



'''simple docstring'''

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	Any     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	List[str]=99     ,  UpperCamelCase_				:	Optional[int]=13     ,  UpperCamelCase_				:	str=7     ,  UpperCamelCase_				:	str=9     ,  UpperCamelCase_				:	str=True     ,  UpperCamelCase_				:	Optional[int]=True     ,  UpperCamelCase_				:	str=False     ,  UpperCamelCase_				:	Dict=32     ,  UpperCamelCase_				:	Dict=5     ,  UpperCamelCase_				:	int=4     ,  UpperCamelCase_				:	Optional[int]=37     ,  UpperCamelCase_				:	Tuple=8     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	List[str]=0.0_02     ,  UpperCamelCase_				:	List[str]=1     ,  UpperCamelCase_				:	Tuple=0     ,  UpperCamelCase_				:	Optional[int]=0     ,  UpperCamelCase_				:	Optional[int]=None     ,  UpperCamelCase_				:	List[str]=None     ,  )  ->       Any:



						'''simple docstring'''





						_lowercase					:       int       =		parent
						_lowercase					:       Optional[int]       =		batch_size
						_lowercase					:       Dict       =		encoder_seq_length
						_lowercase					:       Union[str, Any]       =		decoder_seq_length
						# For common tests
						_lowercase					:       int       =		self.decoder_seq_length
						_lowercase					:       Tuple       =		is_training
						_lowercase					:       Optional[int]       =		use_attention_mask
						_lowercase					:       List[str]       =		use_labels
						_lowercase					:       int       =		vocab_size
						_lowercase					:       Union[str, Any]       =		hidden_size
						_lowercase					:       int       =		num_hidden_layers
						_lowercase					:       int       =		num_attention_heads
						_lowercase					:       Union[str, Any]       =		d_ff
						_lowercase					:       Tuple       =		relative_attention_num_buckets
						_lowercase					:       List[Any]       =		dropout_rate
						_lowercase					:       Dict       =		initializer_factor
						_lowercase					:       Optional[Any]       =		eos_token_id
						_lowercase					:       str       =		pad_token_id
						_lowercase					:       int       =		decoder_start_token_id
						_lowercase					:       Dict       =		None
						_lowercase					:       List[str]       =		decoder_layers




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Any:



						'''simple docstring'''





						return TaConfig.from_pretrained('google/umt5-base'					)




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Optional[int]=None     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	Optional[int]=None     ,  )  ->       List[str]:



						'''simple docstring'''





						if attention_mask is None:
							_lowercase					:       Any       =		input_ids.ne(config.pad_token_id					)
						if decoder_attention_mask is None:
							_lowercase					:       List[str]       =		decoder_input_ids.ne(config.pad_token_id					)
						if head_mask is None:
							_lowercase					:       Dict       =		torch.ones(config.num_hidden_layers     ,  config.num_attention_heads     ,  device=UpperCamelCase_					)
						if decoder_head_mask is None:
							_lowercase					:       int       =		torch.ones(config.num_decoder_layers     ,  config.num_attention_heads     ,  device=UpperCamelCase_					)
						if cross_attn_head_mask is None:
							_lowercase					:       List[str]       =		torch.ones(
							    config.num_decoder_layers     ,  config.num_attention_heads     ,  device=UpperCamelCase_					)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       List[str]       =		ids_tensor([self.batch_size, self.encoder_seq_length]     ,  self.vocab_size					)
						_lowercase					:       Dict       =		ids_tensor([self.batch_size, self.decoder_seq_length]     ,  self.vocab_size					)

						# we need to clamp the input ids here to avoid having pad token in between
						# this is because for NllbMoe the position_ids are prepared such that
						# all pad tokens have pos id = 2 and rest are between 2..seq_length
						# and the seq_length here is seq_length - num_pad_tokens
						# but when using past, there is no way of knowing if the past input ids had
						# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
						# position_ids being off by num_pad_tokens in past input
						_lowercase					:       Tuple       =		input_ids.clamp(self.pad_token_id + 1					)
						_lowercase					:       Tuple       =		decoder_input_ids.clamp(self.pad_token_id + 1					)

						_lowercase					:       Optional[Any]       =		self.get_config()
						_lowercase					:       List[Any]       =		config.num_attention_heads
						_lowercase					:       int       =		self.prepare_inputs_dict(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						return config, input_dict




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Any:



						'''simple docstring'''





						_lowercase  ,    _lowercase					:       Optional[int]       =		self.prepare_config_and_inputs()
						return config, inputs_dict




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						return TaConfig(
						    vocab_size=166     ,  d_model=self.hidden_size     ,  d_ff=self.d_ff     ,  d_kv=self.hidden_size // self.num_attention_heads     ,  num_layers=self.num_hidden_layers     ,  num_decoder_layers=self.decoder_layers     ,  num_heads=self.num_attention_heads     ,  relative_attention_num_buckets=self.relative_attention_num_buckets     ,  dropout_rate=self.dropout_rate     ,  initializer_factor=self.initializer_factor     ,  eos_token_id=self.eos_token_id     ,  bos_token_id=self.pad_token_id     ,  pad_token_id=self.pad_token_id     ,  decoder_start_token_id=self.decoder_start_token_id     ,  )




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       str:



						'''simple docstring'''





						return TaConfig(
						    vocab_size=self.vocab_size     ,  d_model=self.hidden_size     ,  d_ff=self.d_ff     ,  d_kv=self.hidden_size // self.num_attention_heads     ,  num_layers=self.num_hidden_layers     ,  num_decoder_layers=self.decoder_layers     ,  num_heads=self.num_attention_heads     ,  relative_attention_num_buckets=self.relative_attention_num_buckets     ,  dropout_rate=self.dropout_rate     ,  initializer_factor=self.initializer_factor     ,  eos_token_id=self.eos_token_id     ,  bos_token_id=self.pad_token_id     ,  pad_token_id=self.pad_token_id     ,  decoder_start_token_id=self.decoder_start_token_id     ,  )




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Tuple     ,  )  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Dict       =		UMTaModel(config=UpperCamelCase_					)
						model.to(UpperCamelCase_					)
						model.eval()
						_lowercase					:       int       =		model(
						    input_ids=UpperCamelCase_     ,  decoder_input_ids=UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  decoder_attention_mask=UpperCamelCase_     ,  )
						_lowercase					:       Any       =		model(input_ids=UpperCamelCase_     ,  decoder_input_ids=UpperCamelCase_					)
						_lowercase					:       Tuple       =		result.last_hidden_state
						_lowercase					:       Optional[int]       =		result.past_key_values
						_lowercase					:       str       =		result.encoder_last_hidden_state

						self.parent.assertEqual(encoder_output.size()     ,  (self.batch_size, self.encoder_seq_length, self.hidden_size)					)
						self.parent.assertEqual(decoder_output.size()     ,  (self.batch_size, self.decoder_seq_length, self.hidden_size)					)
						# There should be `num_layers` key value embeddings stored in decoder_past
						self.parent.assertEqual(len(UpperCamelCase_					)     ,  config.num_layers					)
						# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
						self.parent.assertEqual(len(decoder_past[0]					)     ,  4					)




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Any     ,  )  ->       Any:



						'''simple docstring'''





						_lowercase					:       str       =		UMTaModel(config=UpperCamelCase_					).get_decoder().to(UpperCamelCase_					).eval()
						# first forward pass
						_lowercase					:       Optional[Any]       =		model(UpperCamelCase_     ,  use_cache=UpperCamelCase_					)
						_lowercase					:       Dict       =		model(UpperCamelCase_					)
						_lowercase					:       Dict       =		model(UpperCamelCase_     ,  use_cache=UpperCamelCase_					)

						self.parent.assertTrue(len(UpperCamelCase_					) == len(UpperCamelCase_					)					)
						self.parent.assertTrue(len(UpperCamelCase_					) == len(UpperCamelCase_					) + 1					)

						_lowercase  ,    _lowercase					:       Any       =		outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						_lowercase					:       Any       =		ids_tensor((self.batch_size, 1)     ,  config.vocab_size					)

						# append to next input_ids and
						_lowercase					:       int       =		torch.cat([input_ids, next_tokens]     ,  dim=-1					)

						_lowercase					:       Optional[int]       =		model(UpperCamelCase_					)['last_hidden_state']
						_lowercase					:       str       =		model(UpperCamelCase_     ,  past_key_values=UpperCamelCase_					)['last_hidden_state']

						# select random slice
						_lowercase					:       List[Any]       =		ids_tensor((1,)     ,  output_from_past.shape[-1]					).item()
						_lowercase					:       List[str]       =		output_from_no_past[:, -1, random_slice_idx].detach()
						_lowercase					:       Optional[int]       =		output_from_past[:, 0, random_slice_idx].detach()

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(UpperCamelCase_     ,  UpperCamelCase_     ,  atol=1E-3					)					)




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Optional[int]     ,  )  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		UMTaModel(config=UpperCamelCase_					).to(UpperCamelCase_					).half().eval()
						_lowercase					:       Tuple       =		model(**UpperCamelCase_					)['last_hidden_state']
						self.parent.assertFalse(torch.isnan(UpperCamelCase_					).any().item()					)






@require_torch
class 		lowerCamelCase__      (						A	,   A	,   A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						(
					    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
					)
					A_						    =						(UMTaForConditionalGeneration,) if is_torch_available() else ()
					A_						    =						(
					    {
					        """conversational""": UMTaForConditionalGeneration,
					        """feature-extraction""": UMTaModel,
					        """summarization""": UMTaForConditionalGeneration,
					        """text2text-generation""": UMTaForConditionalGeneration,
					        """translation""": UMTaForConditionalGeneration,
					        """question-answering""": UMTaForQuestionAnswering,
					    }
					    if is_torch_available()
					    else {}
					)
					A_						    =						True
					A_						    =						False
					A_						    =						False
					A_						    =						True
					A_						    =						True
					# The small UMT5 model needs higher percentages for CPU/MP tests
					A_						    =						[0.8, 0.9]




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		UMTaModelTester(self					)




					@unittest.skip('Test has a segmentation fault on torch 1.8.0'					)
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		self.model_tester.prepare_config_and_inputs()
						_lowercase					:       Optional[int]       =		UMTaModel(config_and_inputs[0]					).to(UpperCamelCase_					)
						with tempfile.TemporaryDirectory() as tmpdirname:
							torch.onnx.export(
							    UpperCamelCase_     ,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,  F'''{tmpdirname}/t5_test.onnx'''     ,  export_params=UpperCamelCase_     ,  opset_version=9     ,  input_names=['input_ids', 'decoder_input_ids']     ,  )




					@unittest.skipIf(torch_device == 'cpu'     ,  'Cant do half precision'					)
					def 						__UpperCAmelCase     (  self				:	str					)  ->       str:



						'''simple docstring'''





						_lowercase					:       str       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		['encoder_attentions', 'decoder_attentions', 'cross_attentions']
						_lowercase					:       List[Any]       =		self.model_tester.prepare_config_and_inputs()
						_lowercase					:       Dict       =		config_and_inputs[0]
						_lowercase					:       Union[str, Any]       =		UMTaForConditionalGeneration(UpperCamelCase_					).eval()
						model.to(UpperCamelCase_					)

						_lowercase					:       List[Any]       =		{
						    'head_mask': torch.zeros(config.num_layers     ,  config.num_heads     ,  device=UpperCamelCase_					),
						    'decoder_head_mask': torch.zeros(config.num_decoder_layers     ,  config.num_heads     ,  device=UpperCamelCase_					),
						    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers     ,  config.num_heads     ,  device=UpperCamelCase_					),
						}

						for attn_name, (name, mask) in zip(UpperCamelCase_     ,  head_masking.items()					):
							_lowercase					:       Tuple       =		{name: mask}
							# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
							if name == "head_mask":
								_lowercase					:       Dict       =		torch.ones(
								    config.num_decoder_layers     ,  config.num_heads     ,  device=UpperCamelCase_					)

							_lowercase					:       Optional[Any]       =		model.generate(
							    config_and_inputs[1]['input_ids']     ,  num_beams=1     ,  max_length=3     ,  output_attentions=UpperCamelCase_     ,  return_dict_in_generate=UpperCamelCase_     ,  **UpperCamelCase_     ,  )
							# We check the state of decoder_attentions and cross_attentions just from the last step
							_lowercase					:       Dict       =		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
							self.assertEqual(sum([w.sum().item() for w in attn_weights]					)     ,  0.0					)




					@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'					)
					def 						__UpperCAmelCase     (  self				:	str					)  ->       Optional[Any]:



						'''simple docstring'''





						pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					@unittest.skip(
					    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'					)
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       int       =		UMTaForConditionalGeneration.from_pretrained('google/umt5-small'     ,  return_dict=UpperCamelCase_					).to(UpperCamelCase_					)
						_lowercase					:       int       =		AutoTokenizer.from_pretrained('google/umt5-small'     ,  use_fast=UpperCamelCase_     ,  legacy=UpperCamelCase_					)
						_lowercase					:       List[str]       =		[
						    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
						    'No se como puedo <extra_id_0>.',
						    'This is the reason why we <extra_id_0> them.',
						    'The <extra_id_0> walks in <extra_id_1>, seats',
						    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
						]
						_lowercase					:       Dict       =		tokenizer(UpperCamelCase_     ,  return_tensors='pt'     ,  padding=UpperCamelCase_					).input_ids
						# fmt: off
						_lowercase					:       Optional[int]       =		torch.tensor(
						    [
						        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
						    ]					)
						# fmt: on
						torch.testing.assert_allclose(UpperCamelCase_     ,  UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		model.generate(input_ids.to(UpperCamelCase_					)					)
						_lowercase					:       Tuple       =		[
						    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
						    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						]
						_lowercase					:       Optional[int]       =		tokenizer.batch_decode(UpperCamelCase_					)
						self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)





'''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 						__UpperCamelCase				(							_lowercase					) ->			Tuple:
	_lowercase					:       Tuple       =		SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		_lowercase					:       Optional[Any]       =		4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		_lowercase					:       Tuple       =		4
		_lowercase					:       Union[str, Any]       =		48
		_lowercase					:       Any       =		'pixelshuffle_aux'
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		_lowercase					:       Dict       =		[6, 6, 6, 6]
		_lowercase					:       Optional[int]       =		60
		_lowercase					:       List[str]       =		[6, 6, 6, 6]
		_lowercase					:       Dict       =		'pixelshuffledirect'
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		_lowercase					:       str       =		4
		_lowercase					:       str       =		'nearest+conv'
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		_lowercase					:       str       =		1
		_lowercase					:       Tuple       =		1
		_lowercase					:       Dict       =		126
		_lowercase					:       Optional[int]       =		7
		_lowercase					:       List[Any]       =		2_5_5.0
		_lowercase					:       Tuple       =		''

	return config







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

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

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

	return name







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			List[str]:
	for key in orig_state_dict.copy().keys():
		_lowercase					:       int       =		orig_state_dict.pop(_lowercase					)

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

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

	return orig_state_dict







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Union[str, Any]:
	_lowercase					:       Optional[Any]       =		get_config(_lowercase					)
	_lowercase					:       Union[str, Any]       =		SwinaSRForImageSuperResolution(_lowercase					)
	model.eval()

	_lowercase					:       List[Any]       =		torch.hub.load_state_dict_from_url(_lowercase,				map_location='cpu'					)
	_lowercase					:       Any       =		convert_state_dict(_lowercase,				_lowercase					)
	_lowercase  ,    _lowercase					:       str       =		model.load_state_dict(_lowercase,				strict=_lowercase					)

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

	_lowercase					:       Tuple       =		126 if 'Jpeg' in checkpoint_url else 256
	_lowercase					:       List[str]       =		Compose(
	    [
	        Resize((image_size, image_size)					),
	        ToTensor(),
	        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6],				std=[0.2_2_9, 0.2_2_4, 0.2_2_5]					),
	    ]					)
	_lowercase					:       Optional[Any]       =		transforms(_lowercase					).unsqueeze(0					)

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

	_lowercase					:       Optional[int]       =		model(_lowercase					)

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		_lowercase					:       Any       =		torch.Size([1, 3, 512, 512]					)
		_lowercase					:       Tuple       =		torch.tensor(
		    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]					)
	elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		_lowercase					:       Optional[Any]       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       int       =		torch.tensor(
		    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]					)
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		# TODO values didn't match exactly here
		_lowercase					:       Optional[int]       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       Dict       =		torch.tensor(
		    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]					)
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		_lowercase					:       List[str]       =		torch.Size([1, 3, 512, 512]					)
		_lowercase					:       int       =		torch.tensor(
		    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]					)
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		_lowercase					:       Any       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       Union[str, Any]       =		torch.tensor(
		    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]					)

	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],				_lowercase,				atol=1E-3					)
	print('Looks ok!'					)

	_lowercase					:       List[str]       =		{
	    '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'
	    ),
	}
	_lowercase					:       int       =		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(_lowercase					)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
		processor.save_pretrained(_lowercase					)

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


if __name__ == "__main__":
		_A				:    Dict     						=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.''')

		_A				:    int     						=parser.parse_args()
		convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
