


"""simple docstring"""





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class 						snake_case_:






     def __init__(		self     :    Optional[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[int]=sys.maxsize ):
           lowerCAmelCase    :		Optional[Any]				=      "bilinear"
           lowerCAmelCase    :		Dict				=      max_size
           lowerCAmelCase    :		List[Any]				=      short_edge_length






     def __call__(		self     :    Any    , UpperCamelCase_     :    int ):
           lowerCAmelCase    :		Any				=      []
           for img in imgs:
                 lowerCAmelCase    :		int				=      img.shape[:2]
                 # later: provide list and randomly choose index for resize
                 lowerCAmelCase    :		Tuple				=      np.random.randint(self.short_edge_length[0]    , self.short_edge_length[1] + 1 )
                 if size == 0:
                       return img
                 lowerCAmelCase    :		List[Any]				=      size * 1.0 / min(UpperCamelCase__    , UpperCamelCase__ )
                 if h < w:
                       lowerCAmelCase    :		Tuple				=      size, scale * w
                 else:
                       lowerCAmelCase    :		str				=      scale * h, size
                 if max(UpperCamelCase__    , UpperCamelCase__ ) > self.max_size:
                       lowerCAmelCase    :		Union[str, Any]				=      self.max_size * 1.0 / max(UpperCamelCase__    , UpperCamelCase__ )
                       lowerCAmelCase    :		Optional[int]				=      newh * scale
                       lowerCAmelCase    :		str				=      neww * scale
                 lowerCAmelCase    :		Union[str, Any]				=      int(neww + 0.5 )
                 lowerCAmelCase    :		int				=      int(newh + 0.5 )

                 if img.dtype == np.uinta:
                       lowerCAmelCase    :		List[Any]				=      Image.fromarray(UpperCamelCase__ )
                       lowerCAmelCase    :		str				=      pil_image.resize((neww, newh)    , PILImageResampling.BILINEAR )
                       lowerCAmelCase    :		List[Any]				=      np.asarray(UpperCamelCase__ )
                 else:
                       lowerCAmelCase    :		int				=      img.permute(2    , 0    , 1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
                       lowerCAmelCase    :		Tuple				=      nn.functional.interpolate(
                           UpperCamelCase__    , (newh, neww)    , mode=self.interp_method    , align_corners=UpperCamelCase__ ).squeeze(0 )
                 img_augs.append(UpperCamelCase__ )

           return img_augs



class 						snake_case_:






     def __init__(		self     :    int    , UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		Tuple				=      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    , cfg.INPUT.MAX_SIZE_TEST )
           lowerCAmelCase    :		Optional[int]				=      cfg.INPUT.FORMAT
           lowerCAmelCase    :		Union[str, Any]				=      cfg.SIZE_DIVISIBILITY
           lowerCAmelCase    :		Dict				=      cfg.PAD_VALUE
           lowerCAmelCase    :		List[str]				=      cfg.INPUT.MAX_SIZE_TEST
           lowerCAmelCase    :		List[Any]				=      cfg.MODEL.DEVICE
           lowerCAmelCase    :		Any				=      torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )    , 1    , 1 )
           lowerCAmelCase    :		Dict				=      torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )    , 1    , 1 )
           lowerCAmelCase    :		Union[str, Any]				=      lambda UpperCamelCase_ : (x - self.pixel_mean) / self.pixel_std






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    Optional[Any] ):
           lowerCAmelCase    :		str				=      tuple(max(UpperCamelCase__ ) for s in zip(*[img.shape for img in images] ) )
           lowerCAmelCase    :		Optional[Any]				=      [im.shape[-2:] for im in images]
           lowerCAmelCase    :		Optional[Any]				=      [
               nn.functional.pad(
                   UpperCamelCase__    , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    , value=self.pad_value    , )
               for size, im in zip(UpperCamelCase__    , UpperCamelCase__ )
           ]

           return torch.stack(UpperCamelCase__ ), torch.tensor(UpperCamelCase__ )






     def __call__(		self     :    Any    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    int=False ):


           with torch.no_grad():
                 if not isinstance(UpperCamelCase__    , UpperCamelCase__ ):
                       lowerCAmelCase    :		List[Any]				=      [images]
                 if single_image:
                       assert len(UpperCamelCase__ ) == 1
                 for i in range(len(UpperCamelCase__ ) ):
                       if isinstance(images[i]    , torch.Tensor ):
                             images.insert(UpperCamelCase__    , images.pop(UpperCamelCase__ ).to(self.device ).float() )
                       elif not isinstance(images[i]    , torch.Tensor ):
                             images.insert(
                                 UpperCamelCase__    , torch.as_tensor(img_tensorize(images.pop(UpperCamelCase__ )    , input_format=self.input_format ) )
                                 .to(self.device )
                                 .float()    , )
            # resize smallest edge
                 lowerCAmelCase    :		Tuple				=      torch.tensor([im.shape[:2] for im in images] )
                 lowerCAmelCase    :		Optional[int]				=      self.aug(UpperCamelCase__ )
                 # transpose images and convert to torch tensors
                 # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                 # now normalize before pad to avoid useless arithmetic
                 lowerCAmelCase    :		Optional[Any]				=      [self.normalizer(UpperCamelCase__ ) for x in images]
                 # now pad them to do the following operations
                 lowerCAmelCase    :		List[str]				=      self.pad(UpperCamelCase__ )
                 # Normalize

                 if self.size_divisibility > 0:
                       raise NotImplementedError()
                 # pad
                 lowerCAmelCase    :		str				=      torch.true_divide(UpperCamelCase__    , UpperCamelCase__ )
                 if single_image:
                       return images[0], sizes[0], scales_yx[0]
                 else:
                       return images, sizes, scales_yx



def    _snake_case  (  _snake_case :	Optional[int]       ,      _snake_case :	Union[str, Any]	):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes






def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Tuple	):
      assert torch.isfinite(__SCREAMING_SNAKE_CASE	).all(), "Box tensor contains infinite or NaN!"
      lowerCAmelCase    :		str				=      box_size
      tensor[:, 0].clamp_(min=0       ,      max=__SCREAMING_SNAKE_CASE	)
      tensor[:, 1].clamp_(min=0       ,      max=__SCREAMING_SNAKE_CASE	)
      tensor[:, 2].clamp_(min=0       ,      max=__SCREAMING_SNAKE_CASE	)
      tensor[:, 3].clamp_(min=0       ,      max=__SCREAMING_SNAKE_CASE	)





"""simple docstring"""


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      str         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      int         =  True
				SCREAMING_SNAKE_CASE_   :      Tuple         =  False
				SCREAMING_SNAKE_CASE_   :      int         =  """tokenizer_file"""
				SCREAMING_SNAKE_CASE_   :      List[str]         =  {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

				def 							lowercase_     (	self							:				List[Any])->   Dict:



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase:       Optional[Any]          =			BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
						tokenizer.save_pretrained(self.tmpdirname)

				def 							lowercase_     (	self							:				List[Any]     ,  **UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.get_rust_tokenizer()

						__lowerCAmelCase:       int          =			["The quick brown fox</s>", "jumps over the lazy dog</s>"]
						__lowerCAmelCase:       List[str]          =			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

						__lowerCAmelCase:       List[str]          =			tokenizer.batch_encode_plus(UpperCamelCase__)["input_ids"]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			tokenizer.batch_decode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple=6)->   Tuple:



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Optional[Any]          =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										# tokenizer_r.pad_token = None # Hotfixing padding = None
										# Simple input
										__lowerCAmelCase:       Dict          =			"This is a simple input"
										__lowerCAmelCase:       str          =			["This is a simple input 1", "This is a simple input 2"]
										__lowerCAmelCase:       int          =			("This is a simple input", "This is a pair")
										__lowerCAmelCase:       Union[str, Any]          =			[
										    ("This is a simple input 1", "This is a simple input 2"),
										    ("This is a simple pair 1", "This is a simple pair 2"),
										]

										# Simple input tests
										try:
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)

												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
										except ValueError:
												self.fail("Bloom Tokenizer should be able to deal with padding")

										__lowerCAmelCase:       Tuple          =			None  # Hotfixing padding = None
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.get_rust_tokenizer()
						__lowerCAmelCase:       List[str]          =			load_dataset("xnli"     ,  "all_languages"     ,  split="test"     ,  streaming=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			next(iter(UpperCamelCase__))["premise"]  # pick up one data
						__lowerCAmelCase:       Any          =			list(sample_data.values())

						__lowerCAmelCase:       int          =			list(map(tokenizer.encode     ,  UpperCamelCase__))
						__lowerCAmelCase:       str          =			[tokenizer.decode(UpperCamelCase__     ,  clean_up_tokenization_spaces=UpperCamelCase__) for x in output_tokens]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				Optional[int])->   str:



						'''simple docstring'''


						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)     ,  1)
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])     ,  1)


