




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    params

											SCREAMING_SNAKE_CASE_   	=    np.array(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    np.array([len(_lowerCamelCase		) for t in data]		)

											self.check()
											self.remove_long_sequences()
											self.remove_empty_sequences()
											self.remove_unknown_sequences()
											self.check()
											self.print_statistics()


				def __getitem__(							self:   str		,					_lowerCamelCase:   List[str]		):
											return (self.token_ids[index], self.lengths[index])


				def __len__(							self:   Optional[int]		):
											return len(self.lengths		)


				def     _A							(							self:   Tuple		):
											assert len(self.token_ids		) == len(self.lengths		)
											assert all(self.lengths[i] == len(self.token_ids[i]		) for i in range(len(self.lengths		)		)		)


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.params.max_model_input_size
											SCREAMING_SNAKE_CASE_   	=    self.lengths > max_len
											logger.info(f"Splitting {sum(_lowerCamelCase		)} too long sequences."		)

											def divide_chunks(_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Tuple		):
																		return [l[i : i + n] for i in range(0		,					len(_lowerCamelCase		)		,					_lowerCamelCase		)]

											SCREAMING_SNAKE_CASE_   	=    []
											SCREAMING_SNAKE_CASE_   	=    []
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE_   	=    self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

											for seq_, len_ in zip(self.token_ids		,					self.lengths		):
																		assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																		if len_ <= max_len:
																									new_tok_ids.append(seq_		)
																									new_lengths.append(len_		)
																		else:
																									SCREAMING_SNAKE_CASE_   	=    []
																									for sub_s in divide_chunks(seq_		,					max_len - 2		):
																																if sub_s[0] != cls_id:
																																							SCREAMING_SNAKE_CASE_   	=    np.insert(_lowerCamelCase		,					0		,					_lowerCamelCase		)
																																if sub_s[-1] != sep_id:
																																							SCREAMING_SNAKE_CASE_   	=    np.insert(_lowerCamelCase		,					len(_lowerCamelCase		)		,					_lowerCamelCase		)
																																assert len(_lowerCamelCase		) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(_lowerCamelCase		)

																									new_tok_ids.extend(_lowerCamelCase		)
																									new_lengths.extend([len(_lowerCamelCase		) for l in sub_seqs]		)

											SCREAMING_SNAKE_CASE_   	=    np.array(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    np.array(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    len(self		)
											SCREAMING_SNAKE_CASE_   	=    self.lengths > 11
											SCREAMING_SNAKE_CASE_   	=    self.token_ids[indices]
											SCREAMING_SNAKE_CASE_   	=    self.lengths[indices]
											SCREAMING_SNAKE_CASE_   	=    len(self		)
											logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."		)


				def     _A							(							self:   List[str]		):







											if "unk_token" not in self.params.special_tok_ids:
																		return
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.params.special_tok_ids['unk_token']
											SCREAMING_SNAKE_CASE_   	=    len(self		)
											SCREAMING_SNAKE_CASE_   	=    np.array([np.count_nonzero(a == unk_token_id		) for a in self.token_ids]		)
											SCREAMING_SNAKE_CASE_   	=    (unk_occs / self.lengths) < 0.5
											SCREAMING_SNAKE_CASE_   	=    self.token_ids[indices]
											SCREAMING_SNAKE_CASE_   	=    self.lengths[indices]
											SCREAMING_SNAKE_CASE_   	=    len(self		)
											logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."		)


				def     _A							(							self:   Union[str, Any]		):







											if not self.params.is_master:
																		return
											logger.info(f"{len(self		)} sequences"		)
											# data_len = sum(self.lengths)
											# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
											# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

											# unk_idx = self.params.special_tok_ids['unk_token']
											# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
											# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





				def     _A							(							self:   int		,					_lowerCamelCase:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    [t[0] for t in batch]
											SCREAMING_SNAKE_CASE_   	=    [t[1] for t in batch]
											assert len(_lowerCamelCase		) == len(_lowerCamelCase		)

											# Max for paddings
											SCREAMING_SNAKE_CASE_   	=    max(_lowerCamelCase		)

											# Pad token ids
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE_   	=    self.params.special_tok_ids['pad_token']
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.params.special_tok_ids['unk_token']
											SCREAMING_SNAKE_CASE_   	=    [list(t.astype(_lowerCamelCase		)		) + [pad_idx] * (max_seq_len_ - len(_lowerCamelCase		)) for t in token_ids]
											assert len(tk_		) == len(_lowerCamelCase		)
											assert all(len(_lowerCamelCase		) == max_seq_len_ for t in tk_		)

											SCREAMING_SNAKE_CASE_   	=    torch.tensor(tk_		)  # (bs, max_seq_len_)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor(_lowerCamelCase		)  # (bs)
											return tk_t, lg_t








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					"swin2sr"

				SCREAMING_SNAKE_CASE__					:							str	      =					{
				    "hidden_size": "embed_dim",
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}


				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Dict=64		,					_lowerCamelCase:   str=1		,					_lowerCamelCase:   Optional[int]=3		,					_lowerCamelCase:   List[Any]=1_80		,					_lowerCamelCase:   Optional[int]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Union[str, Any]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Tuple=8		,					_lowerCamelCase:   List[Any]=2.0		,					_lowerCamelCase:   Dict=True		,					_lowerCamelCase:   Dict=0.0		,					_lowerCamelCase:   int=0.0		,					_lowerCamelCase:   Optional[int]=0.1		,					_lowerCamelCase:   Any="gelu"		,					_lowerCamelCase:   int=False		,					_lowerCamelCase:   Tuple=0.02		,					_lowerCamelCase:   List[str]=1E-5		,					_lowerCamelCase:   List[Any]=2		,					_lowerCamelCase:   str=1.0		,					_lowerCamelCase:   List[Any]="1conv"		,					_lowerCamelCase:   str="pixelshuffle"		,					**_lowerCamelCase:   List[str]		,					):
											super().__init__(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    patch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    embed_dim
											SCREAMING_SNAKE_CASE_   	=    depths
											SCREAMING_SNAKE_CASE_   	=    len(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    num_heads
											SCREAMING_SNAKE_CASE_   	=    window_size
											SCREAMING_SNAKE_CASE_   	=    mlp_ratio
											SCREAMING_SNAKE_CASE_   	=    qkv_bias
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    drop_path_rate
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    use_absolute_embeddings
											SCREAMING_SNAKE_CASE_   	=    layer_norm_eps
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    upscale
											SCREAMING_SNAKE_CASE_   	=    img_range
											SCREAMING_SNAKE_CASE_   	=    resi_connection
											SCREAMING_SNAKE_CASE_   	=    upsampler


