

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_   							=  logging.get_logger(__name__)
def 	a (     A__      :  Dict	)       -> List[Any]:





      """simple docstring"""



      _lowercase      			=MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            _lowercase      			=[144, 192, 240]
            _lowercase      			=[16, 32, 64, 96, 128, 160, 640]
      elif "mobilevit_xs" in mobilevit_name:
            _lowercase      			=[96, 120, 144]
            _lowercase      			=[16, 32, 48, 64, 80, 96, 384]
      elif "mobilevit_xxs" in mobilevit_name:
            _lowercase      			=[64, 80, 96]
            _lowercase      			=[16, 16, 24, 48, 64, 80, 320]
            _lowercase      			=0.05
            _lowercase      			=2.0

      if mobilevit_name.startswith('deeplabv3_'	):
            _lowercase      			=512
            _lowercase      			=16
            _lowercase      			=21
            _lowercase      			='pascal-voc-id2label.json'
      else:
            _lowercase      			=1000
            _lowercase      			='imagenet-1k-id2label.json'

      _lowercase      			='huggingface/label-files'
      _lowercase      			=json.load(open(hf_hub_download(A__ ,  A__ ,  repo_type='dataset'	) ,  'r'	)	)
      _lowercase      			={int(A__	): v for k, v in idalabel.items()}
      _lowercase      			=idalabel
      _lowercase      			={v: k for k, v in idalabel.items()}

      return config
def 	a (     A__      :  Tuple ,  A__      :  List[Any]=False	)       -> List[str]:





      """simple docstring"""



      for i in range(1 ,  6	):
            if F'''layer_{i}.''' in name:
                  _lowercase      			=name.replace(F'''layer_{i}.''' ,  F'''encoder.layer.{i - 1}.'''	)

      if "conv_1." in name:
            _lowercase      			=name.replace('conv_1.' ,  'conv_stem.'	)
      if ".block." in name:
            _lowercase      			=name.replace('.block.' ,  '.'	)

      if "exp_1x1" in name:
            _lowercase      			=name.replace('exp_1x1' ,  'expand_1x1'	)
      if "red_1x1" in name:
            _lowercase      			=name.replace('red_1x1' ,  'reduce_1x1'	)
      if ".local_rep.conv_3x3." in name:
            _lowercase      			=name.replace('.local_rep.conv_3x3.' ,  '.conv_kxk.'	)
      if ".local_rep.conv_1x1." in name:
            _lowercase      			=name.replace('.local_rep.conv_1x1.' ,  '.conv_1x1.'	)
      if ".norm." in name:
            _lowercase      			=name.replace('.norm.' ,  '.normalization.'	)
      if ".conv." in name:
            _lowercase      			=name.replace('.conv.' ,  '.convolution.'	)
      if ".conv_proj." in name:
            _lowercase      			=name.replace('.conv_proj.' ,  '.conv_projection.'	)

      for i in range(0 ,  2	):
            for j in range(0 ,  4	):
                  if F'''.{i}.{j}.''' in name:
                        _lowercase      			=name.replace(F'''.{i}.{j}.''' ,  F'''.{i}.layer.{j}.'''	)

      for i in range(2 ,  6	):
            for j in range(0 ,  4	):
                  if F'''.{i}.{j}.''' in name:
                        _lowercase      			=name.replace(F'''.{i}.{j}.''' ,  F'''.{i}.'''	)
                        if "expand_1x1" in name:
                              _lowercase      			=name.replace('expand_1x1' ,  'downsampling_layer.expand_1x1'	)
                        if "conv_3x3" in name:
                              _lowercase      			=name.replace('conv_3x3' ,  'downsampling_layer.conv_3x3'	)
                        if "reduce_1x1" in name:
                              _lowercase      			=name.replace('reduce_1x1' ,  'downsampling_layer.reduce_1x1'	)

      for i in range(2 ,  5	):
            if F'''.global_rep.{i}.weight''' in name:
                  _lowercase      			=name.replace(F'''.global_rep.{i}.weight''' ,  '.layernorm.weight'	)
            if F'''.global_rep.{i}.bias''' in name:
                  _lowercase      			=name.replace(F'''.global_rep.{i}.bias''' ,  '.layernorm.bias'	)

      if ".global_rep." in name:
            _lowercase      			=name.replace('.global_rep.' ,  '.transformer.'	)
      if ".pre_norm_mha.0." in name:
            _lowercase      			=name.replace('.pre_norm_mha.0.' ,  '.layernorm_before.'	)
      if ".pre_norm_mha.1.out_proj." in name:
            _lowercase      			=name.replace('.pre_norm_mha.1.out_proj.' ,  '.attention.output.dense.'	)
      if ".pre_norm_ffn.0." in name:
            _lowercase      			=name.replace('.pre_norm_ffn.0.' ,  '.layernorm_after.'	)
      if ".pre_norm_ffn.1." in name:
            _lowercase      			=name.replace('.pre_norm_ffn.1.' ,  '.intermediate.dense.'	)
      if ".pre_norm_ffn.4." in name:
            _lowercase      			=name.replace('.pre_norm_ffn.4.' ,  '.output.dense.'	)
      if ".transformer." in name:
            _lowercase      			=name.replace('.transformer.' ,  '.transformer.layer.'	)

      if ".aspp_layer." in name:
            _lowercase      			=name.replace('.aspp_layer.' ,  '.'	)
      if ".aspp_pool." in name:
            _lowercase      			=name.replace('.aspp_pool.' ,  '.'	)
      if "seg_head." in name:
            _lowercase      			=name.replace('seg_head.' ,  'segmentation_head.'	)
      if "segmentation_head.classifier.classifier." in name:
            _lowercase      			=name.replace('segmentation_head.classifier.classifier.' ,  'segmentation_head.classifier.'	)

      if "classifier.fc." in name:
            _lowercase      			=name.replace('classifier.fc.' ,  'classifier.'	)
      elif (not base_model) and ("segmentation_head." not in name):
            _lowercase      			='mobilevit.' + name

      return name
def 	a (     A__      :  int ,  A__      :  List[str] ,  A__      :  List[Any]=False	)       -> List[str]:





      """simple docstring"""



      if base_model:
            _lowercase      			=''
      else:
            _lowercase      			='mobilevit.'

      for key in orig_state_dict.copy().keys():
            _lowercase      			=orig_state_dict.pop(A__	)

            if key[:8] == "encoder.":
                  _lowercase      			=key[8:]

            if "qkv" in key:
                  _lowercase      			=key.split('.'	)
                  _lowercase      			=int(key_split[0][6:]	) - 1
                  _lowercase      			=int(key_split[3]	)
                  _lowercase      			=model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}'''	)
                  _lowercase      			=layer.transformer.layer[transformer_num].attention.attention.all_head_size
                  _lowercase      			=(
                      F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
                  )
                  if "weight" in key:
                        _lowercase      			=val[:dim, :]
                        _lowercase      			=val[dim : dim * 2, :]
                        _lowercase      			=val[-dim:, :]
                  else:
                        _lowercase      			=val[:dim]
                        _lowercase      			=val[dim : dim * 2]
                        _lowercase      			=val[-dim:]
            else:
                  _lowercase      			=val

      return orig_state_dict
def 	a (     )       -> Dict:





      """simple docstring"""



      _lowercase      			='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase      			=Image.open(requests.get(A__ ,  stream=A__	).raw	)
      return im
@torch.no_grad()
def 	a (     A__      :  Optional[int] ,  A__      :  Any ,  A__      :  Any ,  A__      :  List[str]=False	)       -> Optional[int]:





      """simple docstring"""



      _lowercase      			=get_mobilevit_config(A__	)

      # load original state_dict
      _lowercase      			=torch.load(A__ ,  map_location='cpu'	)

      # load 🤗 model
      if mobilevit_name.startswith('deeplabv3_'	):
            _lowercase      			=MobileViTForSemanticSegmentation(A__	).eval()
      else:
            _lowercase      			=MobileViTForImageClassification(A__	).eval()

      _lowercase      			=convert_state_dict(A__ ,  A__	)
      model.load_state_dict(A__	)

      # Check outputs on an image, prepared by MobileViTImageProcessor
      _lowercase      			=MobileViTImageProcessor(crop_size=config.image_size ,  size=config.image_size + 32	)
      _lowercase      			=image_processor(images=prepare_img() ,  return_tensors='pt'	)
      _lowercase      			=model(**A__	)
      _lowercase      			=outputs.logits

      if mobilevit_name.startswith('deeplabv3_'	):
            assert logits.shape == (1, 21, 32, 32)

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  _lowercase      			=torch.tensor(
                      [
                          [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                          [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                          [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                      ]	)
            elif mobilevit_name == "deeplabv3_mobilevit_xs":
                  _lowercase      			=torch.tensor(
                      [
                          [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                          [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                          [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                      ]	)
            elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                  _lowercase      			=torch.tensor(
                      [
                          [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                          [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                          [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                      ]	)
            else:
                  raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''	)

            assert torch.allclose(logits[0, :3, :3, :3] ,  A__ ,  atol=1e-4	)
      else:
            assert logits.shape == (1, 1000)

            if mobilevit_name == "mobilevit_s":
                  _lowercase      			=torch.tensor([-0.9866, 0.2392, -1.1241]	)
            elif mobilevit_name == "mobilevit_xs":
                  _lowercase      			=torch.tensor([-2.4761, -0.9399, -1.9587]	)
            elif mobilevit_name == "mobilevit_xxs":
                  _lowercase      			=torch.tensor([-1.9364, -1.2327, -0.4653]	)
            else:
                  raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''	)

            assert torch.allclose(logits[0, :3] ,  A__ ,  atol=1e-4	)

      Path(A__	).mkdir(exist_ok=A__	)
      print(F'''Saving model {mobilevit_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:
            _lowercase      			={
                'mobilevit_s': 'mobilevit-small',
                'mobilevit_xs': 'mobilevit-x-small',
                'mobilevit_xxs': 'mobilevit-xx-small',
                'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
                'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
                'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
            }

            print('Pushing to the hub...'	)
            _lowercase      			=model_mapping[mobilevit_name]
            image_processor.push_to_hub(A__ ,  organization='apple'	)
            model.push_to_hub(A__ ,  organization='apple'	)


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--mobilevit_name',
      default='mobilevit_s',
      type=str,
      help=(
          'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
          ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
      ),
  )
  parser.add_argument(
      '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt 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.'
  )

  lowercase_   							=  parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )






import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				List[str]=7	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						None
		if token is not None:
				lowercase__	     =						{"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

		# The id of a workflow (not of a workflow run)
		lowercase__	     =						"""636036"""

		lowercase__	     =						f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
		# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
		url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

		lowercase__	     =						requests.get(__magic_name__       ,       headers=__magic_name__	).json()

		return result["workflow_runs"]






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						get_daily_ci_runs(__magic_name__	)
		lowercase__	     =						None
		for workflow_run in workflow_runs:
				if workflow_run["status"] == "completed":
						lowercase__	     =						workflow_run["""id"""]
						break

		return workflow_run_id






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				List[Any]       ,       __magic_name__	:				Dict	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						get_last_daily_ci_runs(__magic_name__	)
		if workflow_run_id is not None:
				lowercase__	     =						get_artifacts_links(worflow_run_id=__magic_name__       ,       token=__magic_name__	)
				for artifact_name in artifact_names:
						if artifact_name in artifacts_links:
								lowercase__	     =						artifacts_links[artifact_name]
								download_artifact(
								    artifact_name=__magic_name__       ,       artifact_url=__magic_name__       ,       output_dir=__magic_name__       ,       token=__magic_name__	)







def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				str       ,       __magic_name__	:				Union[str, Any]	)  ->    Tuple:
		"""simple docstring"""




		get_last_daily_ci_artifacts(__magic_name__       ,       __magic_name__       ,       __magic_name__	)

		lowercase__	     =						{}
		for artifact_name in artifact_names:
				lowercase__	     =						os.path.join(__magic_name__       ,       f'''{artifact_name}.zip'''	)
				if os.path.isfile(__magic_name__	):
						lowercase__	     =						{}
						with zipfile.ZipFile(__magic_name__	) as z:
								for filename in z.namelist():
										if not os.path.isdir(__magic_name__	):
												# read the file
												with z.open(__magic_name__	) as f:
														lowercase__	     =						f.read().decode("""UTF-8"""	)

		return results

