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()
UpperCAmelCase							   =      logging.get_logger(__name__)







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Union[str, Any]:
     a  :    List[str]				=						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_v2_([^_]*)_([^_]*)$'''						,      lowerCAmelCase_       )
     if matches:
          a  :    Tuple				=						float(matches[1]       )
          a  :    List[Any]				=						int(matches[2]       )

     if model_name.startswith('''deeplabv3_'''       ):
          a  :    Any				=						8
          a  :    Any				=						2_1
          a  :    Optional[Any]				=						"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".
          a  :    Optional[Any]				=						1_0_0_1
          a  :    str				=						"imagenet-1k-id2label.json"

     a  :    Any				=						"huggingface/label-files"
     a  :    str				=						json.load(open(hf_hub_download(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='''dataset'''       )						,      '''r'''       )       )

     if config.num_labels == 1_0_0_1:
          a  :    Optional[Any]				=						{int(lowerCAmelCase_       ) + 1: v for k, v in idalabel.items()}
          a  :    Any				=						"background"
     else:
          a  :    Optional[Any]				=						{int(lowerCAmelCase_       ): v for k, v in idalabel.items()}

     a  :    Any				=						idalabel
     a  :    List[str]				=						{v: k for k, v in idalabel.items()}

     return config







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







@torch.no_grad()
def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False       )	->	str:
     a  :    Dict				=						get_mobilenet_va_config(lowerCAmelCase_       )

     # Load 🤗 model
     if model_name.startswith('''deeplabv3_'''       ):
          a  :    str				=						MobileNetVaForSemanticSegmentation(lowerCAmelCase_       ).eval()
     else:
          a  :    Any				=						MobileNetVaForImageClassification(lowerCAmelCase_       ).eval()

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

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     a  :    List[str]				=						MobileNetVaImageProcessor(
         crop_size={'''width''': config.image_size, '''height''': config.image_size}						,      size={'''shortest_edge''': config.image_size + 3_2}						,      )
     a  :    Any				=						image_processor(images=prepare_img()						,      return_tensors='''pt'''       )
     a  :    Dict				=						model(**lowerCAmelCase_       )
     a  :    List[Any]				=						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":
               a  :    Any				=						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]],
                       [[-2.15_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]						,      lowerCAmelCase_						,      atol=1e-4       )
     else:
          assert logits.shape == (1, 1_0_0_1)

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

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

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

     if push_to_hub:
          print('''Pushing to the hub...'''       )
          a  :    Optional[Any]				=						"google/" + model_name
          image_processor.push_to_hub(lowerCAmelCase_       )
          model.push_to_hub(lowerCAmelCase_       )


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

  UpperCAmelCase							   =      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 torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						torch.FloatTensor






class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      snake_case__	):
   @register_to_config
   def __init__(    self							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 3							,     lowerCAmelCase_ = 3							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "positional"							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")							,     lowerCAmelCase_ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")							,     lowerCAmelCase_ = (2_24, 4_48, 6_72, 8_96)							,     lowerCAmelCase_ = 2							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = "conv"							,     lowerCAmelCase_ = "conv"							,     lowerCAmelCase_ = "silu"							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 1e-5							,     lowerCAmelCase_ = "default"							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     )	->Tuple:
        super().__init__()

        a  :    List[Any]				=						sample_size
        a  :    List[Any]				=						block_out_channels[0] * 4

        # Check inputs
        if len(lowerCAmelCase_    ) != len(lowerCAmelCase_    ):
             raise ValueError(
                 F"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""    )

        if len(lowerCAmelCase_    ) != len(lowerCAmelCase_    ):
             raise ValueError(
                 F"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""    )

        # input
        a  :    List[Any]				=						nn.Convad(lowerCAmelCase_							,     block_out_channels[0]							,     kernel_size=3							,     padding=(1, 1)    )

        # time
        if time_embedding_type == "fourier":
             a  :    List[Any]				=						GaussianFourierProjection(embedding_size=block_out_channels[0]							,     scale=16    )
             a  :    List[Any]				=						2 * block_out_channels[0]
        elif time_embedding_type == "positional":
             a  :    Dict				=						Timesteps(block_out_channels[0]							,     lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    List[str]				=						block_out_channels[0]

        a  :    List[str]				=						TimestepEmbedding(lowerCAmelCase_							,     lowerCAmelCase_    )

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
             a  :    Dict				=						nn.Embedding(lowerCAmelCase_							,     lowerCAmelCase_    )
        elif class_embed_type == "timestep":
             a  :    str				=						TimestepEmbedding(lowerCAmelCase_							,     lowerCAmelCase_    )
        elif class_embed_type == "identity":
             a  :    List[str]				=						nn.Identity(lowerCAmelCase_							,     lowerCAmelCase_    )
        else:
             a  :    Optional[Any]				=						None

        a  :    str				=						nn.ModuleList([]    )
        a  :    Any				=						None
        a  :    List[Any]				=						nn.ModuleList([]    )

        # down
        a  :    List[Any]				=						block_out_channels[0]
        for i, down_block_type in enumerate(lowerCAmelCase_    ):
             a  :    Dict				=						output_channel
             a  :    Union[str, Any]				=						block_out_channels[i]
             a  :    int				=						i == len(lowerCAmelCase_    ) - 1

             a  :    Tuple				=						get_down_block(
                 lowerCAmelCase_							,     num_layers=lowerCAmelCase_							,     in_channels=lowerCAmelCase_							,     out_channels=lowerCAmelCase_							,     temb_channels=lowerCAmelCase_							,     add_downsample=not is_final_block							,     resnet_eps=lowerCAmelCase_							,     resnet_act_fn=lowerCAmelCase_							,     resnet_groups=lowerCAmelCase_							,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel							,     downsample_padding=lowerCAmelCase_							,     resnet_time_scale_shift=lowerCAmelCase_							,     downsample_type=lowerCAmelCase_							,     )
             self.down_blocks.append(lowerCAmelCase_    )

        # mid
        a  :    int				=						UNetMidBlockaD(
            in_channels=block_out_channels[-1]							,     temb_channels=lowerCAmelCase_							,     resnet_eps=lowerCAmelCase_							,     resnet_act_fn=lowerCAmelCase_							,     output_scale_factor=lowerCAmelCase_							,     resnet_time_scale_shift=lowerCAmelCase_							,     attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]							,     resnet_groups=lowerCAmelCase_							,     add_attention=lowerCAmelCase_							,     )

        # up
        a  :    Optional[int]				=						list(reversed(lowerCAmelCase_    )    )
        a  :    Tuple				=						reversed_block_out_channels[0]
        for i, up_block_type in enumerate(lowerCAmelCase_    ):
             a  :    List[Any]				=						output_channel
             a  :    Optional[Any]				=						reversed_block_out_channels[i]
             a  :    Dict				=						reversed_block_out_channels[min(i + 1							,     len(lowerCAmelCase_    ) - 1    )]

             a  :    Any				=						i == len(lowerCAmelCase_    ) - 1

             a  :    List[Any]				=						get_up_block(
                 lowerCAmelCase_							,     num_layers=layers_per_block + 1							,     in_channels=lowerCAmelCase_							,     out_channels=lowerCAmelCase_							,     prev_output_channel=lowerCAmelCase_							,     temb_channels=lowerCAmelCase_							,     add_upsample=not is_final_block							,     resnet_eps=lowerCAmelCase_							,     resnet_act_fn=lowerCAmelCase_							,     resnet_groups=lowerCAmelCase_							,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel							,     resnet_time_scale_shift=lowerCAmelCase_							,     upsample_type=lowerCAmelCase_							,     )
             self.up_blocks.append(lowerCAmelCase_    )
             a  :    Any				=						output_channel

        # out
        a  :    str				=						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4							,     32    )
        a  :    Tuple				=						nn.GroupNorm(num_channels=block_out_channels[0]							,     num_groups=lowerCAmelCase_							,     eps=lowerCAmelCase_    )
        a  :    Any				=						nn.SiLU()
        a  :    List[Any]				=						nn.Convad(block_out_channels[0]							,     lowerCAmelCase_							,     kernel_size=3							,     padding=1    )


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     )	->Union[UNetaDOutput, Tuple]:
        # 0. center input if necessary
        if self.config.center_input_sample:
             a  :    Any				=						2 * sample - 1.0

        # 1. time
        a  :    Optional[Any]				=						timestep
        if not torch.is_tensor(lowerCAmelCase_    ):
             a  :    Dict				=						torch.tensor([timesteps]							,     dtype=torch.long							,     device=sample.device    )
        elif torch.is_tensor(lowerCAmelCase_    ) and len(timesteps.shape    ) == 0:
             a  :    Any				=						timesteps[None].to(sample.device    )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        a  :    Optional[Any]				=						timesteps * torch.ones(sample.shape[0]							,     dtype=timesteps.dtype							,     device=timesteps.device    )

        a  :    Dict				=						self.time_proj(lowerCAmelCase_    )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        a  :    Optional[Any]				=						t_emb.to(dtype=self.dtype    )
        a  :    Dict				=						self.time_embedding(lowerCAmelCase_    )

        if self.class_embedding is not None:
             if class_labels is None:
                  raise ValueError('''class_labels should be provided when doing class conditioning'''    )

             if self.config.class_embed_type == "timestep":
                  a  :    List[str]				=						self.time_proj(lowerCAmelCase_    )

             a  :    Any				=						self.class_embedding(lowerCAmelCase_    ).to(dtype=self.dtype    )
             a  :    Optional[int]				=						emb + class_emb

        # 2. pre-process
        a  :    int				=						sample
        a  :    Dict				=						self.conv_in(lowerCAmelCase_    )

        # 3. down
        a  :    Union[str, Any]				=						(sample,)
        for downsample_block in self.down_blocks:
             if hasattr(lowerCAmelCase_							,     '''skip_conv'''    ):
                  a			,							a			,							a  :    str				=						downsample_block(
                      hidden_states=lowerCAmelCase_							,     temb=lowerCAmelCase_							,     skip_sample=lowerCAmelCase_    )
             else:
                  a			,							a  :    List[str]				=						downsample_block(hidden_states=lowerCAmelCase_							,     temb=lowerCAmelCase_    )

             down_block_res_samples += res_samples

        # 4. mid
        a  :    str				=						self.mid_block(lowerCAmelCase_							,     lowerCAmelCase_    )

        # 5. up
        a  :    int				=						None
        for upsample_block in self.up_blocks:
             a  :    List[str]				=						down_block_res_samples[-len(upsample_block.resnets    ) :]
             a  :    Any				=						down_block_res_samples[: -len(upsample_block.resnets    )]

             if hasattr(lowerCAmelCase_							,     '''skip_conv'''    ):
                  a			,							a  :    Any				=						upsample_block(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
             else:
                  a  :    Union[str, Any]				=						upsample_block(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        # 6. post-process
        a  :    Tuple				=						self.conv_norm_out(lowerCAmelCase_    )
        a  :    Optional[Any]				=						self.conv_act(lowerCAmelCase_    )
        a  :    Tuple				=						self.conv_out(lowerCAmelCase_    )

        if skip_sample is not None:
             sample += skip_sample

        if self.config.time_embedding_type == "fourier":
             a  :    Optional[Any]				=						timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]    )))    )
             a  :    int				=						sample / timesteps

        if not return_dict:
             return (sample,)

        return UNetaDOutput(sample=lowerCAmelCase_    )