




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				     = logging.get_logger(__name__)







def 		_A(       UpperCamelCase__							:			Dict				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    MobileNetVaConfig(layer_norm_eps=0.001				)

							if "_quant" in model_name:
														raise ValueError('''Quantized models are not supported.'''				)

							__lowercase      =    re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''				,       UpperCamelCase__				)
							if matches:
														__lowercase      =    float(matches[1]				)
														__lowercase      =    int(matches[2]				)

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							__lowercase      =    1001
							__lowercase      =    '''imagenet-1k-id2label.json'''
							__lowercase      =    '''huggingface/label-files'''
							__lowercase      =    json.load(open(hf_hub_download(UpperCamelCase__				,       UpperCamelCase__				,       repo_type='''dataset'''				)				,       '''r'''				)				)
							__lowercase      =    {int(UpperCamelCase__				) + 1: v for k, v in idalabel.items()}
							__lowercase      =    '''background'''
							__lowercase      =    idalabel
							__lowercase      =    {v: k for k, v in idalabel.items()}

							return config







def 		_A(       )    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowercase      =    Image.open(requests.get(UpperCamelCase__				,       stream=UpperCamelCase__				).raw				)
							return im







@torch.no_grad()
def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			str=False				)    -> int:
							'''simple docstring'''


							__lowercase      =    get_mobilenet_va_config(UpperCamelCase__				)

							# Load 🤗 model
							__lowercase      =    MobileNetVaForImageClassification(UpperCamelCase__				).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				)

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							__lowercase      =    MobileNetVaImageProcessor(
							    crop_size={'''width''': config.image_size, '''height''': config.image_size}				,       size={'''shortest_edge''': config.image_size + 32}				,       )
							__lowercase      =    image_processor(images=prepare_img()				,       return_tensors='''pt'''				)
							__lowercase      =    model(**UpperCamelCase__				)
							__lowercase      =    outputs.logits

							assert logits.shape == (1, 1001)

							if model_name == "mobilenet_v1_1.0_224":
														__lowercase      =    torch.tensor([-4.1739, -1.1233, 3.1205]				)
							elif model_name == "mobilenet_v1_0.75_192":
														__lowercase      =    torch.tensor([-3.9440, -2.3141, -0.3333]				)
							else:
														__lowercase      =    None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]				,       UpperCamelCase__				,       atol=1e-4				)

							Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
							print(F'Saving model {model_name} to {pytorch_dump_folder_path}'				)
							model.save_pretrained(UpperCamelCase__				)
							print(F'Saving image processor to {pytorch_dump_folder_path}'				)
							image_processor.save_pretrained(UpperCamelCase__				)

							if push_to_hub:
														print('''Pushing to the hub...'''				)
														__lowercase      =    '''google/''' + model_name
														image_processor.push_to_hub(UpperCamelCase__				)
														model.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="mobilenet_v1_1.0_224",
			    type=str,
			    help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
			)
			parser.add_argument(
			    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
			)
			parser.add_argument(
			    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
			)

			UpperCAmelCase__				     = parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)












import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			UpperCAmelCase__				     = None


UpperCAmelCase__				     = logging.get_logger(__name__)


UpperCAmelCase__				     = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				     = {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase__				     = {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
UpperCAmelCase__				     = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		VOCAB_FILES_NAMES
						UpperCamelCase_	:       List[str]       	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	:       int       	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	:       Optional[int]       	=		['input_ids', 'attention_mask']
						UpperCamelCase_	:       Optional[int]       	=		MBartTokenizer

						UpperCamelCase_	:       List[int]       	=		[]
						UpperCamelCase_	:       List[int]       	=		[]






						def __init__( self :     Tuple				,							lowerCamelCase__ :     str=None				,							lowerCamelCase__ :     Dict=None				,							lowerCamelCase__ :     Optional[Any]="<s>"				,							lowerCamelCase__ :     int="</s>"				,							lowerCamelCase__ :     int="</s>"				,							lowerCamelCase__ :     List[Any]="<s>"				,							lowerCamelCase__ :     Union[str, Any]="<unk>"				,							lowerCamelCase__ :     Union[str, Any]="<pad>"				,							lowerCamelCase__ :     List[Any]="<mask>"				,							lowerCamelCase__ :     Optional[Any]=None				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     Dict=None				,							**lowerCamelCase__ :     List[str]				,							)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    AddedToken(lowerCamelCase__				,							lstrip=lowerCamelCase__				,							rstrip=lowerCamelCase__				) if isinstance(lowerCamelCase__				,							lowerCamelCase__				) else mask_token

													super().__init__(
													    vocab_file=lowerCamelCase__				,							tokenizer_file=lowerCamelCase__				,							bos_token=lowerCamelCase__				,							eos_token=lowerCamelCase__				,							sep_token=lowerCamelCase__				,							cls_token=lowerCamelCase__				,							unk_token=lowerCamelCase__				,							pad_token=lowerCamelCase__				,							mask_token=lowerCamelCase__				,							src_lang=lowerCamelCase__				,							tgt_lang=lowerCamelCase__				,							additional_special_tokens=lowerCamelCase__				,							**lowerCamelCase__				,							)

													__lowercase      =    vocab_file
													__lowercase      =    False if not self.vocab_file else True

													__lowercase      =    FAIRSEQ_LANGUAGE_CODES.copy()

													if additional_special_tokens is not None:
																				# Only add those special tokens if they are not already there.
																				_additional_special_tokens.extend(
																				    [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

													self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}				)
													__lowercase      =    {
													    lang_code: self.convert_tokens_to_ids(lowerCamelCase__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
													}

													__lowercase      =    src_lang if src_lang is not None else '''en_XX'''
													__lowercase      =    self.convert_tokens_to_ids(self._src_lang				)
													__lowercase      =    tgt_lang
													self.set_src_lang_special_tokens(self._src_lang				)






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													return self._src_lang






						@src_lang.setter
						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     str				)    ->						None:
													"""simple docstring"""


													__lowercase      =    new_src_lang
													self.set_src_lang_special_tokens(self._src_lang				)






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				)    ->						List[int]:
													"""simple docstring"""


													if token_ids_a is None:
																				return self.prefix_tokens + token_ids_a + self.suffix_tokens
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				)    ->						List[int]:
													"""simple docstring"""


													__lowercase      =    [self.sep_token_id]
													__lowercase      =    [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     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str]				,							lowerCamelCase__ :     Optional[str]				,							**lowerCamelCase__ :     List[str]				)    ->						Optional[int]:
													"""simple docstring"""


													if src_lang is None or tgt_lang is None:
																				raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''				)
													__lowercase      =    src_lang
													__lowercase      =    self(lowerCamelCase__				,							add_special_tokens=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)
													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    tgt_lang_id
													return inputs






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     str = "en_XX"				,							lowerCamelCase__ :     Optional[List[str]] = None				,							lowerCamelCase__ :     str = "ro_RO"				,							**lowerCamelCase__ :     Union[str, Any]				,							)    ->						BatchEncoding:
													"""simple docstring"""


													__lowercase      =    src_lang
													__lowercase      =    tgt_lang
													return super().prepare_seqaseq_batch(lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													return self.set_src_lang_special_tokens(self.src_lang				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													return self.set_tgt_lang_special_tokens(self.tgt_lang				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Optional[Any]				)    ->						None:
													"""simple docstring"""


													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    []
													__lowercase      =    [self.eos_token_id, self.cur_lang_code]

													__lowercase      =    self.convert_ids_to_tokens(self.prefix_tokens				)
													__lowercase      =    self.convert_ids_to_tokens(self.suffix_tokens				)

													__lowercase      =    processors.TemplateProcessing(
													    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,							pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,							self.prefix_tokens + self.suffix_tokens				)				)				,							)






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     str				)    ->						None:
													"""simple docstring"""


													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    []
													__lowercase      =    [self.eos_token_id, self.cur_lang_code]

													__lowercase      =    self.convert_ids_to_tokens(self.prefix_tokens				)
													__lowercase      =    self.convert_ids_to_tokens(self.suffix_tokens				)

													__lowercase      =    processors.TemplateProcessing(
													    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,							pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,							self.prefix_tokens + self.suffix_tokens				)				)				,							)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str] = None				)    ->						Tuple[str]:
													"""simple docstring"""


													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																				    '''tokenizer.'''				)

													if not os.path.isdir(lowerCamelCase__				):
																				logger.error(f'Vocabulary path ({save_directory}) should be a directory.'				)
																				return
													__lowercase      =    os.path.join(
													    lowerCamelCase__				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

													if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
																				copyfile(self.vocab_file				,							lowerCamelCase__				)

													return (out_vocab_file,)






