
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_nllb import NllbTokenizer
else:
     lowerCamelCase__     :		str        = None


lowerCamelCase__     :		int        = logging.get_logger(__name__)


lowerCamelCase__     :		Optional[int]        = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__     :		List[str]        = {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase__     :		Union[str, Any]        = {
    """facebook/nllb-large-en-ro""": 1_0_2_4,
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
lowerCamelCase__     :		Tuple        = ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[str]	   =      VOCAB_FILES_NAMES
 __lowerCAmelCase	:  Optional[Any]	   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase	:  str	   =      PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase	:  Tuple	   =      ['input_ids', 'attention_mask']
 __lowerCAmelCase	:  List[Any]	   =      NllbTokenizer

 __lowerCAmelCase	:  List[int]	   =      []
 __lowerCAmelCase	:  List[int]	   =      []


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="<unk>"       ,	SCREAMING_SNAKE_CASE_="<pad>"       ,	SCREAMING_SNAKE_CASE_="<mask>"       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=False       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Any     		=   AddedToken(SCREAMING_SNAKE_CASE_       ,	lstrip=SCREAMING_SNAKE_CASE_       ,	rstrip=SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else mask_token
   lowercase__	: Optional[int]     		=   legacy_behaviour
   super().__init__(
       vocab_file=SCREAMING_SNAKE_CASE_       ,	tokenizer_file=SCREAMING_SNAKE_CASE_       ,	bos_token=SCREAMING_SNAKE_CASE_       ,	eos_token=SCREAMING_SNAKE_CASE_       ,	sep_token=SCREAMING_SNAKE_CASE_       ,	cls_token=SCREAMING_SNAKE_CASE_       ,	unk_token=SCREAMING_SNAKE_CASE_       ,	pad_token=SCREAMING_SNAKE_CASE_       ,	mask_token=SCREAMING_SNAKE_CASE_       ,	src_lang=SCREAMING_SNAKE_CASE_       ,	tgt_lang=SCREAMING_SNAKE_CASE_       ,	additional_special_tokens=SCREAMING_SNAKE_CASE_       ,	legacy_behaviour=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: int     		=   vocab_file
   lowercase__	: Any     		=   False if not self.vocab_file else True

   lowercase__	: Union[str, Any]     		=   FAIRSEQ_LANGUAGE_CODES.copy()

   if additional_special_tokens is not None:
     # Only add those special tokens if they are not already there.
     _additional_special_tokens.extend(
         [t for t in additional_special_tokens if t not in _additional_special_tokens])

   self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens})
   lowercase__	: Optional[Any]     		=   {
       lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_) for lang_code in FAIRSEQ_LANGUAGE_CODES
   }

   lowercase__	: int     		=   src_lang if src_lang is not None else """eng_Latn"""
   lowercase__	: Tuple     		=   self.convert_tokens_to_ids(self._src_lang)
   lowercase__	: Dict     		=   tgt_lang
   self.set_src_lang_special_tokens(self._src_lang)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self._src_lang


 @src_lang.setter
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   new_src_lang
   self.set_src_lang_special_tokens(self._src_lang)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if token_ids_a is None:
     return self.prefix_tokens + token_ids_a + self.suffix_tokens
   # We don't expect to process pairs, but leave the pair logic for API consistency
   return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   [self.sep_token_id]
   lowercase__	: Any     		=   [self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if src_lang is None or tgt_lang is None:
     raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
   lowercase__	: Tuple     		=   src_lang
   lowercase__	: Any     		=   self(SCREAMING_SNAKE_CASE_       ,	add_special_tokens=SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   tgt_lang_id
   return inputs


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = "eng_Latn"       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = "fra_Latn"       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   src_lang
   lowercase__	: List[str]     		=   tgt_lang
   return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   return self.set_src_lang_special_tokens(self.src_lang)


 def   lowercase__  (       self):


   '''simple docstring'''




   return self.set_tgt_lang_special_tokens(self.tgt_lang)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)

   if self.legacy_behaviour:
     lowercase__	: Optional[Any]     		=   []
     lowercase__	: Union[str, Any]     		=   [self.eos_token_id, self.cur_lang_code]
   else:
     lowercase__	: Optional[Any]     		=   [self.cur_lang_code]
     lowercase__	: Tuple     		=   [self.eos_token_id]

   lowercase__	: int     		=   self.convert_ids_to_tokens(self.prefix_tokens)
   lowercase__	: Optional[Any]     		=   self.convert_ids_to_tokens(self.suffix_tokens)

   lowercase__	: str     		=   processors.TemplateProcessing(
       single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,	pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,	self.prefix_tokens + self.suffix_tokens))       ,	)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)
   if self.legacy_behaviour:
     lowercase__	: Dict     		=   []
     lowercase__	: Dict     		=   [self.eos_token_id, self.cur_lang_code]
   else:
     lowercase__	: Tuple     		=   [self.cur_lang_code]
     lowercase__	: str     		=   [self.eos_token_id]

   lowercase__	: Dict     		=   self.convert_ids_to_tokens(self.prefix_tokens)
   lowercase__	: int     		=   self.convert_ids_to_tokens(self.suffix_tokens)

   lowercase__	: Optional[Any]     		=   processors.TemplateProcessing(
       single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,	pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,	self.prefix_tokens + self.suffix_tokens))       ,	)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer.""")

   if not os.path.isdir(SCREAMING_SNAKE_CASE_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
     return
   lowercase__	: List[Any]     		=   os.path.join(
       SCREAMING_SNAKE_CASE_       ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

   if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE_):
     copyfile(self.vocab_file       ,	SCREAMING_SNAKE_CASE_)

   return (out_vocab_file,)








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_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    lowercase__	: List[str]     		=   4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    lowercase__	: Optional[int]     		=   4
    lowercase__	: Optional[Any]     		=   48
    lowercase__	: int     		=   """pixelshuffle_aux"""
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    lowercase__	: List[str]     		=   [6, 6, 6, 6]
    lowercase__	: Any     		=   60
    lowercase__	: Tuple     		=   [6, 6, 6, 6]
    lowercase__	: Dict     		=   """pixelshuffledirect"""
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    lowercase__	: Tuple     		=   4
    lowercase__	: Any     		=   """nearest+conv"""
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    lowercase__	: str     		=   1
    lowercase__	: Optional[int]     		=   1
    lowercase__	: Optional[int]     		=   1_26
    lowercase__	: Any     		=   7
    lowercase__	: int     		=   255.0
    lowercase__	: List[Any]     		=   """"""

  return config

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






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

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

  return name

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  for key in orig_state_dict.copy().keys():
    lowercase__	: str     		=   orig_state_dict.pop(lowercase_ )

    if "qkv" in key:
      lowercase__	: Any     		=   key.split(""".""" )
      lowercase__	: List[Any]     		=   int(key_split[1] )
      lowercase__	: Dict     		=   int(key_split[4] )
      lowercase__	: Optional[Any]     		=   config.embed_dim

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

  return orig_state_dict

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Dict     		=   get_config(lowercase_ )
  lowercase__	: Any     		=   SwinaSRForImageSuperResolution(lowercase_ )
  model.eval()

  lowercase__	: List[str]     		=   torch.hub.load_state_dict_from_url(lowercase_			,			map_location="""cpu""" )
  lowercase__	: Union[str, Any]     		=   convert_state_dict(lowercase_			,			lowercase_ )
  lowercase__		,     lowercase__	: Dict     		=   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__	: Any     		=   """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__	: Any     		=   SwinaSRImageProcessor()
  # pixel_values = processor(image, return_tensors="pt").pixel_values

  lowercase__	: Optional[int]     		=   1_26 if """Jpeg""" in checkpoint_url else 2_56
  lowercase__	: Union[str, Any]     		=   Compose(
      [
          Resize((image_size, image_size) ),
          ToTensor(),
          Normalize(mean=[0.485, 0.456, 0.406]			,			std=[0.229, 0.224, 0.225] ),
      ] )
  lowercase__	: Dict     		=   transforms(lowercase_ ).unsqueeze(0 )

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

  lowercase__	: Union[str, Any]     		=   model(lowercase_ )

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: Optional[Any]     		=   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:
    lowercase__	: List[str]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: int     		=   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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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]			,			lowercase_			,			atol=1E-3 )
  print("""Looks ok!""" )

  lowercase__	: 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__	: str     		=   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__":
     lowerCamelCase__     :		List[str]        = 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.""")

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






