"""simple docstring"""



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(		lowerCAmelCase							):
							A__       :     List[Any]								=			MobileNetVaConfig(layer_norm_eps=0.001							)

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

							A__       :     Union[str, Any]								=			re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""			,  _A							)
							if matches:
														A__       :     int								=			float(matches[1]							)
														A__       :     int								=			int(matches[2]							)

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

							return config

def  _A(		):
							A__       :     Optional[Any]								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
							A__       :     int								=			Image.open(requests.get(_A			,  stream=_A							).raw							)
							return im

@torch.no_grad()
def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=False							):
							A__       :     str								=			get_mobilenet_va_config(_A							)

							# Load 🤗 model
							A__       :     Tuple								=			MobileNetVaForImageClassification(_A							).eval()

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

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							A__       :     List[str]								=			MobileNetVaImageProcessor(
							    crop_size={"""width""": config.image_size, """height""": config.image_size}			,  size={"""shortest_edge""": config.image_size + 32}			,  )
							A__       :     List[str]								=			image_processor(images=prepare_img()			,  return_tensors="""pt"""							)
							A__       :     Dict								=			model(**_A							)
							A__       :     Optional[int]								=			outputs.logits

							assert logits.shape == (1, 1001)

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

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

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

							if push_to_hub:
														print("""Pushing to the hub..."""							)
														A__       :     int								=			'google/' + model_name
														image_processor.push_to_hub(_A							)
														model.push_to_hub(_A							)


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
				)









'''simple docstring'''







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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase     :  Tuple											=    logging.get_logger(__name__)

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

lowerCAmelCase     :  str											=    {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase     :  str											=    {
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     GPTaTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            _lowerCAmelCase     :				str       =							kwargs.pop('add_bos_token'   ,						snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				str       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				List[str]       =							add_prefix_space
                   _lowerCAmelCase     :				Dict       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Any       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            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(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            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(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							[]
            for is_user, text in conversation.iter_texts():
                   input_ids.extend(self.encode(snake_case__   ,						add_special_tokens=snake_case__ ) + [self.eos_token_id] )

            if len(snake_case__ ) > self.model_max_length:
                   _lowerCAmelCase     :				str       =							input_ids[-self.model_max_length :]
            return input_ids

