



'''simple docstring'''


import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:       Dict      	=	logging.get_logger(__name__)







def 				__lowerCamelCase				(   A__    )       ->	Union[str, Any]:






   """simple docstring"""
   print('Loading config file...'    )

   def flatten_yaml_as_dict(A__    ,						A__=""    ,						A__="."    ):
      UpperCamelCase					=  []
      for k, v in d.items():
         UpperCamelCase					=  parent_key + sep + k if parent_key else k
         if isinstance(A__    ,						collections.abc.MutableMapping    ):
            items.extend(flatten_yaml_as_dict(A__    ,						A__    ,						sep=A__    ).items()    )
         else:
            items.append((new_key, v)    )
      return dict(A__    )

   UpperCamelCase					=  argparse.Namespace()
   with open(A__    ,						'r'    ) as yaml_file:
      try:
         UpperCamelCase					=  yaml.load(A__    ,						Loader=yaml.FullLoader    )

         UpperCamelCase					=  flatten_yaml_as_dict(A__    )
         for k, v in flat_cfg.items():
            setattr(A__    ,						A__    ,						A__    )
      except yaml.YAMLError as exc:
         logger.error('Error while loading config file: {}. Error message: {}'.format(A__    ,						str(A__    )    )    )
   return config







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  MobileViTVaConfig()

   UpperCamelCase					=  False

   # dataset
   if task_name.startswith('imagenet1k_'    ):
      UpperCamelCase					=  1_000
      if int(task_name.strip().split('_'    )[-1]    ) == 384:
         UpperCamelCase					=  384
      else:
         UpperCamelCase					=  256
      UpperCamelCase					=  'imagenet-1k-id2label.json'
   elif task_name.startswith('imagenet21k_to_1k_'    ):
      UpperCamelCase					=  21_000
      if int(task_name.strip().split('_'    )[-1]    ) == 384:
         UpperCamelCase					=  384
      else:
         UpperCamelCase					=  256
      UpperCamelCase					=  'imagenet-22k-id2label.json'
   elif task_name.startswith('ade20k_'    ):
      UpperCamelCase					=  151
      UpperCamelCase					=  512
      UpperCamelCase					=  'ade20k-id2label.json'
      UpperCamelCase					=  True
   elif task_name.startswith('voc_'    ):
      UpperCamelCase					=  21
      UpperCamelCase					=  512
      UpperCamelCase					=  'pascal-voc-id2label.json'
      UpperCamelCase					=  True

   # orig_config
   UpperCamelCase					=  load_orig_config_file(A__    )
   assert getattr(A__    ,						'model.classification.name'    ,						-1    ) == "mobilevit_v2", "Invalid model"
   UpperCamelCase					=  getattr(A__    ,						'model.classification.mitv2.width_multiplier'    ,						1.0    )
   assert (
       getattr(A__    ,						'model.classification.mitv2.attn_norm_layer'    ,						-1    ) == "layer_norm_2d"
   ), "Norm layers other than layer_norm_2d is not supported"
   UpperCamelCase					=  getattr(A__    ,						'model.classification.activation.name'    ,						'swish'    )
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      UpperCamelCase					=  getattr(A__    ,						'model.segmentation.output_stride'    ,						16    )
      if "_deeplabv3" in task_name:
         UpperCamelCase					=  getattr(A__    ,						'model.segmentation.deeplabv3.aspp_rates'    ,						[12, 24, 36]    )
         UpperCamelCase					=  getattr(A__    ,						'model.segmentation.deeplabv3.aspp_out_channels'    ,						512    )
         UpperCamelCase					=  getattr(A__    ,						'model.segmentation.deeplabv3.aspp_dropout'    ,						0.1    )

    # id2label
   UpperCamelCase					=  'huggingface/label-files'
   UpperCamelCase					=  json.load(open(hf_hub_download(A__    ,						A__    ,						repo_type='dataset'    )    ,						'r'    )    )
   UpperCamelCase					=  {int(A__    ): v for k, v in idalabel.items()}
   UpperCamelCase					=  idalabel
   UpperCamelCase					=  {v: k for k, v in idalabel.items()}

   return config







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  dct.pop(A__    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   A__    ,						A__=False    )       ->	Tuple:






   """simple docstring"""
   if base_model:
      UpperCamelCase					=  ''
   else:
      UpperCamelCase					=  'mobilevitv2.'

   UpperCamelCase					=  []
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         UpperCamelCase					=  k[8:]
      else:
         UpperCamelCase					=  k

      if ".block." in k:
         UpperCamelCase					=  k_new.replace('.block.'    ,						'.'    )
      if ".conv." in k:
         UpperCamelCase					=  k_new.replace('.conv.'    ,						'.convolution.'    )
      if ".norm." in k:
         UpperCamelCase					=  k_new.replace('.norm.'    ,						'.normalization.'    )

      if "conv_1." in k:
         UpperCamelCase					=  k_new.replace('conv_1.'    ,						F"""{model_prefix}conv_stem."""    )
      for i in [1, 2]:
         if F"""layer_{i}.""" in k:
            UpperCamelCase					=  k_new.replace(F"""layer_{i}."""    ,						F"""{model_prefix}encoder.layer.{i-1}.layer."""    )
      if ".exp_1x1." in k:
         UpperCamelCase					=  k_new.replace('.exp_1x1.'    ,						'.expand_1x1.'    )
      if ".red_1x1." in k:
         UpperCamelCase					=  k_new.replace('.red_1x1.'    ,						'.reduce_1x1.'    )

      for i in [3, 4, 5]:
         if F"""layer_{i}.0.""" in k:
            UpperCamelCase					=  k_new.replace(F"""layer_{i}.0."""    ,						F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""    )
         if F"""layer_{i}.1.local_rep.0.""" in k:
            UpperCamelCase					=  k_new.replace(F"""layer_{i}.1.local_rep.0."""    ,						F"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""    )
         if F"""layer_{i}.1.local_rep.1.""" in k:
            UpperCamelCase					=  k_new.replace(F"""layer_{i}.1.local_rep.1."""    ,						F"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""    )

      for i in [3, 4, 5]:
         if i == 3:
            UpperCamelCase					=  [0, 1]
         elif i == 4:
            UpperCamelCase					=  [0, 1, 2, 3]
         elif i == 5:
            UpperCamelCase					=  [0, 1, 2]

         for j in j_in:
            if F"""layer_{i}.1.global_rep.{j}.""" in k:
               UpperCamelCase					=  k_new.replace(
                   F"""layer_{i}.1.global_rep.{j}."""    ,						F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""    )
         if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
            UpperCamelCase					=  k_new.replace(
                F"""layer_{i}.1.global_rep.{j+1}."""    ,						F"""{model_prefix}encoder.layer.{i-1}.layernorm."""    )

         if F"""layer_{i}.1.conv_proj.""" in k:
            UpperCamelCase					=  k_new.replace(F"""layer_{i}.1.conv_proj."""    ,						F"""{model_prefix}encoder.layer.{i-1}.conv_projection."""    )

      if "pre_norm_attn.0." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_attn.0.'    ,						'layernorm_before.'    )
      if "pre_norm_attn.1." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_attn.1.'    ,						'attention.'    )
      if "pre_norm_ffn.0." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_ffn.0.'    ,						'layernorm_after.'    )
      if "pre_norm_ffn.1." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_ffn.1.'    ,						'ffn.conv1.'    )
      if "pre_norm_ffn.3." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_ffn.3.'    ,						'ffn.conv2.'    )

      if "classifier.1." in k:
         UpperCamelCase					=  k_new.replace('classifier.1.'    ,						'classifier.'    )

      if "seg_head." in k:
         UpperCamelCase					=  k_new.replace('seg_head.'    ,						'segmentation_head.'    )
      if ".aspp_layer." in k:
         UpperCamelCase					=  k_new.replace('.aspp_layer.'    ,						'.'    )
      if ".aspp_pool." in k:
         UpperCamelCase					=  k_new.replace('.aspp_pool.'    ,						'.'    )

      rename_keys.append((k, k_new)    )
   return rename_keys







def 				__lowerCamelCase				(   A__    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  []
   for k in state_dict.keys():
      if k.startswith('seg_head.aux_head.'    ):
         keys_to_ignore.append(A__    )
   for k in keys_to_ignore:
      state_dict.pop(A__    ,						A__    )







def 				__lowerCamelCase				(   )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   UpperCamelCase					=  Image.open(requests.get(A__    ,						stream=A__    ).raw    )
   return im







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  get_mobilevitva_config(A__    ,						A__    )

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

   # load huggingface model
   if task_name.startswith('ade20k_'    ) or task_name.startswith('voc_'    ):
      UpperCamelCase					=  MobileViTVaForSemanticSegmentation(A__    ).eval()
      UpperCamelCase					=  False
   else:
      UpperCamelCase					=  MobileViTVaForImageClassification(A__    ).eval()
      UpperCamelCase					=  False

   # remove and rename some keys of load the original model
   UpperCamelCase					=  checkpoint
   remove_unused_keys(A__    )
   UpperCamelCase					=  create_rename_keys(A__    ,						base_model=A__    )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(A__    ,						A__    ,						A__    )

   # load modified state_dict
   model.load_state_dict(A__    )

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

   # verify classification model
   if task_name.startswith('imagenet'    ):
      UpperCamelCase					=  outputs.logits
      UpperCamelCase					=  logits.argmax(-1    ).item()
      print('Predicted class:'    ,						model.config.idalabel[predicted_class_idx]    )
      if task_name.startswith('imagenet1k_256'    ) and config.width_multiplier == 1.0:
         # expected_logits for base variant
         UpperCamelCase					=  torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]    )
         assert torch.allclose(logits[0, :3]    ,						A__    ,						atol=1e-4    )

   Path(A__    ).mkdir(exist_ok=A__    )
   print(F"""Saving model {task_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 __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--task",
       default="imagenet1k_256",
       type=str,
       help=(
           "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
           "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
       ),
       choices=[
           "imagenet1k_256",
           "imagenet1k_384",
           "imagenet21k_to_1k_256",
           "imagenet21k_to_1k_384",
           "ade20k_deeplabv3",
           "voc_deeplabv3",
       ],
   )

   parser.add_argument(
       "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
   )
   parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
   parser.add_argument(
       "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
   )

   _lowerCamelCase	:       Tuple      	=	parser.parse_args()
   convert_mobilevitva_checkpoint(
       args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
   )






'''simple docstring'''


import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_lowerCamelCase	:       Union[str, Any]      	=	"\\n\n"

_lowerCamelCase	:       List[str]      	=	"\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

_lowerCamelCase	:       Dict      	=	"\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION		)
class  SCREAMING_SNAKE_CASE (  datasets.Metric		):






    """simple docstring"""





    def  A       (  self      :					Tuple ):


       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
               {
                   'input_texts': datasets.Value('string' ),
               } )  ,						reference_urls=['https://huggingface.co/docs/transformers/perplexity']  ,						)






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int = 1_6  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					List[Any]=None ):


       """simple docstring"""







       if device is not None:
          assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
          if device == "gpu":
             UpperCamelCase					=  'cuda'
       else:
          UpperCamelCase					=  'cuda' if torch.cuda.is_available() else 'cpu'

       UpperCamelCase					=  AutoModelForCausalLM.from_pretrained(UpperCamelCase__ )
       UpperCamelCase					=  model.to(UpperCamelCase__ )

       UpperCamelCase					=  AutoTokenizer.from_pretrained(UpperCamelCase__ )

       # if batch_size > 1 (which generally leads to padding being required), and
       # if there is not an already assigned pad_token, assign an existing
       # special token to also be the padding token
       if tokenizer.pad_token is None and batch_size > 1:
          UpperCamelCase					=  list(tokenizer.special_tokens_map_extended.values() )
          # check that the model already has at least one special token defined
          assert (
              len(UpperCamelCase__ ) > 0
          ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
          # assign one of the special tokens to also be the pad token
          tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]} )

       if add_start_token:
          # leave room for <BOS> token to be added:
          assert (
              tokenizer.bos_token is not None
          ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
          UpperCamelCase					=  model.config.max_length - 1
       else:
          UpperCamelCase					=  model.config.max_length

       UpperCamelCase					=  tokenizer(
           UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						return_tensors='pt'  ,						return_attention_mask=UpperCamelCase__  ,						).to(UpperCamelCase__ )

       UpperCamelCase					=  encodings['input_ids']
       UpperCamelCase					=  encodings['attention_mask']

       # check that each input is long enough:
       if add_start_token:
          assert torch.all(torch.ge(attn_masks.sum(1 )  ,						1 ) ), "Each input text must be at least one token long."
       else:
          assert torch.all(
              torch.ge(attn_masks.sum(1 )  ,						2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

       UpperCamelCase					=  []
       UpperCamelCase					=  CrossEntropyLoss(reduction='none' )

       for start_index in logging.tqdm(range(0  ,						len(UpperCamelCase__ )  ,						UpperCamelCase__ ) ):
          UpperCamelCase					=  min(start_index + batch_size  ,						len(UpperCamelCase__ ) )
          UpperCamelCase					=  encoded_texts[start_index:end_index]
          UpperCamelCase					=  attn_masks[start_index:end_index]

          if add_start_token:
             UpperCamelCase					=  torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(UpperCamelCase__ )
             UpperCamelCase					=  torch.cat([bos_tokens_tensor, encoded_batch]  ,						dim=1 )
             UpperCamelCase					=  torch.cat(
                 [torch.ones(bos_tokens_tensor.size()  ,						dtype=torch.intaa ).to(UpperCamelCase__ ), attn_mask]  ,						dim=1 )

          UpperCamelCase					=  encoded_batch

          with torch.no_grad():
             UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__ ).logits

          UpperCamelCase					=  out_logits[..., :-1, :].contiguous()
          UpperCamelCase					=  labels[..., 1:].contiguous()
          UpperCamelCase					=  attn_mask[..., 1:].contiguous()

          UpperCamelCase					=  torch.expa(
              (loss_fct(shift_logits.transpose(1  ,						2 )  ,						UpperCamelCase__ ) * shift_attention_mask_batch).sum(1 )
              / shift_attention_mask_batch.sum(1 ) )

          ppls += perplexity_batch.tolist()

       return {"perplexities": ppls, "mean_perplexity": np.mean(UpperCamelCase__ )}

