






import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class     lowerCamelCase_					(   lowerCAmelCase__			):
				a__       							=	42
				a__       							=	jnp.floataa
				a__       							=	True







				def       A						(    self  ):
								"""simple docstring"""
								super().setup()
								__magic_name__		:Tuple   =   nn.Dense(5							,       dtype=self.dtype  )







				def __call__(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   super().__call__(*__UpperCAmelCase							,       **__UpperCAmelCase  )
								__magic_name__		:List[str]   =   self.cls(outputs[2]  )
								return outputs[:2] + (cls_out,)







class     lowerCamelCase_					(   lowerCAmelCase__			):
				a__       							=	FlaxBigBirdForNaturalQuestionsModule







def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				def cross_entropy(snake_case,	snake_case,	snake_case=None			):
								__magic_name__		:Union[str, Any]   =   logits.shape[-1]
								__magic_name__		:Optional[int]   =   (labels[..., None] == jnp.arange(_UpperCamelCase			)[None]).astype('''f4'''			)
								__magic_name__		:Union[str, Any]   =   jax.nn.log_softmax(_UpperCamelCase,	axis=-1			)
								__magic_name__		:Union[str, Any]   =   -jnp.sum(labels * logits,	axis=-1			)
								if reduction is not None:
												__magic_name__		:Optional[Any]   =   reduction(_UpperCamelCase			)
								return loss

				__magic_name__		:Union[str, Any]   =   partial(_UpperCamelCase,	reduction=jnp.mean			)
				__magic_name__		:Any   =   cross_entropy(_UpperCamelCase,	_UpperCamelCase			)
				__magic_name__		:Optional[Any]   =   cross_entropy(_UpperCamelCase,	_UpperCamelCase			)
				__magic_name__		:Any   =   cross_entropy(_UpperCamelCase,	_UpperCamelCase			)
				return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class     lowerCamelCase_					:
				a__       							=	'''google/bigbird-roberta-base'''
				a__       							=	30_00
				a__       							=	1_05_00

				a__       							=	1_28
				a__       							=	3

				a__       							=	1
				a__       							=	5

				# tx_args
				a__       							=	3e-5
				a__       							=	0.0
				a__       							=	2_00_00
				a__       							=	0.0095

				a__       							=	'''bigbird-roberta-natural-questions'''
				a__       							=	'''training-expt'''
				a__       							=	'''data/nq-training.jsonl'''
				a__       							=	'''data/nq-validation.jsonl'''







				def       A						(    self  ):
								"""simple docstring"""
								os.makedirs(self.base_dir							,       exist_ok=__UpperCAmelCase  )
								__magic_name__		:Tuple   =   os.path.join(self.base_dir							,       self.save_dir  )
								__magic_name__		:Optional[int]   =   self.batch_size_per_device * jax.device_count()







@dataclass
class     lowerCamelCase_					:
				a__       							=	42
				a__       							=	40_96  # no dynamic padding on TPUs







				def __call__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.collate_fn(__UpperCAmelCase  )
								__magic_name__		:str   =   jax.tree_util.tree_map(__UpperCAmelCase							,       __UpperCAmelCase  )
								return batch







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:List[Any]   =   self.fetch_inputs(features['''input_ids''']  )
								__magic_name__		:Optional[int]   =   {
								    '''input_ids''': jnp.array(__UpperCAmelCase							,       dtype=jnp.intaa  ),
								    '''attention_mask''': jnp.array(__UpperCAmelCase							,       dtype=jnp.intaa  ),
								    '''start_labels''': jnp.array(features['''start_token''']							,       dtype=jnp.intaa  ),
								    '''end_labels''': jnp.array(features['''end_token''']							,       dtype=jnp.intaa  ),
								    '''pooled_labels''': jnp.array(features['''category''']							,       dtype=jnp.intaa  ),
								}
								return batch







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   [self._fetch_inputs(__UpperCAmelCase  ) for ids in input_ids]
								return zip(*__UpperCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   [1 for _ in range(len(__UpperCAmelCase  )  )]
								while len(__UpperCAmelCase  ) < self.max_length:
												input_ids.append(self.pad_id  )
												attention_mask.append(0  )
								return input_ids, attention_mask







def   __lowercase       (			snake_case,	snake_case,	snake_case=None			):







				"""simple docstring"""






				if seed is not None:
								__magic_name__		:Dict   =   dataset.shuffle(seed=_UpperCamelCase			)
				for i in range(len(_UpperCamelCase			) // batch_size			):
								__magic_name__		:str   =   dataset[i * batch_size : (i + 1) * batch_size]
								yield dict(_UpperCamelCase			)

@partial(jax.pmap,	axis_name='''batch'''			)
def   __lowercase       (			snake_case,	snake_case,	**snake_case			):







				"""simple docstring"""






				def loss_fn(snake_case			):
								__magic_name__		:Optional[int]   =   model_inputs.pop('''start_labels'''			)
								__magic_name__		:List[str]   =   model_inputs.pop('''end_labels'''			)
								__magic_name__		:Dict   =   model_inputs.pop('''pooled_labels'''			)

								__magic_name__		:str   =   state.apply_fn(**_UpperCamelCase,	params=_UpperCamelCase,	dropout_rng=_UpperCamelCase,	train=_UpperCamelCase			)
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:List[Any]   =   outputs

								return state.loss_fn(
								    _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	)

				__magic_name__  ,					__magic_name__		:Union[str, Any]   =   jax.random.split(_UpperCamelCase			)
				__magic_name__		:Tuple   =   jax.value_and_grad(_UpperCamelCase			)
				__magic_name__  ,					__magic_name__		:Tuple   =   grad_fn(state.params			)
				__magic_name__		:Tuple   =   jax.lax.pmean({'''loss''': loss},	axis_name='''batch'''			)
				__magic_name__		:int   =   jax.lax.pmean(_UpperCamelCase,	'''batch'''			)

				__magic_name__		:Union[str, Any]   =   state.apply_gradients(grads=_UpperCamelCase			)
				return state, metrics, new_drp_rng

@partial(jax.pmap,	axis_name='''batch'''			)
def   __lowercase       (			snake_case,	**snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   model_inputs.pop('''start_labels'''			)
				__magic_name__		:Dict   =   model_inputs.pop('''end_labels'''			)
				__magic_name__		:Optional[int]   =   model_inputs.pop('''pooled_labels'''			)

				__magic_name__		:List[Any]   =   state.apply_fn(**_UpperCamelCase,	params=state.params,	train=_UpperCamelCase			)
				__magic_name__  ,					__magic_name__  ,					__magic_name__		:Dict   =   outputs

				__magic_name__		:str   =   state.loss_fn(_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase			)
				__magic_name__		:str   =   jax.lax.pmean({'''loss''': loss},	axis_name='''batch'''			)
				return metrics
class     lowerCamelCase_					(   train_state.TrainState			):
				a__       							=	struct.field(pytree_node=lowerCAmelCase__			)







@dataclass
class     lowerCamelCase_					:
				a__       							=	42
				a__       							=	42
				a__       							=	42
				a__       							=	42
				a__       							=	42
				a__       							=	42
				a__       							=	None







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   model.params
								__magic_name__		:Union[str, Any]   =   TrainState.create(
								    apply_fn=model.__call__							,       params=__UpperCAmelCase							,       tx=__UpperCAmelCase							,       loss_fn=__UpperCAmelCase							,       )
								if ckpt_dir is not None:
												__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:List[str]   =   restore_checkpoint(__UpperCAmelCase							,       __UpperCAmelCase  )
												__magic_name__		:Optional[Any]   =   {
												    '''lr''': args.lr,
												    '''init_lr''': args.init_lr,
												    '''warmup_steps''': args.warmup_steps,
												    '''num_train_steps''': num_train_steps,
												    '''weight_decay''': args.weight_decay,
												}
												__magic_name__  ,					__magic_name__		:int   =   build_tx(**__UpperCAmelCase  )
												__magic_name__		:int   =   train_state.TrainState(
												    step=__UpperCAmelCase							,       apply_fn=model.__call__							,       params=__UpperCAmelCase							,       tx=__UpperCAmelCase							,       opt_state=__UpperCAmelCase							,       )
												__magic_name__		:int   =   args
												__magic_name__		:List[str]   =   data_collator
												__magic_name__		:Optional[int]   =   lr
												__magic_name__		:str   =   params
								__magic_name__		:Dict   =   jax_utils.replicate(__UpperCAmelCase  )
								return state







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.args
								__magic_name__		:Optional[int]   =   len(__UpperCAmelCase  ) // args.batch_size

								__magic_name__		:str   =   jax.random.PRNGKey(0  )
								__magic_name__		:Optional[int]   =   jax.random.split(__UpperCAmelCase							,       jax.device_count()  )
								for epoch in range(args.max_epochs  ):
												__magic_name__		:Optional[int]   =   jnp.array(0							,       dtype=jnp.floataa  )
												__magic_name__		:List[Any]   =   get_batched_dataset(__UpperCAmelCase							,       args.batch_size							,       seed=__UpperCAmelCase  )
												__magic_name__		:Union[str, Any]   =   0
												for batch in tqdm(__UpperCAmelCase							,       total=__UpperCAmelCase							,       desc=F'''Running EPOCH-{epoch}'''  ):
																__magic_name__		:Union[str, Any]   =   self.data_collator(__UpperCAmelCase  )
																__magic_name__  ,					__magic_name__  ,					__magic_name__		:List[str]   =   self.train_step_fn(__UpperCAmelCase							,       __UpperCAmelCase							,       **__UpperCAmelCase  )
																running_loss += jax_utils.unreplicate(metrics['''loss''']  )
																i += 1
																if i % args.logging_steps == 0:
																				__magic_name__		:Optional[int]   =   jax_utils.unreplicate(state.step  )
																				__magic_name__		:Optional[int]   =   running_loss.item() / i
																				__magic_name__		:Tuple   =   self.scheduler_fn(state_step - 1  )

																				__magic_name__		:str   =   self.evaluate(__UpperCAmelCase							,       __UpperCAmelCase  )
																				__magic_name__		:Optional[Any]   =   {
																				    '''step''': state_step.item(),
																				    '''eval_loss''': eval_loss.item(),
																				    '''tr_loss''': tr_loss,
																				    '''lr''': lr.item(),
																				}
																				tqdm.write(str(__UpperCAmelCase  )  )
																				self.logger.log(__UpperCAmelCase							,       commit=__UpperCAmelCase  )

																if i % args.save_steps == 0:
																				self.save_checkpoint(args.save_dir + F'''-e{epoch}-s{i}'''							,       state=__UpperCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   get_batched_dataset(__UpperCAmelCase							,       self.args.batch_size  )
								__magic_name__		:int   =   len(__UpperCAmelCase  ) // self.args.batch_size
								__magic_name__		:Dict   =   jnp.array(0							,       dtype=jnp.floataa  )
								__magic_name__		:Tuple   =   0
								for batch in tqdm(__UpperCAmelCase							,       total=__UpperCAmelCase							,       desc='''Evaluating ... '''  ):
												__magic_name__		:Dict   =   self.data_collator(__UpperCAmelCase  )
												__magic_name__		:str   =   self.val_step_fn(__UpperCAmelCase							,       **__UpperCAmelCase  )
												running_loss += jax_utils.unreplicate(metrics['''loss''']  )
												i += 1
								return running_loss / i







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   jax_utils.unreplicate(__UpperCAmelCase  )
								print(F'''SAVING CHECKPOINT IN {save_dir}'''							,       end=''' ... '''  )
								self.model_save_fn(__UpperCAmelCase							,       params=state.params  )
								with open(os.path.join(__UpperCAmelCase							,       '''opt_state.msgpack'''  )							,       '''wb'''  ) as f:
												f.write(to_bytes(state.opt_state  )  )
								joblib.dump(self.args							,       os.path.join(__UpperCAmelCase							,       '''args.joblib'''  )  )
								joblib.dump(self.data_collator							,       os.path.join(__UpperCAmelCase							,       '''data_collator.joblib'''  )  )
								with open(os.path.join(__UpperCAmelCase							,       '''training_state.json'''  )							,       '''w'''  ) as f:
												json.dump({'''step''': state.step.item()}							,       __UpperCAmelCase  )
								print('''DONE'''  )







def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				print(f'''RESTORING CHECKPOINT FROM {save_dir}''',	end=''' ... '''			)
				with open(os.path.join(_UpperCamelCase,	'''flax_model.msgpack'''			),	'''rb'''			) as f:
								__magic_name__		:Optional[int]   =   from_bytes(state.params,	f.read()			)

				with open(os.path.join(_UpperCamelCase,	'''opt_state.msgpack'''			),	'''rb'''			) as f:
								__magic_name__		:Tuple   =   from_bytes(state.opt_state,	f.read()			)

				__magic_name__		:int   =   joblib.load(os.path.join(_UpperCamelCase,	'''args.joblib'''			)			)
				__magic_name__		:List[Any]   =   joblib.load(os.path.join(_UpperCamelCase,	'''data_collator.joblib'''			)			)

				with open(os.path.join(_UpperCamelCase,	'''training_state.json'''			),	'''r'''			) as f:
								__magic_name__		:Tuple   =   json.load(_UpperCamelCase			)
				__magic_name__		:Union[str, Any]   =   training_state['''step''']

				print('''DONE'''			)
				return params, opt_state, step, args, data_collator

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   num_train_steps - warmup_steps
				__magic_name__		:Any   =   optax.linear_schedule(init_value=_UpperCamelCase,	end_value=_UpperCamelCase,	transition_steps=_UpperCamelCase			)
				__magic_name__		:Any   =   optax.linear_schedule(init_value=_UpperCamelCase,	end_value=1E-7,	transition_steps=_UpperCamelCase			)
				__magic_name__		:Optional[Any]   =   optax.join_schedules(schedules=[warmup_fn, decay_fn],	boundaries=[warmup_steps]			)
				return lr

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				def weight_decay_mask(snake_case			):
								__magic_name__		:int   =   traverse_util.flatten_dict(_UpperCamelCase			)
								__magic_name__		:List[str]   =   {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
								return traverse_util.unflatten_dict(_UpperCamelCase			)

				__magic_name__		:Optional[int]   =   scheduler_fn(_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase			)

				__magic_name__		:str   =   optax.adamw(learning_rate=_UpperCamelCase,	weight_decay=_UpperCamelCase,	mask=_UpperCamelCase			)
				return tx, lr











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 		a__      (		_UpperCamelCase      :			List[str]				):
   __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    				=     1_26
      __lowerCamelCase    				=     7
      __lowerCamelCase    				=     255.0
      __lowerCamelCase    				=     ''''''

   return config





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			List[str]				):
   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 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Union[str, Any]				):
   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 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			int			,_UpperCamelCase      :			Any				):
   __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    				=     1_26 if '''Jpeg''' in checkpoint_url else 2_56
   __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, 5_12, 5_12]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]				)
   elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      __lowerCamelCase    				=     torch.Size([1, 3, 10_24, 10_24]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]				)
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      # TODO values didn't match exactly here
      __lowerCamelCase    				=     torch.Size([1, 3, 10_24, 10_24]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]				)
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      __lowerCamelCase    				=     torch.Size([1, 3, 5_12, 5_12]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]				)
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      __lowerCamelCase    				=     torch.Size([1, 3, 10_24, 10_24]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]				)

   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__":
  a_								=       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_								=       parser.parse_args()
  convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






