



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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]						)	->    List[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        __lowerCAmelCase		(	__lowerCamelCase						:     Dict					, __lowerCamelCase						:     Dict						)	->    List[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        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Any						)	->    Optional[int]:


					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase						 =orig_state_dict.pop(_A						)

										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        __lowerCAmelCase		(	__lowerCamelCase						:     Dict					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]						)	->    Union[str, Any]:
					__lowerCAmelCase						 =get_config(_A						)
					__lowerCAmelCase						 =SwinaSRForImageSuperResolution(_A						)
					model.eval()

					__lowerCAmelCase						 =torch.hub.load_state_dict_from_url(_A					, map_location="""cpu"""						)
					__lowerCAmelCase						 =convert_state_dict(_A					, _A						)
					__lowerCAmelCase ,						__lowerCAmelCase						 =model.load_state_dict(_A					, strict=_A						)

					if len(_A						) > 0:
										raise ValueError("""Missing keys when converting: {}""".format(_A						)						)
					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(_A					, stream=_A						).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(_A						).unsqueeze(0						)

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

					__lowerCAmelCase						 =model(_A						)

					# 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]					, _A					, 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(_A						)
										print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
										processor.save_pretrained(_A						)

					if push_to_hub:
										model.push_to_hub(f"""caidas/{model_name}"""						)
										processor.push_to_hub(f"""caidas/{model_name}"""						)


if __name__ == "__main__":
				lowercase_													=      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.''')

				lowercase_													=      parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE			:						Any				  =		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizer
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizerFast
  _SCREAMING_SNAKE_CASE     					=      True
  _SCREAMING_SNAKE_CASE     					=      True

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"<pad>"
   __a							=	1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,				"<s>NOTUSED"							)
   self.assertEqual(vocab_keys[1]  ,				"<pad>"							)
   self.assertEqual(vocab_keys[-1]  ,				"<mask>"							)
   self.assertEqual(len(__SCREAMING_SNAKE_CASE							)  ,				10_04							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,				10_05							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)
   __a							=	CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   # <unk> tokens are not the same for `rust` than for `slow`.
   # Because spm gives back raw token instead of `unk` in EncodeAsPieces
   # tokens = tokenizer.tokenize(sequence)
   __a							=	tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   if not self.test_rust_tokenizer:
    return

   __a							=	self.get_tokenizer()
   __a							=	self.get_rust_tokenizer()

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	self.get_rust_tokenizer()
   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   # fmt: off
   __a							=	{"input_ids": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # camembert is a french model. So we also use french texts.
   __a							=	[
       "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
       "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
       "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
       "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
       "telles que la traduction et la synthèse de texte.",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=__SCREAMING_SNAKE_CASE  ,				model_name="camembert-base"  ,				revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"  ,				sequences=__SCREAMING_SNAKE_CASE  ,				)


