
"""simple docstring"""

import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def 							A		(       snake_case   :Optional[Any]	)   ->			List[str]:
 __UpperCamelCase       						= SwinvaConfig()
 __UpperCamelCase       						= swinva_name.split('_'	)

 __UpperCamelCase       						= name_split[1]
 if "to" in name_split[3]:
  __UpperCamelCase       						= int(name_split[3][-3:]	)
 else:
  __UpperCamelCase       						= int(name_split[3]	)
 if "to" in name_split[2]:
  __UpperCamelCase       						= int(name_split[2][-2:]	)
 else:
  __UpperCamelCase       						= int(name_split[2][6:]	)

 if model_size == "tiny":
  __UpperCamelCase       						= 9_6
  __UpperCamelCase       						= (2, 2, 6, 2)
  __UpperCamelCase       						= (3, 6, 1_2, 2_4)
 elif model_size == "small":
  __UpperCamelCase       						= 9_6
  __UpperCamelCase       						= (2, 2, 1_8, 2)
  __UpperCamelCase       						= (3, 6, 1_2, 2_4)
 elif model_size == "base":
  __UpperCamelCase       						= 1_2_8
  __UpperCamelCase       						= (2, 2, 1_8, 2)
  __UpperCamelCase       						= (4, 8, 1_6, 3_2)
 else:
  __UpperCamelCase       						= 1_9_2
  __UpperCamelCase       						= (2, 2, 1_8, 2)
  __UpperCamelCase       						= (6, 1_2, 2_4, 4_8)

 if "to" in swinva_name:
  __UpperCamelCase       						= (1_2, 1_2, 1_2, 6)

 if ("22k" in swinva_name) and ("to" not in swinva_name):
  __UpperCamelCase       						= 2_1_8_4_1
  __UpperCamelCase       						= 'huggingface/label-files'
  __UpperCamelCase       						= 'imagenet-22k-id2label.json'
  __UpperCamelCase       						= json.load(open(hf_hub_download(snake_case       ,      snake_case       ,      repo_type='dataset'	)       ,      'r'	)	)
  __UpperCamelCase       						= {int(snake_case	): v for k, v in idalabel.items()}
  __UpperCamelCase       						= idalabel
  __UpperCamelCase       						= {v: k for k, v in idalabel.items()}

 else:
  __UpperCamelCase       						= 1_0_0_0
  __UpperCamelCase       						= 'huggingface/label-files'
  __UpperCamelCase       						= 'imagenet-1k-id2label.json'
  __UpperCamelCase       						= json.load(open(hf_hub_download(snake_case       ,      snake_case       ,      repo_type='dataset'	)       ,      'r'	)	)
  __UpperCamelCase       						= {int(snake_case	): v for k, v in idalabel.items()}
  __UpperCamelCase       						= idalabel
  __UpperCamelCase       						= {v: k for k, v in idalabel.items()}

 __UpperCamelCase       						= img_size
 __UpperCamelCase       						= num_classes
 __UpperCamelCase       						= embed_dim
 __UpperCamelCase       						= depths
 __UpperCamelCase       						= num_heads
 __UpperCamelCase       						= window_size

 return config






def 							A		(       snake_case   :Dict	)   ->			Union[str, Any]:
 if "patch_embed.proj" in name:
  __UpperCamelCase       						= name.replace('patch_embed.proj'       ,      'embeddings.patch_embeddings.projection'	)
 if "patch_embed.norm" in name:
  __UpperCamelCase       						= name.replace('patch_embed.norm'       ,      'embeddings.norm'	)
 if "layers" in name:
  __UpperCamelCase       						= 'encoder.' + name
 if "attn.proj" in name:
  __UpperCamelCase       						= name.replace('attn.proj'       ,      'attention.output.dense'	)
 if "attn" in name:
  __UpperCamelCase       						= name.replace('attn'       ,      'attention.self'	)
 if "norm1" in name:
  __UpperCamelCase       						= name.replace('norm1'       ,      'layernorm_before'	)
 if "norm2" in name:
  __UpperCamelCase       						= name.replace('norm2'       ,      'layernorm_after'	)
 if "mlp.fc1" in name:
  __UpperCamelCase       						= name.replace('mlp.fc1'       ,      'intermediate.dense'	)
 if "mlp.fc2" in name:
  __UpperCamelCase       						= name.replace('mlp.fc2'       ,      'output.dense'	)
 if "q_bias" in name:
  __UpperCamelCase       						= name.replace('q_bias'       ,      'query.bias'	)
 if "k_bias" in name:
  __UpperCamelCase       						= name.replace('k_bias'       ,      'key.bias'	)
 if "v_bias" in name:
  __UpperCamelCase       						= name.replace('v_bias'       ,      'value.bias'	)
 if "cpb_mlp" in name:
  __UpperCamelCase       						= name.replace('cpb_mlp'       ,      'continuous_position_bias_mlp'	)
 if name == "norm.weight":
  __UpperCamelCase       						= 'layernorm.weight'
 if name == "norm.bias":
  __UpperCamelCase       						= 'layernorm.bias'

 if "head" in name:
  __UpperCamelCase       						= name.replace('head'       ,      'classifier'	)
 else:
  __UpperCamelCase       						= 'swinv2.' + name

 return name






def 							A		(       snake_case   :Union[str, Any]       ,      snake_case   :List[Any]	)   ->			str:
 for key in orig_state_dict.copy().keys():
  __UpperCamelCase       						= orig_state_dict.pop(snake_case	)

  if "mask" in key:
   continue
  elif "qkv" in key:
   __UpperCamelCase       						= key.split('.'	)
   __UpperCamelCase       						= int(key_split[1]	)
   __UpperCamelCase       						= int(key_split[3]	)
   __UpperCamelCase       						= model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

   if "weight" in key:
    __UpperCamelCase       						= val[:dim, :]
    __UpperCamelCase       						= val[dim : dim * 2, :]
    __UpperCamelCase       						= val[-dim:, :]
   else:
    __UpperCamelCase       						= val[:dim]
    __UpperCamelCase       						= val[
        dim : dim * 2
    ]
    __UpperCamelCase       						= val[-dim:]
  else:
   __UpperCamelCase       						= val

 return orig_state_dict






def 							A		(       snake_case   :Dict       ,      snake_case   :List[Any]	)   ->			Union[str, Any]:
 __UpperCamelCase       						= timm.create_model(snake_case       ,      pretrained=snake_case	)
 timm_model.eval()

 __UpperCamelCase       						= get_swinva_config(snake_case	)
 __UpperCamelCase       						= SwinvaForImageClassification(snake_case	)
 model.eval()

 __UpperCamelCase       						= convert_state_dict(timm_model.state_dict()       ,      snake_case	)
 model.load_state_dict(snake_case	)

 __UpperCamelCase       						= 'http://images.cocodataset.org/val2017/000000039769.jpg'

 __UpperCamelCase       						= AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'       ,      '-'	)	)	)
 __UpperCamelCase       						= Image.open(requests.get(snake_case       ,      stream=snake_case	).raw	)
 __UpperCamelCase       						= image_processor(images=snake_case       ,      return_tensors='pt'	)

 __UpperCamelCase       						= timm_model(inputs['pixel_values']	)
 __UpperCamelCase       						= model(**snake_case	).logits

 assert torch.allclose(snake_case       ,      snake_case       ,      atol=1e-3	)

 print(f'Saving model {swinva_name} to {pytorch_dump_folder_path}'	)
 model.save_pretrained(snake_case	)

 print(f'Saving image processor to {pytorch_dump_folder_path}'	)
 image_processor.save_pretrained(snake_case	)

 model.push_to_hub(
     repo_path_or_name=Path(snake_case       ,      snake_case	)       ,      organization='nandwalritik'       ,      commit_message='Add model'       ,      )


if __name__ == "__main__":
      UpperCamelCase			:       List[Any]									=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--swinv2_name",
          default="swinv2_tiny_patch4_window8_256",
          type=str,
          help="Name of the Swinv2 timm model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      UpperCamelCase			:       Tuple									=				parser.parse_args()
      convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





"""simple docstring"""

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

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)






def 							A		(       snake_case   :Optional[int]	)   ->			str:
 __UpperCamelCase       						= torch.exp(snake_case	)
 __UpperCamelCase       						= torch.sum(snake_case       ,      dim=1	)  # sum of exp(x_i)
 __UpperCamelCase       						= torch.sum(x * exp_x       ,      dim=1	)  # sum of x_i * exp(x_i)
 return torch.log(snake_case	) - B / A

class __lowerCAmelCase	(   nn.Module    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__()
      __UpperCamelCase       						= config.output_attentions
      __UpperCamelCase       						= config.output_hidden_states
      __UpperCamelCase       						= nn.ModuleList([BertLayer(__UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )
      __UpperCamelCase       						= nn.ModuleList([BertHighway(__UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )

      __UpperCamelCase       						= [-1 for _ in range(config.num_hidden_layers   )]





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if (type(__UpperCAmelCase   ) is float) or (type(__UpperCAmelCase   ) is int):
       for i in range(len(self.early_exit_entropy   )   ):
        __UpperCamelCase       						= x
      else:
       __UpperCamelCase       						= x





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= pooler.state_dict()
      for highway in self.highway:
       for name, param in highway.pooler.state_dict().items():
        param.copy_(loaded_model[name]   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= ()
      __UpperCamelCase       						= ()
      __UpperCamelCase       						= ()
      for i, layer_module in enumerate(self.layer   ):
       if self.output_hidden_states:
        __UpperCamelCase       						= all_hidden_states + (hidden_states,)

       __UpperCamelCase       						= layer_module(
           __UpperCAmelCase			,  __UpperCAmelCase			,  head_mask[i]			,  __UpperCAmelCase			,  __UpperCAmelCase   )
       __UpperCamelCase       						= layer_outputs[0]

       if self.output_attentions:
        __UpperCamelCase       						= all_attentions + (layer_outputs[1],)

       __UpperCamelCase       						= (hidden_states,)
       if self.output_hidden_states:
        __UpperCamelCase       						= current_outputs + (all_hidden_states,)
       if self.output_attentions:
        __UpperCamelCase       						= current_outputs + (all_attentions,)

       __UpperCamelCase       						= self.highway[i](__UpperCAmelCase   )
       # logits, pooled_output

       if not self.training:
        __UpperCamelCase       						= highway_exit[0]
        __UpperCamelCase       						= entropy(__UpperCAmelCase   )
        __UpperCamelCase       						= highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
        __UpperCamelCase       						= all_highway_exits + (highway_exit,)

        if highway_entropy < self.early_exit_entropy[i]:
         __UpperCamelCase       						= (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
         raise HighwayException(__UpperCAmelCase			,  i + 1   )
       else:
        __UpperCamelCase       						= all_highway_exits + (highway_exit,)

        # Add last layer
      if self.output_hidden_states:
       __UpperCamelCase       						= all_hidden_states + (hidden_states,)

      __UpperCamelCase       						= (hidden_states,)
      if self.output_hidden_states:
       __UpperCamelCase       						= outputs + (all_hidden_states,)
      if self.output_attentions:
       __UpperCamelCase       						= outputs + (all_attentions,)

      __UpperCamelCase       						= outputs + (all_highway_exits,)
      return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits


@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "    ,						__SCREAMING_SNAKE_CASE    ,						)
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase   )
      __UpperCamelCase       						= config

      __UpperCamelCase       						= BertEmbeddings(__UpperCAmelCase   )
      __UpperCamelCase       						= DeeBertEncoder(__UpperCAmelCase   )
      __UpperCamelCase       						= BertPooler(__UpperCAmelCase   )

      self.init_weights()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.encoder.init_highway_pooler(self.pooler   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.embeddings.word_embeddings





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= value





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      for layer, heads in heads_to_prune.items():
       self.encoder.layer[layer].attention.prune_heads(__UpperCAmelCase   )







     @add_start_docstrings_to_model_forward(__UpperCAmelCase   )
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      if input_ids is not None and inputs_embeds is not None:
       raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'   )
      elif input_ids is not None:
       __UpperCamelCase       						= input_ids.size()
      elif inputs_embeds is not None:
       __UpperCamelCase       						= inputs_embeds.size()[:-1]
      else:
       raise ValueError('You have to specify either input_ids or inputs_embeds'   )

      __UpperCamelCase       						= input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
       __UpperCamelCase       						= torch.ones(__UpperCAmelCase			,  device=__UpperCAmelCase   )
      if encoder_attention_mask is None:
       __UpperCamelCase       						= torch.ones(__UpperCAmelCase			,  device=__UpperCAmelCase   )
      if token_type_ids is None:
       __UpperCamelCase       						= torch.zeros(__UpperCAmelCase			,  dtype=torch.long			,  device=__UpperCAmelCase   )

      # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
      # ourselves in which case we just need to make it broadcastable to all heads.
      __UpperCamelCase       						= self.get_extended_attention_mask(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )

      # If a 2D ou 3D attention mask is provided for the cross-attention
      # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
      if encoder_attention_mask.dim() == 3:
       __UpperCamelCase       						= encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.dim() == 2:
       __UpperCamelCase       						= encoder_attention_mask[:, None, None, :]

      __UpperCamelCase       						= encoder_extended_attention_mask.to(
          dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
      __UpperCamelCase       						= (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

      # Prepare head mask if needed
      # 1.0 in head_mask indicate we keep the head
      # attention_probs has shape bsz x n_heads x N x N
      # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
      # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
      __UpperCamelCase       						= self.get_head_mask(__UpperCAmelCase			,  self.config.num_hidden_layers   )

      __UpperCamelCase       						= self.embeddings(
          input_ids=__UpperCAmelCase			,  position_ids=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase			,  inputs_embeds=__UpperCAmelCase   )
      __UpperCamelCase       						= self.encoder(
          __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  head_mask=__UpperCAmelCase			,  encoder_hidden_states=__UpperCAmelCase			,  encoder_attention_mask=__UpperCAmelCase			,  )
      __UpperCamelCase       						= encoder_outputs[0]
      __UpperCamelCase       						= self.pooler(__UpperCAmelCase   )

      __UpperCamelCase       						= (
          sequence_output,
          pooled_output,
      ) + encoder_outputs[
          1:
      ]  # add hidden_states and attentions if they are here
      return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= message
      __UpperCamelCase       						= exit_layer  # start from 1!


class __lowerCAmelCase	(   nn.Module    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__()
      __UpperCamelCase       						= BertPooler(__UpperCAmelCase   )
      __UpperCamelCase       						= nn.Dropout(config.hidden_dropout_prob   )
      __UpperCamelCase       						= nn.Linear(config.hidden_size			,  config.num_labels   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= encoder_outputs[0]
      __UpperCamelCase       						= self.pooler(__UpperCAmelCase   )
      # "return" pooler_output

      # BertModel
      __UpperCamelCase       						= (pooler_input, pooler_output) + encoder_outputs[1:]
      # "return" bmodel_output

      # Dropout and classification
      __UpperCamelCase       						= bmodel_output[1]

      __UpperCamelCase       						= self.dropout(__UpperCAmelCase   )
      __UpperCamelCase       						= self.classifier(__UpperCAmelCase   )

      return logits, pooled_output


@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "    ,						__SCREAMING_SNAKE_CASE    ,						)
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase   )
      __UpperCamelCase       						= config.num_labels
      __UpperCamelCase       						= config.num_hidden_layers

      __UpperCamelCase       						= DeeBertModel(__UpperCAmelCase   )
      __UpperCamelCase       						= nn.Dropout(config.hidden_dropout_prob   )
      __UpperCamelCase       						= nn.Linear(config.hidden_size			,  self.config.num_labels   )

      self.init_weights()







     @add_start_docstrings_to_model_forward(__UpperCAmelCase   )
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=-1			,  __UpperCAmelCase=False			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_layers
      try:
       __UpperCamelCase       						= self.bert(
           __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase			,  position_ids=__UpperCAmelCase			,  head_mask=__UpperCAmelCase			,  inputs_embeds=__UpperCAmelCase			,  )
       # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

       __UpperCamelCase       						= outputs[1]

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

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

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

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

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



