





"""simple docstring"""
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class       a							(   a__   , a__   , a__  ):
 snake_case__             =       [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']



 @register_to_config
 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case = None				,       _snake_case = 5_02_57				,       _snake_case = 10_24				,       _snake_case = 7_68				,       _snake_case = 12				,       _snake_case = 12				,       _snake_case = None				,       _snake_case = "gelu_new"				,       _snake_case = 0.1				,       _snake_case = 0.1				,       _snake_case = 0.1				,       _snake_case = 1E-5				,       _snake_case = 0.02				,       _snake_case = True				,       _snake_case = True				,       _snake_case = False				,       _snake_case = False				,       ):







  """simple docstring"""






  super().__init__()

  lowerCAmelCase    				= prefix_length

  if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
   raise ValueError(
       F'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
       F' `n_embd`: {n_embd} are not equal.'     )

  lowerCAmelCase    				= prefix_inner_dim
  lowerCAmelCase    				= prefix_hidden_dim

  lowerCAmelCase    				= (
      nn.Linear(self.prefix_inner_dim				,       self.prefix_hidden_dim     )
      if self.prefix_hidden_dim is not None
      else nn.Identity()
  )
  lowerCAmelCase    				= (
      nn.Linear(self.prefix_hidden_dim				,       _snake_case     ) if self.prefix_hidden_dim is not None else nn.Identity()
  )

  lowerCAmelCase    				= GPTaConfig(
      vocab_size=_snake_case				,       n_positions=_snake_case				,       n_embd=_snake_case				,       n_layer=_snake_case				,       n_head=_snake_case				,       n_inner=_snake_case				,       activation_function=_snake_case				,       resid_pdrop=_snake_case				,       embd_pdrop=_snake_case				,       attn_pdrop=_snake_case				,       layer_norm_epsilon=_snake_case				,       initializer_range=_snake_case				,       scale_attn_weights=_snake_case				,       use_cache=_snake_case				,       scale_attn_by_inverse_layer_idx=_snake_case				,       reorder_and_upcast_attn=_snake_case				,       )
  lowerCAmelCase    				= GPTaLMHeadModel(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case = None				,       _snake_case = None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= self.transformer.transformer.wte(_snake_case     )
  lowerCAmelCase    				= self.encode_prefix(_snake_case     )
  lowerCAmelCase    				= self.decode_prefix(_snake_case     )
  lowerCAmelCase    				= torch.cat((prefix_embeds, embedding_text)				,       dim=1     )

  if labels is not None:
   lowerCAmelCase    				= self.get_dummy_token(input_ids.shape[0]				,       input_ids.device     )
   lowerCAmelCase    				= torch.cat((dummy_token, input_ids)				,       dim=1     )
  lowerCAmelCase    				= self.transformer(inputs_embeds=_snake_case				,       labels=_snake_case				,       attention_mask=_snake_case     )
  if self.prefix_hidden_dim is not None:
   return out, hidden
  else:
   return out



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  return torch.zeros(_snake_case				,       self.prefix_length				,       dtype=torch.intaa				,       device=_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.encode_prefix(_snake_case     )



 @torch.no_grad()
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= torch.split(_snake_case				,       1				,       dim=0     )
  lowerCAmelCase    				= []
  lowerCAmelCase    				= []
  for feature in features:
   lowerCAmelCase    				= self.decode_prefix(feature.to(_snake_case     )     )  # back to the clip feature
   # Only support beam search for now
   lowerCAmelCase				,lowerCAmelCase    				= self.generate_beam(
       input_embeds=_snake_case				,       device=_snake_case				,       eos_token_id=_snake_case     )
   generated_tokens.append(output_tokens[0]     )
   generated_seq_lengths.append(seq_lengths[0]     )
  lowerCAmelCase    				= torch.stack(_snake_case     )
  lowerCAmelCase    				= torch.stack(_snake_case     )
  return generated_tokens, generated_seq_lengths



 @torch.no_grad()
 def    UpperCamelCase__  (     self				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case = 5				,       _snake_case = 67				,       _snake_case = 1.0				,       _snake_case = None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= eos_token_id
  lowerCAmelCase    				= None
  lowerCAmelCase    				= None
  lowerCAmelCase    				= torch.ones(_snake_case				,       device=_snake_case				,       dtype=torch.int     )
  lowerCAmelCase    				= torch.zeros(_snake_case				,       device=_snake_case				,       dtype=torch.bool     )

  if input_embeds is not None:
   lowerCAmelCase    				= input_embeds
  else:
   lowerCAmelCase    				= self.transformer.transformer.wte(_snake_case     )

  for i in range(_snake_case     ):
   lowerCAmelCase    				= self.transformer(inputs_embeds=_snake_case     )
   lowerCAmelCase    				= outputs.logits
   lowerCAmelCase    				= logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
   lowerCAmelCase    				= logits.softmax(-1     ).log()

   if scores is None:
    lowerCAmelCase				,lowerCAmelCase    				= logits.topk(_snake_case				,       -1     )
    lowerCAmelCase    				= generated.expand(_snake_case				,       *generated.shape[1:]     )
    lowerCAmelCase				,lowerCAmelCase    				= next_tokens.permute(1				,       0     ), scores.squeeze(0     )
    if tokens is None:
     lowerCAmelCase    				= next_tokens
    else:
     lowerCAmelCase    				= tokens.expand(_snake_case				,       *tokens.shape[1:]     )
     lowerCAmelCase    				= torch.cat((tokens, next_tokens)				,       dim=1     )
   else:
    lowerCAmelCase    				= -float(np.inf     )
    lowerCAmelCase    				= 0
    lowerCAmelCase    				= scores[:, None] + logits
    seq_lengths[~is_stopped] += 1
    lowerCAmelCase    				= scores_sum / seq_lengths[:, None]
    lowerCAmelCase				,lowerCAmelCase    				= scores_sum_average.view(-1     ).topk(_snake_case				,       -1     )
    lowerCAmelCase    				= next_tokens // scores_sum.shape[1]
    lowerCAmelCase    				= seq_lengths[next_tokens_source]
    lowerCAmelCase    				= next_tokens % scores_sum.shape[1]
    lowerCAmelCase    				= next_tokens.unsqueeze(1     )
    lowerCAmelCase    				= tokens[next_tokens_source]
    lowerCAmelCase    				= torch.cat((tokens, next_tokens)				,       dim=1     )
    lowerCAmelCase    				= generated[next_tokens_source]
    lowerCAmelCase    				= scores_sum_average * seq_lengths
    lowerCAmelCase    				= is_stopped[next_tokens_source]

   lowerCAmelCase    				= self.transformer.transformer.wte(next_tokens.squeeze()     ).view(generated.shape[0]				,       1				,       -1     )
   lowerCAmelCase    				= torch.cat((generated, next_token_embed)				,       dim=1     )
   lowerCAmelCase    				= is_stopped + next_tokens.eq(_snake_case     ).squeeze()
   if is_stopped.all():
    break

  lowerCAmelCase    				= scores / seq_lengths
  lowerCAmelCase    				= scores.argsort(descending=_snake_case     )
  # tokens tensors are already padded to max_seq_length
  lowerCAmelCase    				= [tokens[i] for i in order]
  lowerCAmelCase    				= torch.stack(_snake_case				,       dim=0     )
  lowerCAmelCase    				= torch.tensor([seq_lengths[i] for i in order]				,       dtype=seq_lengths.dtype     )
  return output_texts, seq_lengths












"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any			):
 lowerCAmelCase    				= SwinaSRConfig()

 if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
  lowerCAmelCase    				= 4
 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 48
  lowerCAmelCase    				= 'pixelshuffle_aux'
 elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
  lowerCAmelCase    				= [6, 6, 6, 6]
  lowerCAmelCase    				= 60
  lowerCAmelCase    				= [6, 6, 6, 6]
  lowerCAmelCase    				= 'pixelshuffledirect'
 elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 'nearest+conv'
 elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
  lowerCAmelCase    				= 1
  lowerCAmelCase    				= 1
  lowerCAmelCase    				= 126
  lowerCAmelCase    				= 7
  lowerCAmelCase    				= 255.0
  lowerCAmelCase    				= ''

 return config

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Union[str, Any]			):
 if "patch_embed.proj" in name and "layers" not in name:
  lowerCAmelCase    				= name.replace('patch_embed.proj'  ,     'embeddings.patch_embeddings.projection'			)
 if "patch_embed.norm" in name:
  lowerCAmelCase    				= name.replace('patch_embed.norm'  ,     'embeddings.patch_embeddings.layernorm'			)
 if "layers" in name:
  lowerCAmelCase    				= name.replace('layers'  ,     'encoder.stages'			)
 if "residual_group.blocks" in name:
  lowerCAmelCase    				= name.replace('residual_group.blocks'  ,     'layers'			)
 if "attn.proj" in name:
  lowerCAmelCase    				= name.replace('attn.proj'  ,     'attention.output.dense'			)
 if "attn" in name:
  lowerCAmelCase    				= name.replace('attn'  ,     'attention.self'			)
 if "norm1" in name:
  lowerCAmelCase    				= name.replace('norm1'  ,     'layernorm_before'			)
 if "norm2" in name:
  lowerCAmelCase    				= name.replace('norm2'  ,     'layernorm_after'			)
 if "mlp.fc1" in name:
  lowerCAmelCase    				= name.replace('mlp.fc1'  ,     'intermediate.dense'			)
 if "mlp.fc2" in name:
  lowerCAmelCase    				= name.replace('mlp.fc2'  ,     'output.dense'			)
 if "q_bias" in name:
  lowerCAmelCase    				= name.replace('q_bias'  ,     'query.bias'			)
 if "k_bias" in name:
  lowerCAmelCase    				= name.replace('k_bias'  ,     'key.bias'			)
 if "v_bias" in name:
  lowerCAmelCase    				= name.replace('v_bias'  ,     'value.bias'			)
 if "cpb_mlp" in name:
  lowerCAmelCase    				= name.replace('cpb_mlp'  ,     'continuous_position_bias_mlp'			)
 if "patch_embed.proj" in name:
  lowerCAmelCase    				= name.replace('patch_embed.proj'  ,     'patch_embed.projection'			)

 if name == "norm.weight":
  lowerCAmelCase    				= 'layernorm.weight'
 if name == "norm.bias":
  lowerCAmelCase    				= 'layernorm.bias'

 if "conv_first" in name:
  lowerCAmelCase    				= name.replace('conv_first'  ,     'first_convolution'			)

 if (
     "upsample" in name
     or "conv_before_upsample" in name
     or "conv_bicubic" in name
     or "conv_up" in name
     or "conv_hr" in name
     or "conv_last" in name
     or "aux" in name
 ):
  # heads
  if "conv_last" in name:
   lowerCAmelCase    				= name.replace('conv_last'  ,     'final_convolution'			)
  if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
   if "conv_before_upsample.0" in name:
    lowerCAmelCase    				= name.replace('conv_before_upsample.0'  ,     'conv_before_upsample'			)
   if "upsample.0" in name:
    lowerCAmelCase    				= name.replace('upsample.0'  ,     'upsample.convolution_0'			)
   if "upsample.2" in name:
    lowerCAmelCase    				= name.replace('upsample.2'  ,     'upsample.convolution_1'			)
   lowerCAmelCase    				= 'upsample.' + name
  elif config.upsampler == "pixelshuffledirect":
   lowerCAmelCase    				= name.replace('upsample.0.weight'  ,     'upsample.conv.weight'			)
   lowerCAmelCase    				= name.replace('upsample.0.bias'  ,     'upsample.conv.bias'			)
  else:
   pass
 else:
  lowerCAmelCase    				= 'swin2sr.' + name

 return name

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Dict			):
 for key in orig_state_dict.copy().keys():
  lowerCAmelCase    				= orig_state_dict.pop(_UpperCAmelCase			)

  if "qkv" in key:
   lowerCAmelCase    				= key.split('.'			)
   lowerCAmelCase    				= int(key_split[1]			)
   lowerCAmelCase    				= int(key_split[4]			)
   lowerCAmelCase    				= config.embed_dim

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

 return orig_state_dict

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Tuple			):
 lowerCAmelCase    				= get_config(_UpperCAmelCase			)
 lowerCAmelCase    				= SwinaSRForImageSuperResolution(_UpperCAmelCase			)
 model.eval()

 lowerCAmelCase    				= torch.hub.load_state_dict_from_url(_UpperCAmelCase  ,     map_location='cpu'			)
 lowerCAmelCase    				= convert_state_dict(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase				,lowerCAmelCase    				= model.load_state_dict(_UpperCAmelCase  ,     strict=_UpperCAmelCase			)

 if len(_UpperCAmelCase			) > 0:
  raise ValueError('Missing keys when converting: {}'.format(_UpperCAmelCase			)			)
 for key in unexpected_keys:
  if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
   raise ValueError(F'Unexpected key {key} in state_dict'			)

    # verify values
 lowerCAmelCase    				= 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
 lowerCAmelCase    				= Image.open(requests.get(_UpperCAmelCase  ,     stream=_UpperCAmelCase			).raw			).convert('RGB'			)
 lowerCAmelCase    				= SwinaSRImageProcessor()
 # pixel_values = processor(image, return_tensors="pt").pixel_values

 lowerCAmelCase    				= 126 if 'Jpeg' in checkpoint_url else 256
 lowerCAmelCase    				= Compose(
     [
         Resize((image_size, image_size)			),
         ToTensor(),
         Normalize(mean=[0.485, 0.456, 0.406]  ,     std=[0.229, 0.224, 0.225]			),
     ]			)
 lowerCAmelCase    				= transforms(_UpperCAmelCase			).unsqueeze(0			)

 if config.num_channels == 1:
  lowerCAmelCase    				= pixel_values[:, 0, :, :].unsqueeze(1			)

 lowerCAmelCase    				= model(_UpperCAmelCase			)

 # assert values
 if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]			)
 elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]			)
 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
  # TODO values didn't match exactly here
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]			)
 elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]			)
 elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= torch.tensor(
      [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]			)

 assert (
     outputs.reconstruction.shape == expected_shape
 ), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
 assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,     _UpperCAmelCase  ,     atol=1e-3			)
 print('Looks ok!'			)

 lowerCAmelCase    				= {
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
         'swin2SR-classical-sr-x2-64'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
         'swin2SR-classical-sr-x4-64'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
         'swin2SR-compressed-sr-x4-48'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
         'swin2SR-lightweight-x2-64'
     ),
     'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
         'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
     ),
 }
 lowerCAmelCase    				= url_to_name[checkpoint_url]

 if pytorch_dump_folder_path is not None:
  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}'			)
  processor.save_pretrained(_UpperCAmelCase			)

 if push_to_hub:
  model.push_to_hub(F'caidas/{model_name}'			)
  processor.push_to_hub(F'caidas/{model_name}'			)


if __name__ == "__main__":
       __UpperCamelCase     :						Any						       =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
           type=str,
           help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )
       parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

       __UpperCamelCase     :						Optional[int]						       =  parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





