



"""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()
SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
		a_     :      Optional[int] 						=     MobileNetVaConfig(layer_norm_eps=0.0_01	)

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

		a_     :      Dict 						=     re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$", SCREAMING_SNAKE_CASE__	)
		if matches:
				a_     :      Optional[Any] 						=     float(matches[1]	)
				a_     :      Dict 						=     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] 						=     1_001
		a_     :      Optional[Any] 						=     "imagenet-1k-id2label.json"
		a_     :      Dict 						=     "huggingface/label-files"
		a_     :      List[Any] 						=     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, repo_type="dataset"	), "r"	)	)
		a_     :      Dict 						=     {int(SCREAMING_SNAKE_CASE__	) + 1: v for k, v in idalabel.items()}
		a_     :      Union[str, Any] 						=     "background"
		a_     :      Union[str, Any] 						=     idalabel
		a_     :      Optional[Any] 						=     {v: k for k, v in idalabel.items()}

		return config



def        lowerCAmelCase_						(							)			->      str:
		a_     :      str 						=     "http://images.cocodataset.org/val2017/000000039769.jpg"
		a_     :      int 						=     Image.open(requests.get(SCREAMING_SNAKE_CASE__, stream=SCREAMING_SNAKE_CASE__	).raw	)
		return im



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=False	)			->      List[str]:
		a_     :      Any 						=     get_mobilenet_va_config(SCREAMING_SNAKE_CASE__	)

		# Load 🤗 model
		a_     :      Optional[int] 						=     MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__	).eval()

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

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

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				a_     :      List[Any] 						=     torch.tensor([-4.17_39, -1.12_33, 3.12_05]	)
		elif model_name == "mobilenet_v1_0.75_192":
				a_     :      str 						=     torch.tensor([-3.94_40, -2.31_41, -0.33_33]	)
		else:
				a_     :      str 						=     None

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

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

		if push_to_hub:
				print("Pushing to the hub..."	)
				a_     :      Any 						=     "google/" + model_name
				image_processor.push_to_hub(SCREAMING_SNAKE_CASE__	)
				model.push_to_hub(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= 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."""
					)

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




"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, help=(
		        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
		        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
		        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
		        "with 'huggingface'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)