



from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''      ,   A__      ,   )
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					RobertaConfig
     UpperCamelCase__		     =					'''roberta'''




     def __init__(					self			,    a__):
         super().__init__(a__)

         A__					=       RobertaEmbeddings(a__)
         self.init_weights()







@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''      ,   A__      ,   )
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					RobertaConfig
     UpperCamelCase__		     =					'''roberta'''




     def __init__(					self			,    a__):
         super().__init__(a__)
         A__					=       config.num_labels
         A__					=       config.num_hidden_layers

         A__					=       DeeRobertaModel(a__)
         A__					=       nn.Dropout(config.hidden_dropout_prob)
         A__					=       nn.Linear(config.hidden_size			,    self.config.num_labels)




     @add_start_docstrings_to_model_forward(a__)
     def 		snake_case_		(					self			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__=-1			,    a__=False			,    ):
         A__					=       self.num_layers
         try:
             A__					=       self.roberta(
                 a__			,    attention_mask=a__			,    token_type_ids=a__			,    position_ids=a__			,    head_mask=a__			,    inputs_embeds=a__			,    )

             A__					=       outputs[1]

             A__					=       self.dropout(a__)
             A__					=       self.classifier(a__)
             A__					=       (logits,) + outputs[2:]  # add hidden states and attention if they are here
         except HighwayException as e:
             A__					=       e.message
             A__					=       e.exit_layer
             A__					=       outputs[0]

         if not self.training:
             A__					=       entropy(a__)
             A__					=       []
             A__					=       []
         if labels is not None:
             if self.num_labels == 1:
                 #  We are doing regression
                 A__					=       MSELoss()
                 A__					=       loss_fct(logits.view(-1)			,    labels.view(-1))
             else:
                 A__					=       CrossEntropyLoss()
                 A__					=       loss_fct(logits.view(-1			,    self.num_labels)			,    labels.view(-1))

             # work with highway exits
             A__					=       []
             for highway_exit in outputs[-1]:
                 A__					=       highway_exit[0]
                 if not self.training:
                     highway_logits_all.append(a__)
                     highway_entropy.append(highway_exit[2])
                 if self.num_labels == 1:
                     #  We are doing regression
                     A__					=       MSELoss()
                     A__					=       loss_fct(highway_logits.view(-1)			,    labels.view(-1))
                 else:
                     A__					=       CrossEntropyLoss()
                     A__					=       loss_fct(highway_logits.view(-1			,    self.num_labels)			,    labels.view(-1))
                 highway_losses.append(a__)

             if train_highway:
                 A__					=       (sum(highway_losses[:-1]),) + outputs
                 # exclude the final highway, of course
             else:
                 A__					=       (loss,) + outputs
         if not self.training:
             A__					=       outputs + ((original_entropy, highway_entropy), exit_layer)
             if output_layer >= 0:
                 A__					=       (
                     (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                 )  # use the highway of the last layer

         return outputs  # (loss), logits, (hidden_states), (attentions), entropy





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


logging.set_verbosity_info()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

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

    A__					=       re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,							UpperCamelCase_						)
    if matches:
        A__					=       float(matches[1]						)
        A__					=       int(matches[2]						)

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    A__					=       1_0_0_1
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       '''huggingface/label-files'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						) + 1: v for k, v in idalabel.items()}
    A__					=       '''background'''
    A__					=       idalabel
    A__					=       {v: k for k, v in idalabel.items()}

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       MobileNetVaForImageClassification(UpperCamelCase_						).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

    assert logits.shape == (1, 1_0_0_1)

    if model_name == "mobilenet_v1_1.0_224":
        A__					=       torch.tensor([-4.1739, -1.1233, 3.1205]						)
    elif model_name == "mobilenet_v1_0.75_192":
        A__					=       torch.tensor([-3.9440, -2.3141, -0.3333]						)
    else:
        A__					=       None

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

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

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


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="mobilenet_v1_1.0_224",
       type=str,
       help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<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."
   )

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