'''simple docstring'''
import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  SwinConfig()
    __UpperCamelCase	:	List[Any]		    =  swin_name.split('_'			)

    __UpperCamelCase	:	Optional[Any]		    =  name_split[1]
    __UpperCamelCase	:	Tuple		    =  int(name_split[4]			)
    __UpperCamelCase	:	Tuple		    =  int(name_split[3][-1]			)

    if model_size == "tiny":
        __UpperCamelCase	:	Union[str, Any]		    =  9_6
        __UpperCamelCase	:	List[Any]		    =  (2, 2, 6, 2)
        __UpperCamelCase	:	int		    =  (3, 6, 1_2, 2_4)
    elif model_size == "small":
        __UpperCamelCase	:	Dict		    =  9_6
        __UpperCamelCase	:	List[str]		    =  (2, 2, 1_8, 2)
        __UpperCamelCase	:	List[str]		    =  (3, 6, 1_2, 2_4)
    elif model_size == "base":
        __UpperCamelCase	:	Tuple		    =  1_2_8
        __UpperCamelCase	:	Optional[Any]		    =  (2, 2, 1_8, 2)
        __UpperCamelCase	:	Optional[int]		    =  (4, 8, 1_6, 3_2)
    else:
        __UpperCamelCase	:	Optional[int]		    =  1_9_2
        __UpperCamelCase	:	str		    =  (2, 2, 1_8, 2)
        __UpperCamelCase	:	Optional[Any]		    =  (6, 1_2, 2_4, 4_8)

    if "in22k" in swin_name:
        __UpperCamelCase	:	Optional[Any]		    =  2_1_8_4_1
    else:
        __UpperCamelCase	:	Optional[Any]		    =  1_0_0_0
        __UpperCamelCase	:	List[Any]		    =  'huggingface/label-files'
        __UpperCamelCase	:	int		    =  'imagenet-1k-id2label.json'
        __UpperCamelCase	:	Optional[int]		    =  json.load(open(hf_hub_download(_a				,     _a				,     repo_type='dataset'			)				,     'r'			)			)
        __UpperCamelCase	:	Union[str, Any]		    =  {int(_a			): v for k, v in idalabel.items()}
        __UpperCamelCase	:	int		    =  idalabel
        __UpperCamelCase	:	Dict		    =  {v: k for k, v in idalabel.items()}

    __UpperCamelCase	:	Union[str, Any]		    =  img_size
    __UpperCamelCase	:	List[Any]		    =  num_classes
    __UpperCamelCase	:	str		    =  embed_dim
    __UpperCamelCase	:	Union[str, Any]		    =  depths
    __UpperCamelCase	:	Tuple		    =  num_heads
    __UpperCamelCase	:	Optional[Any]		    =  window_size

    return config







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if "patch_embed.proj" in name:
        __UpperCamelCase	:	Union[str, Any]		    =  name.replace('patch_embed.proj'				,     'embeddings.patch_embeddings.projection'			)
    if "patch_embed.norm" in name:
        __UpperCamelCase	:	Optional[int]		    =  name.replace('patch_embed.norm'				,     'embeddings.norm'			)
    if "layers" in name:
        __UpperCamelCase	:	List[str]		    =  'encoder.' + name
    if "attn.proj" in name:
        __UpperCamelCase	:	Union[str, Any]		    =  name.replace('attn.proj'				,     'attention.output.dense'			)
    if "attn" in name:
        __UpperCamelCase	:	List[Any]		    =  name.replace('attn'				,     'attention.self'			)
    if "norm1" in name:
        __UpperCamelCase	:	Optional[Any]		    =  name.replace('norm1'				,     'layernorm_before'			)
    if "norm2" in name:
        __UpperCamelCase	:	Any		    =  name.replace('norm2'				,     'layernorm_after'			)
    if "mlp.fc1" in name:
        __UpperCamelCase	:	Tuple		    =  name.replace('mlp.fc1'				,     'intermediate.dense'			)
    if "mlp.fc2" in name:
        __UpperCamelCase	:	List[Any]		    =  name.replace('mlp.fc2'				,     'output.dense'			)

    if name == "norm.weight":
        __UpperCamelCase	:	Any		    =  'layernorm.weight'
    if name == "norm.bias":
        __UpperCamelCase	:	List[str]		    =  'layernorm.bias'

    if "head" in name:
        __UpperCamelCase	:	Optional[int]		    =  name.replace('head'				,     'classifier'			)
    else:
        __UpperCamelCase	:	Any		    =  'swin.' + name

    return name







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        __UpperCamelCase	:	Optional[int]		    =  orig_state_dict.pop(_a			)

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

            if "weight" in key:
                __UpperCamelCase	:	Union[str, Any]		    =  val[:dim, :]
                __UpperCamelCase	:	Optional[int]		    =  val[
                    dim : dim * 2, :
                ]
                __UpperCamelCase	:	List[str]		    =  val[-dim:, :]
            else:
                __UpperCamelCase	:	int		    =  val[
                    :dim
                ]
                __UpperCamelCase	:	Optional[int]		    =  val[
                    dim : dim * 2
                ]
                __UpperCamelCase	:	List[str]		    =  val[
                    -dim:
                ]
        else:
            __UpperCamelCase	:	List[str]		    =  val

    return orig_state_dict







def  _UpperCamelCase (     _a	:	List[str]				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  timm.create_model(_a				,     pretrained=_a			)
    timm_model.eval()

    __UpperCamelCase	:	Dict		    =  get_swin_config(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  SwinForImageClassification(_a			)
    model.eval()

    __UpperCamelCase	:	Optional[int]		    =  convert_state_dict(timm_model.state_dict()				,     _a			)
    model.load_state_dict(_a			)

    __UpperCamelCase	:	Optional[Any]		    =  'http://images.cocodataset.org/val2017/000000039769.jpg'

    __UpperCamelCase	:	Dict		    =  AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'				,     '-'			)			)			)
    __UpperCamelCase	:	Union[str, Any]		    =  Image.open(requests.get(_a				,     stream=_a			).raw			)
    __UpperCamelCase	:	int		    =  image_processor(images=_a				,     return_tensors='pt'			)

    __UpperCamelCase	:	Tuple		    =  timm_model(inputs['pixel_values']			)
    __UpperCamelCase	:	Optional[Any]		    =  model(**_a			).logits

    assert torch.allclose(_a				,     _a				,     atol=1E-3			)

    print(f"""Saving model {swin_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__":
  a=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--swin_name''',
      default='''swin_tiny_patch4_window7_224''',
      type=str,
      help='''Name of the Swin 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.'''
  )

  a=  parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




'''simple docstring'''
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCamelCase (     _a	:	int				,     _a	:	Tuple				,     _a	:	Dict=None			):




    """simple docstring"""



    assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
    __UpperCamelCase	:	List[Any]		    =  nn.Parameter(_a			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
        __UpperCamelCase	:	Dict		    =  nn.Parameter(_a			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	str				,     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	str		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[2]			)

    set_param(
        torch_layer.self_attention.query_key				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.value				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.output.dense				,     torch.tensor(_a			).view(-1				,     _a			).contiguous().transpose(0				,     1			)				,     )







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[2]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[3]			)

    set_param(
        torch_layer.self_attention.query				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.key				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.value				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.output.dense				,     torch.tensor(_a			).view(-1				,     _a			).contiguous().transpose(0				,     1			)				,     )







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  weights[0][0][0]
    __UpperCamelCase	:	Tuple		    =  np.asarray(layer_norm_a[0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(layer_norm_a[1]			)
    set_param(
        torch_block.attention.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # lsh weights + output
    __UpperCamelCase	:	int		    =  weights[0][1]
    if len(_a			) < 4:
        set_layer_weights_in_torch_lsh(_a				,     torch_block.attention				,     _a			)
    else:
        set_layer_weights_in_torch_local(_a				,     torch_block.attention				,     _a			)

    # intermediate weighs
    __UpperCamelCase	:	int		    =  weights[2][0][1][2]

    # Chunked Feed Forward
    if len(_a			) == 4:
        __UpperCamelCase	:	Optional[Any]		    =  intermediate_weights[2]

    # layernorm 2
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[0][0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(intermediate_weights[0][1]			)
    set_param(
        torch_block.feed_forward.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # intermediate dense
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(intermediate_weights[1][0]			)
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[1][1]			)
    set_param(
        torch_block.feed_forward.dense.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )

    # intermediate out
    __UpperCamelCase	:	Dict		    =  np.asarray(intermediate_weights[4][0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(intermediate_weights[4][1]			)
    set_param(
        torch_block.feed_forward.output.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	int				,     _a	:	str				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch_model.reformer

    # word embeds
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    set_param(
        torch_model_reformer.embeddings.word_embeddings				,     torch.tensor(_a			)				,     )

    if isinstance(weights[3]				,     _a			):
        __UpperCamelCase	:	Dict		    =  torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights			)			):
            __UpperCamelCase	:	str		    =  np.asarray(weights[3][emb_idx][0]			)
            assert (
                position_embeddings.weights[emb_idx].shape == emb_weights.shape
            ), f"""{position_embeddings[emb_idx]} emb does not match"""
            __UpperCamelCase	:	Tuple		    =  nn.Parameter(torch.tensor(_a			)			)

    __UpperCamelCase	:	int		    =  weights[5]
    assert len(torch_model_reformer.encoder.layers			) * 4 == len(
        _a			), "HF and trax model do not have the same number of layers"
    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
        __UpperCamelCase	:	Any		    =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(_a				,     _a				,     _a			)

    # output layer norm
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(weights[7][0]			)
    __UpperCamelCase	:	Any		    =  np.asarray(weights[7][1]			)
    set_param(
        torch_model_reformer.encoder.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # output embeddings
    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[9][0]			)
    __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(weights[9][1]			)
    set_param(
        torch_model.lm_head.decoder				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  ReformerConfig.from_json_file(_a			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    __UpperCamelCase	:	List[str]		    =  ReformerModelWithLMHead(_a			)

    with open(_a				,     'rb'			) as f:
        __UpperCamelCase	:	Tuple		    =  pickle.load(_a			)['weights']

    set_model_weights_in_torch(_a				,     _a				,     config.hidden_size			)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    torch.save(model.state_dict()				,     _a			)


if __name__ == "__main__":
  a=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained Reformer model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  a=  parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



