





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__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Union[str, Any]:



					'''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													=			126
										_snake_case													=			7
										_snake_case													=			255.0
										_snake_case													=			''

					return config





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''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   lowerCamelCase__       (       UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	Optional[int]:



					'''simple docstring'''




					for key in orig_state_dict.copy().keys():
										_snake_case													=			orig_state_dict.pop(UpperCamelCase__  )

										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   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    Union[str, Any]  )     ->	int:



					'''simple docstring'''




					_snake_case													=			get_config(UpperCamelCase__  )
					_snake_case													=			SwinaSRForImageSuperResolution(UpperCamelCase__  )
					model.eval()

					_snake_case													=			torch.hub.load_state_dict_from_url(UpperCamelCase__    ,      map_location='cpu'  )
					_snake_case													=			convert_state_dict(UpperCamelCase__    ,      UpperCamelCase__  )
					_snake_case							,       _snake_case													=			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
					_snake_case													=			'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
					_snake_case													=			Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__  ).raw  ).convert('RGB'  )
					_snake_case													=			SwinaSRImageProcessor()
					# pixel_values = processor(image, return_tensors="pt").pixel_values

					_snake_case													=			126 if 'Jpeg' in checkpoint_url else 256
					_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(UpperCamelCase__  ).unsqueeze(0  )

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

					_snake_case													=			model(UpperCamelCase__  )

					# assert values
					if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
										_snake_case													=			torch.Size([1, 3, 512, 512]  )
										_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, 1_024, 1_024]  )
										_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, 1_024, 1_024]  )
										_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, 512, 512]  )
										_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, 1_024, 1_024]  )
										_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]    ,      UpperCamelCase__    ,      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(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_ 							=       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_ 							=       parser.parse_args()
					convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class      UpperCamelCase_						(					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		LEDTokenizer
					lowerCAmelCase_							=		LEDTokenizerFast
					lowerCAmelCase_							=		True




					def 		lowerCAmelCase     (						self					)						->							List[str]:
										super().setUp()
										_snake_case													=			[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										_snake_case													=			dict(zip(lowerCAmelCase_			,  range(len(lowerCAmelCase_					)					)					)					)
										_snake_case													=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										_snake_case													=			{'unk_token': '<unk>'}

										_snake_case													=			os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES['vocab_file']					)
										_snake_case													=			os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES['merges_file']					)
										with open(self.vocab_file			,  'w'			,  encoding='utf-8'					) as fp:
															fp.write(json.dumps(lowerCAmelCase_					) + '\n'					)
										with open(self.merges_file			,  'w'			,  encoding='utf-8'					) as fp:
															fp.write('\n'.join(lowerCAmelCase_					)					)




					def 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							List[str]:
										kwargs.update(self.special_tokens_map					)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							str:
										kwargs.update(self.special_tokens_map					)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										return "lower newer", "lower newer"




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										return LEDTokenizer.from_pretrained('allenai/led-base-16384'					)




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
										_snake_case													=			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(lowerCAmelCase_			,  max_length=len(lowerCAmelCase_					)			,  padding=lowerCAmelCase_			,  return_tensors='pt'					)
															self.assertIsInstance(lowerCAmelCase_			,  lowerCAmelCase_					)

															self.assertEqual((2, 9)			,  batch.input_ids.shape					)
															self.assertEqual((2, 9)			,  batch.attention_mask.shape					)
															_snake_case													=			batch.input_ids.tolist()[0]
															self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(lowerCAmelCase_			,  padding=lowerCAmelCase_			,  return_tensors='pt'					)
															self.assertIn('input_ids'			,  lowerCAmelCase_					)
															self.assertIn('attention_mask'			,  lowerCAmelCase_					)
															self.assertNotIn('labels'			,  lowerCAmelCase_					)
															self.assertNotIn('decoder_attention_mask'			,  lowerCAmelCase_					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										_snake_case													=			[
										    'Summary of the text.',
										    'Another summary.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(text_target=lowerCAmelCase_			,  max_length=32			,  padding='max_length'			,  return_tensors='pt'					)
															self.assertEqual(32			,  targets['input_ids'].shape[1]					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(
															    ['I am a small frog' * 1024, 'I am a small frog']			,  padding=lowerCAmelCase_			,  truncation=lowerCAmelCase_			,  return_tensors='pt'					)
															self.assertIsInstance(lowerCAmelCase_			,  lowerCAmelCase_					)
															self.assertEqual(batch.input_ids.shape			,  (2, 5122)					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			['A long paragraph for summarization.']
										_snake_case													=			[
										    'Summary of the text.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(lowerCAmelCase_			,  return_tensors='pt'					)
															_snake_case													=			tokenizer(text_target=lowerCAmelCase_			,  return_tensors='pt'					)
															_snake_case													=			inputs['input_ids']
															_snake_case													=			targets['input_ids']
															self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
															self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
															self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
															self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			['Summary of the text.', 'Another summary.']
															_snake_case													=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

															_snake_case													=			tokenizer(lowerCAmelCase_			,  padding=lowerCAmelCase_					)
															_snake_case													=			[[0] * len(lowerCAmelCase_					) for x in encoded_output['input_ids']]
															_snake_case													=			tokenizer.pad(lowerCAmelCase_					)
															self.assertSequenceEqual(outputs['global_attention_mask']			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										pass






					def 		lowerCAmelCase     (						self					)						->							str:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																				_snake_case													=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)
																				_snake_case													=			self.tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)
																				_snake_case													=			'A, <mask> AllenNLP sentence.'
																				_snake_case													=			tokenizer_r.encode_plus(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  return_token_type_ids=lowerCAmelCase_					)
																				_snake_case													=			tokenizer_p.encode_plus(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  return_token_type_ids=lowerCAmelCase_					)
																				self.assertEqual(sum(tokens_r['token_type_ids']					)			,  sum(tokens_p['token_type_ids']					)					)
																				self.assertEqual(
																				    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)			,  sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)			,  )

																				_snake_case													=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
																				_snake_case													=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)
																				self.assertSequenceEqual(tokens_p['input_ids']			,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
																				self.assertSequenceEqual(tokens_r['input_ids']			,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

																				self.assertSequenceEqual(
																				    lowerCAmelCase_			,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
																				self.assertSequenceEqual(
																				    lowerCAmelCase_			,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)



