





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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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





def  __UpperCamelCase   (     a)			->Dict:
					lowerCamelCase__      =							MobileNetVaConfig(layer_norm_eps=0.0_0_1)

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

					lowerCamelCase__      =							re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$",  a)
					if matches:
										lowerCamelCase__      =							float(matches[1])
										lowerCamelCase__      =							int(matches[2])

					if model_name.startswith("deeplabv3_"):
										lowerCamelCase__      =							8
										lowerCamelCase__      =							21
										lowerCamelCase__      =							"pascal-voc-id2label.json"
					else:
										# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
										# of the usual 1000. The first class (index 0) is "background".
										lowerCamelCase__      =							1001
										lowerCamelCase__      =							"imagenet-1k-id2label.json"

					lowerCamelCase__      =							"huggingface/label-files"
					lowerCamelCase__      =							json.load(open(hf_hub_download(a,  a,  repo_type="dataset"),  "r"))

					if config.num_labels == 1001:
										lowerCamelCase__      =							{int(a) + 1: v for k, v in idalabel.items()}
										lowerCamelCase__      =							"background"
					else:
										lowerCamelCase__      =							{int(a): v for k, v in idalabel.items()}

					lowerCamelCase__      =							idalabel
					lowerCamelCase__      =							{v: k for k, v in idalabel.items()}

					return config





def  __UpperCamelCase   (     )			->Optional[Any]:
					lowerCamelCase__      =							"http://images.cocodataset.org/val2017/000000039769.jpg"
					lowerCamelCase__      =							Image.open(requests.get(a,  stream=a).raw)
					return im





@torch.no_grad()
def  __UpperCamelCase   (     a,  a,  a,  a=False)			->Union[str, Any]:
					lowerCamelCase__      =							get_mobilenet_va_config(a)

					# Load 🤗 model
					if model_name.startswith("deeplabv3_"):
										lowerCamelCase__      =							MobileNetVaForSemanticSegmentation(a).eval()
					else:
										lowerCamelCase__      =							MobileNetVaForImageClassification(a).eval()

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

					# Check outputs on an image, prepared by MobileNetV2ImageProcessor
					lowerCamelCase__      =							MobileNetVaImageProcessor(
					    crop_size={"width": config.image_size, "height": config.image_size},  size={"shortest_edge": config.image_size + 32},  )
					lowerCamelCase__      =							image_processor(images=prepare_img(),  return_tensors="pt")
					lowerCamelCase__      =							model(**a)
					lowerCamelCase__      =							outputs.logits

					if model_name.startswith("deeplabv3_"):
										assert logits.shape == (1, 21, 65, 65)

										if model_name == "deeplabv3_mobilenet_v2_1.0_513":
															lowerCamelCase__      =							torch.tensor(
															    [
															        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
															        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
															        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
															    ])

										else:
															raise ValueError(f"Unknown model name: {model_name}")

										assert torch.allclose(logits[0, :3, :3, :3],  a,  atol=1E-4)
					else:
										assert logits.shape == (1, 1001)

										if model_name == "mobilenet_v2_1.4_224":
															lowerCamelCase__      =							torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8])
										elif model_name == "mobilenet_v2_1.0_224":
															lowerCamelCase__      =							torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5])
										elif model_name == "mobilenet_v2_0.75_160":
															lowerCamelCase__      =							torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9])
										elif model_name == "mobilenet_v2_0.35_96":
															lowerCamelCase__      =							torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2])
										else:
															lowerCamelCase__      =							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...")
										lowerCamelCase__      =							"google/" + model_name
										image_processor.push_to_hub(a)
										model.push_to_hub(a)


if __name__ == "__main__":
						A_        =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="mobilenet_v2_1.0_224",
						    type=str,
						    help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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."
						)

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








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					A__								=     42

					# setable values
					A__								=     42
					A__								=     42
					A__								=     42
					A__								=     None

					# running values
					A__								=     None





					@classmethod
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										return cls(common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						)

@dataclass
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					A__								=     42





					@property
					def        __magic_name__			(			self						):
										return True





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = jnp.floataa						,	):
										lowerCamelCase__      =							dtype





					def        __magic_name__			(			self						,	_lowerCAmelCase = None						):
										if common is None:
															lowerCamelCase__      =							CommonSchedulerState.create(self						)

										lowerCamelCase__      =							jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]
										lowerCamelCase__      =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										lowerCamelCase__      =							sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										((lowerCamelCase__)     ,		)      =							jnp.where(state.timesteps == timestep						,	size=1						)
										lowerCamelCase__      =							step_index[0]

										lowerCamelCase__      =							state.sigmas[step_index]
										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def lms_derivative(_lowerCAmelCase						):
															lowerCamelCase__      =							1.0
															for k in range(_lowerCAmelCase						):
																				if current_order == k:
																									continue
																				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

										lowerCamelCase__      =							integrate.quad(_lowerCAmelCase						,	state.sigmas[t]						,	state.sigmas[t + 1]						,	epsrel=1E-4						)[0]

										return integrated_coeff





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = ()						):
										lowerCamelCase__      =							jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	_lowerCAmelCase						,	dtype=self.dtype						)

										lowerCamelCase__      =							jnp.floor(_lowerCAmelCase						).astype(jnp.intaa						)
										lowerCamelCase__      =							jnp.ceil(_lowerCAmelCase						).astype(jnp.intaa						)

										lowerCamelCase__      =							jnp.mod(_lowerCAmelCase						,	1.0						)

										lowerCamelCase__      =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCamelCase__      =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										lowerCamelCase__      =							jnp.concatenate([sigmas, jnp.array([0.0]						,	dtype=self.dtype						)]						)

										lowerCamelCase__      =							timesteps.astype(jnp.intaa						)

										# initial running values
										lowerCamelCase__      =							jnp.zeros((0,) + shape						,	dtype=self.dtype						)

										return state.replace(
										    timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	num_inference_steps=_lowerCAmelCase						,	derivatives=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 4						,	_lowerCAmelCase = True						,	):
										if state.num_inference_steps is None:
															raise ValueError(
															    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"						)

										lowerCamelCase__      =							state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

										# 2. Convert to an ODE derivative
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma
										lowerCamelCase__      =							state.replace(derivatives=jnp.append(state.derivatives						,	_lowerCAmelCase						)						)
										if len(state.derivatives						) > order:
															lowerCamelCase__      =							state.replace(derivatives=jnp.delete(state.derivatives						,	0						)						)

										# 3. Compute linear multistep coefficients
										lowerCamelCase__      =							min(timestep + 1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[self.get_lms_coefficient(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

										# 4. Compute previous sample based on the derivatives path
										lowerCamelCase__      =							sample + sum(
										    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase						,	reversed(state.derivatives						)						)						)

										if not return_dict:
															return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase						,	state=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							state.sigmas[timesteps].flatten()
										lowerCamelCase__      =							broadcast_to_shape_from_left(_lowerCAmelCase						,	noise.shape						)

										lowerCamelCase__      =							original_samples + noise * sigma

										return noisy_samples






					def __len__(			self						):
										return self.config.num_train_timesteps

