


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 			UpperCAmelCase__						(UpperCamelCase_				):



     """simple docstring"""






     snake_case		 =				SwinaSRConfig()

     if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
          snake_case		 =				4
     elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
          snake_case		 =				4
          snake_case		 =				48
          snake_case		 =				'''pixelshuffle_aux'''
     elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
          snake_case		 =				[6, 6, 6, 6]
          snake_case		 =				60
          snake_case		 =				[6, 6, 6, 6]
          snake_case		 =				'''pixelshuffledirect'''
     elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
          snake_case		 =				4
          snake_case		 =				'''nearest+conv'''
     elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
          snake_case		 =				1
          snake_case		 =				1
          snake_case		 =				1_26
          snake_case		 =				7
          snake_case		 =				2_55.0
          snake_case		 =				''''''

     return config
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_				):



     """simple docstring"""






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

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

     if "conv_first" in name:
          snake_case		 =				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:
               snake_case		 =				name.replace('''conv_last'''							,'''final_convolution'''				)
          if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
               if "conv_before_upsample.0" in name:
                    snake_case		 =				name.replace('''conv_before_upsample.0'''							,'''conv_before_upsample'''				)
               if "upsample.0" in name:
                    snake_case		 =				name.replace('''upsample.0'''							,'''upsample.convolution_0'''				)
               if "upsample.2" in name:
                    snake_case		 =				name.replace('''upsample.2'''							,'''upsample.convolution_1'''				)
               snake_case		 =				'''upsample.''' + name
          elif config.upsampler == "pixelshuffledirect":
               snake_case		 =				name.replace('''upsample.0.weight'''							,'''upsample.conv.weight'''				)
               snake_case		 =				name.replace('''upsample.0.bias'''							,'''upsample.conv.bias'''				)
          else:
               pass
     else:
          snake_case		 =				'''swin2sr.''' + name

     return name
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_				):



     """simple docstring"""






     for key in orig_state_dict.copy().keys():
          snake_case		 =				orig_state_dict.pop(lowerCamelCase_				)

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

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

     return orig_state_dict
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_				):



     """simple docstring"""






     snake_case		 =				get_config(lowerCamelCase_				)
     snake_case		 =				SwinaSRForImageSuperResolution(lowerCamelCase_				)
     model.eval()

     snake_case		 =				torch.hub.load_state_dict_from_url(lowerCamelCase_							,map_location='''cpu'''				)
     snake_case		 =				convert_state_dict(lowerCamelCase_							,lowerCamelCase_				)
     snake_case ,  snake_case		 =				model.load_state_dict(lowerCamelCase_							,strict=lowerCamelCase_				)

     if len(lowerCamelCase_				) > 0:
          raise ValueError('''Missing keys when converting: {}'''.format(lowerCamelCase_				)				)
     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
     snake_case		 =				'''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
     snake_case		 =				Image.open(requests.get(lowerCamelCase_							,stream=lowerCamelCase_				).raw				).convert('''RGB'''				)
     snake_case		 =				SwinaSRImageProcessor()
     # pixel_values = processor(image, return_tensors="pt").pixel_values

     snake_case		 =				1_26 if '''Jpeg''' in checkpoint_url else 2_56
     snake_case		 =				Compose(
         [
             Resize((image_size, image_size)				),
             ToTensor(),
             Normalize(mean=[0.485, 0.456, 0.406]							,std=[0.229, 0.224, 0.225]				),
         ]				)
     snake_case		 =				transforms(lowerCamelCase_				).unsqueeze(0				)

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

     snake_case		 =				model(lowerCamelCase_				)

     # assert values
     if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
          snake_case		 =				torch.Size([1, 3, 5_12, 5_12]				)
          snake_case		 =				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:
          snake_case		 =				torch.Size([1, 3, 10_24, 10_24]				)
          snake_case		 =				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
          snake_case		 =				torch.Size([1, 3, 10_24, 10_24]				)
          snake_case		 =				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:
          snake_case		 =				torch.Size([1, 3, 5_12, 5_12]				)
          snake_case		 =				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:
          snake_case		 =				torch.Size([1, 3, 10_24, 10_24]				)
          snake_case		 =				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]							,lowerCamelCase_							,atol=1e-3				)
     print('''Looks ok!'''				)

     snake_case		 =				{
         '''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'''
         ),
     }
     snake_case		 =				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(lowerCamelCase_				)
          print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
          processor.save_pretrained(lowerCamelCase_				)

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE			: Union[str, 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.")

 _SCREAMING_SNAKE_CASE			: Any			      =    parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str=14,  lowerCamelCase						:    List[str]=7,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    List[str]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    List[str]=99,  lowerCamelCase						:    Optional[Any]=32,  lowerCamelCase						:    Optional[int]=5,  lowerCamelCase						:    List[Any]=4,  lowerCamelCase						:    List[Any]=37,  lowerCamelCase						:    List[str]="gelu",  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    List[Any]=512,  lowerCamelCase						:    List[str]=16,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    Union[str, Any]=0.02,  lowerCamelCase						:    Optional[int]=3,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    List[Any]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_token_type_ids
								lowercase__    =			use_input_mask
								lowercase__    =			use_labels
								lowercase__    =			use_mc_token_ids
								lowercase__    =			vocab_size
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			max_position_embeddings
								lowercase__    =			type_vocab_size
								lowercase__    =			type_sequence_label_size
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			num_choices
								lowercase__    =			scope
								lowercase__    =			self.vocab_size - 1




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__    =			None
								if self.use_input_mask:
												lowercase__    =			random_attention_mask([self.batch_size, self.seq_length]  )

								lowercase__    =			None
								if self.use_token_type_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.type_vocab_size  )

								lowercase__    =			None
								if self.use_mc_token_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.num_choices],  self.seq_length  )

								lowercase__    =			None
								lowercase__    =			None
								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__    =			ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__    =			self.get_config()

								lowercase__    =			ids_tensor([self.num_hidden_layers, self.num_attention_heads],  2  )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								return CTRLConfig(
								    vocab_size=self.vocab_size,  n_embd=self.hidden_size,  n_layer=self.num_hidden_layers,  n_head=self.num_attention_heads,  n_positions=self.max_position_embeddings,  pad_token_id=self.pad_token_id,  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Union[str, Any],  *lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			CTRLModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								model(lowerCamelCase,  token_type_ids=lowerCamelCase,  head_mask=lowerCamelCase  )
								model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )
								self.parent.assertEqual(len(result.past_key_values  ),  config.n_layer  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int],  *lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.loss.shape,  ()  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()

								(
								    (
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						
								)    =			config_and_inputs

								lowercase__    =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

								return config, inputs_dict




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    Any,  *lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			CTRLForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
				lowercase__											=       (CTRLLMHeadModel,) if is_torch_available() else ()
				lowercase__											=       (
				    {
				        """feature-extraction""": CTRLModel,
				        """text-classification""": CTRLForSequenceClassification,
				        """text-generation""": CTRLLMHeadModel,
				        """zero-shot""": CTRLForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__											=       True
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
												# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
												# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
												# config could not be created.
												return True

								return False




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  n_embd=37  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCamelCase  )




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			CTRLModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




				@unittest.skip('''The model doesn\'t support left padding'''  )  # and it's not used enough to be worth fixing :)
				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								pass



@require_torch
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				@slow
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel.from_pretrained('''ctrl'''  )
								model.to(lowerCamelCase  )
								lowercase__    =			torch.tensor(
								    [[11_859, 0, 1_611, 8]],  dtype=torch.long,  device=lowerCamelCase  )  # Legal the president is
								lowercase__    =			[
								    11_859,
								    0,
								    1_611,
								    8,
								    5,
								    150,
								    26_449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2_595,
								    48,
								    20_740,
								    246_533,
								    246_533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								lowercase__    =			model.generate(lowerCamelCase,  do_sample=lowerCamelCase  )
								self.assertListEqual(output_ids[0].tolist(),  lowerCamelCase  )




