							Dict				=    new_word
           if len(_UpperCAmelCase       ) == 1:
               break
           else:
               A							:							int				=    get_pairs(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    ''' '''.join(_UpperCAmelCase       )
       A							:							Tuple				=    word
       return word


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Tuple				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Tuple				=    ''''''.join(
               self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase       ).split(''' '''       )       )
       return bpe_tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Any				=    ''''''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if not os.path.isdir(_UpperCAmelCase       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as f:
           f.write(json.dumps(self.encoder     , indent=2     , sort_keys=_UpperCAmelCase     , ensure_ascii=_UpperCAmelCase       ) + '''\n'''       )

       A							:							Tuple				=    0
       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as writer:
           writer.write('''#version: 0.2\n'''       )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     , key=lambda _UpperCAmelCase       : kv[1]       ):
               if index != token_index:
                   logger.warning(
                       f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                       ''' Please check that the tokenizer is not corrupted!'''       )
                   A							:							List[Any]				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Dict				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase     , token_ids_a=_UpperCAmelCase     , already_has_special_tokens=_UpperCAmelCase       )

       if token_ids_a is None:
           return [1] + ([0] * len(_UpperCAmelCase       )) + [1]
       return [1] + ([0] * len(_UpperCAmelCase       )) + [1, 1] + ([0] * len(_UpperCAmelCase       )) + [1]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Dict				=    [self.sep_token_id]
       A							:							Union[str, Any]				=    [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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							str				=    kwargs.pop('''add_prefix_space'''     , self.add_prefix_space       )
       if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase       ) > 0 and not text[0].isspace()):
           A							:							List[Any]				=    ''' ''' + text
       return (text, kwargs)
