


'''simple docstring'''




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

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




@flax.struct.dataclass
class  _lowercase   :
   _SCREAMING_SNAKE_CASE     :     CommonSchedulerState
   _SCREAMING_SNAKE_CASE     :     jnp.ndarray

   # setable values
   _SCREAMING_SNAKE_CASE     :     jnp.ndarray
   _SCREAMING_SNAKE_CASE     :     jnp.ndarray
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  None
   @classmethod
   def a     (	cls					:			Dict					,      SCREAMING_SNAKE_CASE_					:			CommonSchedulerState					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      )   ->       Dict:
        return cls(
            common=SCREAMING_SNAKE_CASE_					,      final_alpha_cumprod=SCREAMING_SNAKE_CASE_					,      init_noise_sigma=SCREAMING_SNAKE_CASE_					,      timesteps=SCREAMING_SNAKE_CASE_					,      )




@dataclass
class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     DDIMSchedulerState




class  _lowercase   (  __lowercase	,				__lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  [e.name for e in FlaxKarrasDiffusionSchedulers]

   _SCREAMING_SNAKE_CASE     :     jnp.dtype
   @property
   def a     (	self					:			Tuple			)   ->       Any:
        return True
   @register_to_config
   def __init__(	self					:			str					,      SCREAMING_SNAKE_CASE_					:			int = 1000					,      SCREAMING_SNAKE_CASE_					:			float = 0.0_0_0_1					,      SCREAMING_SNAKE_CASE_					:			float = 0.0_2					,      SCREAMING_SNAKE_CASE_					:			str = "linear"					,      SCREAMING_SNAKE_CASE_					:			Optional[jnp.ndarray] = None					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      SCREAMING_SNAKE_CASE_					:			int = 0					,      SCREAMING_SNAKE_CASE_					:			str = "epsilon"					,      SCREAMING_SNAKE_CASE_					:			jnp.dtype = jnp.floataa					,      )   ->       Dict:
        __snake_case    			=	dtype
   def a     (	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			Optional[CommonSchedulerState] = None			)   ->       DDIMSchedulerState:
        if common is None:
             __snake_case    			=	CommonSchedulerState.create(self			)

        # At every step in ddim, we are looking into the previous alphas_cumprod
        # For the final step, there is no previous alphas_cumprod because we are already at 0
        # `set_alpha_to_one` decides whether we set this parameter simply to one or
        # whether we use the final alpha of the "non-previous" one.
        __snake_case    			=	(
            jnp.array(1.0					,      dtype=self.dtype			) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
        )

        # standard deviation of the initial noise distribution
        __snake_case    			=	jnp.array(1.0					,      dtype=self.dtype			)

        __snake_case    			=	jnp.arange(0					,      self.config.num_train_timesteps			).round()[::-1]

        return DDIMSchedulerState.create(
            common=SCREAMING_SNAKE_CASE_					,      final_alpha_cumprod=SCREAMING_SNAKE_CASE_					,      init_noise_sigma=SCREAMING_SNAKE_CASE_					,      timesteps=SCREAMING_SNAKE_CASE_					,      )
   def a     (	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			DDIMSchedulerState					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			Optional[int] = None			)   ->       jnp.ndarray:
        return sample
   def a     (	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			DDIMSchedulerState					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Tuple = ()			)   ->       DDIMSchedulerState:
        __snake_case    			=	self.config.num_train_timesteps // num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # rounding to avoid issues when num_inference_step is power of 3
        __snake_case    			=	(jnp.arange(0					,      SCREAMING_SNAKE_CASE_			) * step_ratio).round()[::-1] + self.config.steps_offset

        return state.replace(
            num_inference_steps=SCREAMING_SNAKE_CASE_					,      timesteps=SCREAMING_SNAKE_CASE_					,      )
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			DDIMSchedulerState					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       Any:
        __snake_case    			=	state.common.alphas_cumprod[timestep]
        __snake_case    			=	jnp.where(
            prev_timestep >= 0					,      state.common.alphas_cumprod[prev_timestep]					,      state.final_alpha_cumprod			)
        __snake_case    			=	1 - alpha_prod_t
        __snake_case    			=	1 - alpha_prod_t_prev

        __snake_case    			=	(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			DDIMSchedulerState					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			float = 0.0					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      )   ->       Union[FlaxDDIMSchedulerOutput, Tuple]:
        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'			)

        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
        # Ideally, read DDIM paper in-detail understanding

        # Notation (<variable name> -> <name in paper>
        # - pred_noise_t -> e_theta(x_t, t)
        # - pred_original_sample -> f_theta(x_t, t) or x_0
        # - std_dev_t -> sigma_t
        # - eta -> η
        # - pred_sample_direction -> "direction pointing to x_t"
        # - pred_prev_sample -> "x_t-1"

        # 1. get previous step value (=t-1)
        __snake_case    			=	timestep - self.config.num_train_timesteps // state.num_inference_steps

        __snake_case    			=	state.common.alphas_cumprod
        __snake_case    			=	state.final_alpha_cumprod

        # 2. compute alphas, betas
        __snake_case    			=	alphas_cumprod[timestep]
        __snake_case    			=	jnp.where(prev_timestep >= 0					,      alphas_cumprod[prev_timestep]					,      SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
             __snake_case    			=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             __snake_case    			=	model_output
        elif self.config.prediction_type == "sample":
             __snake_case    			=	model_output
             __snake_case    			=	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
             __snake_case    			=	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             __snake_case    			=	(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                 ' `v_prediction`'			)

        # 4. compute variance: "sigma_t(η)" -> see formula (16)
        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
        __snake_case    			=	self._get_variance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	eta * variance ** 0.5

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        __snake_case    			=	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        __snake_case    			=	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
             return (prev_sample, state)

        return FlaxDDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_					,      state=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			DDIMSchedulerState					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      )   ->       jnp.ndarray:
        return add_noise_common(state.common					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			DDIMSchedulerState					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      )   ->       jnp.ndarray:
        return get_velocity_common(state.common					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)





   def __len__(	self					:			Any			)   ->       Union[str, Any]:
        return self.config.num_train_timesteps








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


logging.set_verbosity_info()
_a      :      int       				=      logging.get_logger(__name__)





def      _a		(lowercase__ :      int	) -> Union[str, Any]:






     """simple docstring"""
     __snake_case    			=	MobileNetVaConfig(layer_norm_eps=0.0_01	)

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

     __snake_case    			=	re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'       ,      lowercase__	)
     if matches:
          __snake_case    			=	float(matches[1]	)
          __snake_case    			=	int(matches[2]	)

     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	8
          __snake_case    			=	2_1
          __snake_case    			=	'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".
          __snake_case    			=	1_0_0_1
          __snake_case    			=	'imagenet-1k-id2label.json'

     __snake_case    			=	'huggingface/label-files'
     __snake_case    			=	json.load(open(hf_hub_download(lowercase__       ,      lowercase__       ,      repo_type='dataset'	)       ,      'r'	)	)

     if config.num_labels == 1_0_0_1:
          __snake_case    			=	{int(lowercase__	) + 1: v for k, v in idalabel.items()}
          __snake_case    			=	'background'
     else:
          __snake_case    			=	{int(lowercase__	): v for k, v in idalabel.items()}

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

     return config





def      _a		() -> Dict:






     """simple docstring"""
     __snake_case    			=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __snake_case    			=	Image.open(requests.get(lowercase__       ,      stream=lowercase__	).raw	)
     return im





@torch.no_grad()
def      _a		(lowercase__ :      str       ,      lowercase__ :      Optional[int]       ,      lowercase__ :      Optional[Any]       ,      lowercase__ :      Dict=False	) -> Any:






     """simple docstring"""
     __snake_case    			=	get_mobilenet_va_config(lowercase__	)

     # Load 🤗 model
     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	MobileNetVaForSemanticSegmentation(lowercase__	).eval()
     else:
          __snake_case    			=	MobileNetVaForImageClassification(lowercase__	).eval()

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

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     __snake_case    			=	MobileNetVaImageProcessor(
         crop_size={'width': config.image_size, 'height': config.image_size}       ,      size={'shortest_edge': config.image_size + 3_2}       ,      )
     __snake_case    			=	image_processor(images=prepare_img()       ,      return_tensors='pt'	)
     __snake_case    			=	model(**lowercase__	)
     __snake_case    			=	outputs.logits

     if model_name.startswith('deeplabv3_'	):
          assert logits.shape == (1, 2_1, 6_5, 6_5)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               __snake_case    			=	torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]	)

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

          assert torch.allclose(logits[0, :3, :3, :3]       ,      lowercase__       ,      atol=1e-4	)
     else:
          assert logits.shape == (1, 1_0_0_1)

          if model_name == "mobilenet_v2_1.4_224":
               __snake_case    			=	torch.tensor([0.01_81, -1.00_15, 0.46_88]	)
          elif model_name == "mobilenet_v2_1.0_224":
               __snake_case    			=	torch.tensor([0.24_45, -1.19_93, 0.19_05]	)
          elif model_name == "mobilenet_v2_0.75_160":
               __snake_case    			=	torch.tensor([0.24_82, 0.41_36, 0.66_69]	)
          elif model_name == "mobilenet_v2_0.35_96":
               __snake_case    			=	torch.tensor([0.14_51, -0.46_24, 0.71_92]	)
          else:
               __snake_case    			=	None

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

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

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


if __name__ == "__main__":
       _a      :      List[str]       				=      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      :      str       				=      parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




