





"""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       a							:



 def __init__(     self				,       _snake_case				,       _snake_case=99				,       _snake_case=13				,       _snake_case=7				,       _snake_case=9				,       _snake_case=True				,       _snake_case=True				,       _snake_case=False				,       _snake_case=32				,       _snake_case=5				,       _snake_case=4				,       _snake_case=37				,       _snake_case=8				,       _snake_case=0.1				,       _snake_case=0.002				,       _snake_case=1				,       _snake_case=0				,       _snake_case=0				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= encoder_seq_length
  lowerCAmelCase    				= decoder_seq_length
  # For common tests
  lowerCAmelCase    				= self.decoder_seq_length
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_attention_mask
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= d_ff
  lowerCAmelCase    				= relative_attention_num_buckets
  lowerCAmelCase    				= dropout_rate
  lowerCAmelCase    				= initializer_factor
  lowerCAmelCase    				= eos_token_id
  lowerCAmelCase    				= pad_token_id
  lowerCAmelCase    				= decoder_start_token_id
  lowerCAmelCase    				= None
  lowerCAmelCase    				= decoder_layers



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



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







  """simple docstring"""






  if attention_mask is None:
   lowerCAmelCase    				= input_ids.ne(config.pad_token_id     )
  if decoder_attention_mask is None:
   lowerCAmelCase    				= decoder_input_ids.ne(config.pad_token_id     )
  if head_mask is None:
   lowerCAmelCase    				= torch.ones(config.num_hidden_layers				,       config.num_attention_heads				,       device=_snake_case     )
  if decoder_head_mask is None:
   lowerCAmelCase    				= torch.ones(config.num_decoder_layers				,       config.num_attention_heads				,       device=_snake_case     )
  if cross_attn_head_mask is None:
   lowerCAmelCase    				= torch.ones(
       config.num_decoder_layers				,       config.num_attention_heads				,       device=_snake_case     )
  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     ):







  """simple docstring"""






  lowerCAmelCase    				= ids_tensor([self.batch_size, self.encoder_seq_length]				,       self.vocab_size     )
  lowerCAmelCase    				= 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
  lowerCAmelCase    				= input_ids.clamp(self.pad_token_id + 1     )
  lowerCAmelCase    				= decoder_input_ids.clamp(self.pad_token_id + 1     )

  lowerCAmelCase    				= self.get_config()
  lowerCAmelCase    				= config.num_attention_heads
  lowerCAmelCase    				= self.prepare_inputs_dict(_snake_case				,       _snake_case				,       _snake_case     )
  return config, input_dict



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.prepare_config_and_inputs()
  return config, inputs_dict



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return TaConfig(
      vocab_size=1_66				,       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     ):







  """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				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModel(config=_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(
      input_ids=_snake_case				,       decoder_input_ids=_snake_case				,       attention_mask=_snake_case				,       decoder_attention_mask=_snake_case				,       )
  lowerCAmelCase    				= model(input_ids=_snake_case				,       decoder_input_ids=_snake_case     )
  lowerCAmelCase    				= result.last_hidden_state
  lowerCAmelCase    				= result.past_key_values
  lowerCAmelCase    				= 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(_snake_case     )				,       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				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModel(config=_snake_case     ).get_decoder().to(_snake_case     ).eval()
  # first forward pass
  lowerCAmelCase    				= model(_snake_case				,       use_cache=_snake_case     )
  lowerCAmelCase    				= model(_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       use_cache=_snake_case     )

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

  lowerCAmelCase				,lowerCAmelCase    				= outputs.to_tuple()

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

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

  lowerCAmelCase    				= model(_snake_case     )['last_hidden_state']
  lowerCAmelCase    				= model(_snake_case				,       past_key_values=_snake_case     )['last_hidden_state']

  # select random slice
  lowerCAmelCase    				= ids_tensor((1,)				,       output_from_past.shape[-1]     ).item()
  lowerCAmelCase    				= output_from_no_past[:, -1, random_slice_idx].detach()
  lowerCAmelCase    				= output_from_past[:, 0, random_slice_idx].detach()

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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModel(config=_snake_case     ).to(_snake_case     ).half().eval()
  lowerCAmelCase    				= model(**_snake_case     )['last_hidden_state']
  self.parent.assertFalse(torch.isnan(_snake_case     ).any().item()     )







@require_torch
class       a							(   a__   , a__   , a__   , unittest.TestCase  ):
 snake_case__             =       (
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 snake_case__             =       (UMTaForConditionalGeneration,) if is_torch_available() else ()
 snake_case__             =       (
     {
         '''conversational''': UMTaForConditionalGeneration,
         '''feature-extraction''': UMTaModel,
         '''summarization''': UMTaForConditionalGeneration,
         '''text2text-generation''': UMTaForConditionalGeneration,
         '''translation''': UMTaForConditionalGeneration,
         '''question-answering''': UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 snake_case__             =       True
 snake_case__             =       False
 snake_case__             =       False
 snake_case__             =       True
 snake_case__             =       True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 snake_case__             =       [0.8, 0.9]



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModelTester(self     )



 @unittest.skip('Test has a segmentation fault on torch 1.8.0'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 @unittest.skipIf(torch_device == 'cpu'				,       'Cant do half precision'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model_fpaa_forward(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  lowerCAmelCase    				= config_and_inputs[0]
  lowerCAmelCase    				= UMTaForConditionalGeneration(_snake_case     ).eval()
  model.to(_snake_case     )

  lowerCAmelCase    				= {
      'head_mask': torch.zeros(config.num_layers				,       config.num_heads				,       device=_snake_case     ),
      'decoder_head_mask': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_snake_case     ),
      'cross_attn_head_mask': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_snake_case     ),
  }

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

   lowerCAmelCase    				= model.generate(
       config_and_inputs[1]['input_ids']				,       num_beams=1				,       max_length=3				,       output_attentions=_snake_case				,       return_dict_in_generate=_snake_case				,       **_snake_case				,       )
   # We check the state of decoder_attentions and cross_attentions just from the last step
   lowerCAmelCase    				= 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     ):







  """simple docstring"""






  pass







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



 @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     ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaForConditionalGeneration.from_pretrained('google/umt5-small'				,       return_dict=_snake_case     ).to(_snake_case     )
  lowerCAmelCase    				= AutoTokenizer.from_pretrained('google/umt5-small'				,       use_fast=_snake_case				,       legacy=_snake_case     )
  lowerCAmelCase    				= [
      '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>.',
  ]
  lowerCAmelCase    				= tokenizer(_snake_case				,       return_tensors='pt'				,       padding=_snake_case     ).input_ids
  # fmt: off
  lowerCAmelCase    				= torch.tensor(
      [
          [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
          [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
          [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
          [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
          [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
      ]     )
  # fmt: on
  torch.testing.assert_allclose(_snake_case				,       _snake_case     )

  lowerCAmelCase    				= model.generate(input_ids.to(_snake_case     )     )
  lowerCAmelCase    				= [
      '<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>',
  ]
  lowerCAmelCase    				= tokenizer.batch_decode(_snake_case     )
  self.assertEqual(_snake_case				,       _snake_case     )












"""simple docstring"""
import argparse

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

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor

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

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

 return config

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

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

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

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

 return name

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

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

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

 return orig_state_dict

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

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

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

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

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

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

 lowerCAmelCase    				= model(_UpperCAmelCase			)

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

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

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

 if pytorch_dump_folder_path is not None:
  print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
  model.save_pretrained(_UpperCAmelCase			)
  print(F'Saving image processor to {pytorch_dump_folder_path}'			)
  processor.save_pretrained(_UpperCAmelCase			)

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


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

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





