
"""simple docstring"""




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

from utils import logger



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   params

											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array([len(SCREAMING_SNAKE_CASE__    ) 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   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return (self.token_ids[index], self.lengths[index])



				def __len__(self    )					->		int:



											"""simple docstring"""




											return len(self.lengths    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											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 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.params.max_model_input_size
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.lengths > max_len
											logger.info(F'''Splitting {sum(SCREAMING_SNAKE_CASE__    )} too long sequences.'''    )

											def divide_chunks(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		return [l[i : i + n] for i in range(0   ,       len(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )]

											SCREAMING_SNAKE_CASE__						:       Tuple           =   []
											SCREAMING_SNAKE_CASE__						:       Any           =   []
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
											else:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   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__						:       Union[str, Any]           =   []
																									for sub_s in divide_chunks(seq_   ,       max_len - 2    ):
																																if sub_s[0] != cls_id:
																																							SCREAMING_SNAKE_CASE__						:       int           =   np.insert(SCREAMING_SNAKE_CASE__   ,       0   ,       SCREAMING_SNAKE_CASE__    )
																																if sub_s[-1] != sep_id:
																																							SCREAMING_SNAKE_CASE__						:       str           =   np.insert(SCREAMING_SNAKE_CASE__   ,       len(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )
																																assert len(SCREAMING_SNAKE_CASE__    ) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(SCREAMING_SNAKE_CASE__    )

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

											SCREAMING_SNAKE_CASE__						:       Dict           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   np.array(SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.lengths > 11
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.token_ids[indices]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.lengths[indices]
											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [t[0] for t in batch]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [t[1] for t in batch]
											assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    )

											# Max for paddings
											SCREAMING_SNAKE_CASE__						:       int           =   max(SCREAMING_SNAKE_CASE__    )

											# Pad token ids
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE__						:       Any           =   self.params.special_tok_ids["""pad_token"""]
											else:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.params.special_tok_ids["""unk_token"""]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [list(t.astype(SCREAMING_SNAKE_CASE__    )    ) + [pad_idx] * (max_seq_len_ - len(SCREAMING_SNAKE_CASE__    )) for t in token_ids]
											assert len(tk_    ) == len(SCREAMING_SNAKE_CASE__    )
											assert all(len(SCREAMING_SNAKE_CASE__    ) == max_seq_len_ for t in tk_    )

											SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor(tk_    )  # (bs, max_seq_len_)
											SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor(SCREAMING_SNAKE_CASE__    )  # (bs)
											return tk_t, lg_t











import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






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


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Optional[int]				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Dict				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
