
'''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()
SCREAMING_SNAKE_CASE    =					logging.get_logger(__name__)







def snake_case_    (				lowercase__     :   int		):
  UpperCAmelCase__			:				Dict												=       MobileNetVaConfig(layer_norm_eps=0.001		)

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

  UpperCAmelCase__			:				List[str]												=       re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"      ,					lowercase__		)
  if matches:
    UpperCAmelCase__			:				Optional[int]												=       float(matches[1]		)
    UpperCAmelCase__			:				List[Any]												=       int(matches[2]		)

  if model_name.startswith("deeplabv3_"		):
    UpperCAmelCase__			:				int												=       8
    UpperCAmelCase__			:				List[Any]												=       2_1
    UpperCAmelCase__			:				Dict												=       "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".
    UpperCAmelCase__			:				int												=       1_0_0_1
    UpperCAmelCase__			:				str												=       "imagenet-1k-id2label.json"

  UpperCAmelCase__			:				Optional[Any]												=       "huggingface/label-files"
  UpperCAmelCase__			:				List[Any]												=       json.load(open(hf_hub_download(lowercase__      ,					lowercase__      ,					repo_type="dataset"		)      ,					"r"		)		)

  if config.num_labels == 1_0_0_1:
    UpperCAmelCase__			:				int												=       {int(lowercase__		) + 1: v for k, v in idalabel.items()}
    UpperCAmelCase__			:				List[str]												=       "background"
  else:
    UpperCAmelCase__			:				Optional[int]												=       {int(lowercase__		): v for k, v in idalabel.items()}

  UpperCAmelCase__			:				Tuple												=       idalabel
  UpperCAmelCase__			:				Optional[int]												=       {v: k for k, v in idalabel.items()}

  return config







def snake_case_    (				):
  UpperCAmelCase__			:				int												=       "http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase__			:				List[str]												=       Image.open(requests.get(lowercase__      ,					stream=lowercase__		).raw		)
  return im







@torch.no_grad()
def snake_case_    (				lowercase__     :   str      ,					lowercase__     :   List[Any]      ,					lowercase__     :   int      ,					lowercase__     :   Union[str, Any]=False		):
  UpperCAmelCase__			:				int												=       get_mobilenet_va_config(lowercase__		)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"		):
    UpperCAmelCase__			:				List[str]												=       MobileNetVaForSemanticSegmentation(lowercase__		).eval()
  else:
    UpperCAmelCase__			:				Optional[int]												=       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
  UpperCAmelCase__			:				str												=       MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size}      ,					size={"shortest_edge": config.image_size + 3_2}      ,					)
  UpperCAmelCase__			:				List[str]												=       image_processor(images=prepare_img()      ,					return_tensors="pt"		)
  UpperCAmelCase__			:				int												=       model(**lowercase__		)
  UpperCAmelCase__			:				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":
      UpperCAmelCase__			:				str												=       torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
              [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
          ]		)

    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":
      UpperCAmelCase__			:				Optional[int]												=       torch.tensor([0.0181, -1.0015, 0.4688]		)
    elif model_name == "mobilenet_v2_1.0_224":
      UpperCAmelCase__			:				Dict												=       torch.tensor([0.2445, -1.1993, 0.1905]		)
    elif model_name == "mobilenet_v2_0.75_160":
      UpperCAmelCase__			:				Optional[Any]												=       torch.tensor([0.2482, 0.4136, 0.6669]		)
    elif model_name == "mobilenet_v2_0.35_96":
      UpperCAmelCase__			:				Tuple												=       torch.tensor([0.1451, -0.4624, 0.7192]		)
    else:
      UpperCAmelCase__			:				str												=       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..."		)
    UpperCAmelCase__			:				List[str]												=       "google/" + model_name
    image_processor.push_to_hub(lowercase__		)
    model.push_to_hub(lowercase__		)


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

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





'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE    =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =					{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						Optional[int]				=       "gpt2"
 lowercase_				:						int				=       ["past_key_values"]
 lowercase_				:						Any				=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }







 def __init__(				self		:     Union[str, Any] ,							snake_case__		:     Optional[int]=5_02_57 ,							snake_case__		:     List[Any]=10_24 ,							snake_case__		:     Tuple=7_68 ,							snake_case__		:     List[Any]=12 ,							snake_case__		:     List[str]=12 ,							snake_case__		:     Union[str, Any]=None ,							snake_case__		:     str="gelu_new" ,							snake_case__		:     Any=0.1 ,							snake_case__		:     Union[str, Any]=0.1 ,							snake_case__		:     Tuple=0.1 ,							snake_case__		:     List[str]=1e-5 ,							snake_case__		:     Optional[int]=0.02 ,							snake_case__		:     int="cls_index" ,							snake_case__		:     Tuple=True ,							snake_case__		:     List[Any]=None ,							snake_case__		:     Optional[int]=True ,							snake_case__		:     Optional[Any]=0.1 ,							snake_case__		:     Tuple=True ,							snake_case__		:     int=True ,							snake_case__		:     Optional[int]=5_02_56 ,							snake_case__		:     List[Any]=5_02_56 ,							snake_case__		:     Union[str, Any]=False ,							snake_case__		:     Optional[int]=False ,							**snake_case__		:     List[Any] ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       vocab_size
   UpperCAmelCase__			:				Any												=       n_positions
   UpperCAmelCase__			:				str												=       n_embd
   UpperCAmelCase__			:				List[Any]												=       n_layer
   UpperCAmelCase__			:				Any												=       n_head
   UpperCAmelCase__			:				Any												=       n_inner
   UpperCAmelCase__			:				Tuple												=       activation_function
   UpperCAmelCase__			:				List[str]												=       resid_pdrop
   UpperCAmelCase__			:				Union[str, Any]												=       embd_pdrop
   UpperCAmelCase__			:				int												=       attn_pdrop
   UpperCAmelCase__			:				Optional[Any]												=       layer_norm_epsilon
   UpperCAmelCase__			:				Optional[Any]												=       initializer_range
   UpperCAmelCase__			:				Optional[Any]												=       summary_type
   UpperCAmelCase__			:				Tuple												=       summary_use_proj
   UpperCAmelCase__			:				int												=       summary_activation
   UpperCAmelCase__			:				List[str]												=       summary_first_dropout
   UpperCAmelCase__			:				Optional[Any]												=       summary_proj_to_labels
   UpperCAmelCase__			:				Optional[Any]												=       scale_attn_weights
   UpperCAmelCase__			:				List[Any]												=       use_cache
   UpperCAmelCase__			:				Union[str, Any]												=       scale_attn_by_inverse_layer_idx
   UpperCAmelCase__			:				Tuple												=       reorder_and_upcast_attn

   UpperCAmelCase__			:				Dict												=       bos_token_id
   UpperCAmelCase__			:				Union[str, Any]												=       eos_token_id

   super().__init__(bos_token_id=snake_case__ ,							eos_token_id=snake_case__ ,							**snake_case__   )






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''







 def __init__(				self		:     Union[str, Any] ,							snake_case__		:     PretrainedConfig ,							snake_case__		:     str = "default" ,							snake_case__		:     List[PatchingSpec] = None ,							snake_case__		:     bool = False ,							):



   '''simple docstring'''




   super().__init__(snake_case__ ,							task=snake_case__ ,							patching_specs=snake_case__ ,							use_past=snake_case__   )
   if not getattr(self._config ,							"pad_token_id" ,							snake_case__   ):
     # TODO: how to do that better?
     UpperCAmelCase__			:				List[str]												=       0







 @property
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}   )
   if self.use_past:
     self.fill_with_past_key_values_(snake_case__ ,							direction="inputs"   )
     UpperCAmelCase__			:				Dict												=       {0: "batch", 1: "past_sequence + sequence"}
   else:
     UpperCAmelCase__			:				Dict												=       {0: "batch", 1: "sequence"}

   return common_inputs







 @property
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   return self._config.n_layer







 @property
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   return self._config.n_head







 def        UpperCamelCase     (				self		:     int ,							snake_case__		:     PreTrainedTokenizer ,							snake_case__		:     int = -1 ,							snake_case__		:     int = -1 ,							snake_case__		:     bool = False ,							snake_case__		:     Optional[TensorType] = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       super(snake_case__ ,							self   ).generate_dummy_inputs(
       snake_case__ ,							batch_size=snake_case__ ,							seq_length=snake_case__ ,							is_pair=snake_case__ ,							framework=snake_case__   )

   # We need to order the input in the way they appears in the forward()
   UpperCAmelCase__			:				Dict												=       OrderedDict({"input_ids": common_inputs["input_ids"]}   )

   # Need to add the past_keys
   if self.use_past:
     if not is_torch_available():
       raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
     else:
       import torch

       UpperCAmelCase__					, UpperCAmelCase__			:				int												=       common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       UpperCAmelCase__			:				int												=       seqlen + 2
       UpperCAmelCase__			:				Union[str, Any]												=       (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       UpperCAmelCase__			:				Optional[Any]												=       [
           (torch.zeros(snake_case__   ), torch.zeros(snake_case__   )) for _ in range(self.num_layers   )
       ]

   UpperCAmelCase__			:				List[str]												=       common_inputs["attention_mask"]
   if self.use_past:
     UpperCAmelCase__			:				List[Any]												=       ordered_inputs["attention_mask"].dtype
     UpperCAmelCase__			:				Optional[int]												=       torch.cat(
         [ordered_inputs["attention_mask"], torch.ones(snake_case__ ,							snake_case__ ,							dtype=snake_case__   )] ,							dim=1   )

   return ordered_inputs







 @property
 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   return 13



