'''simple docstring'''
import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class       __lowercase		(    _lowerCamelCase      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    PreTrainedTokenizerFast
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    True
     SCREAMING_SNAKE_CASE__		 =    '''tokenizer_file'''







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	False  # because we don't have pretrained_vocab_files_map
         super().setUp()
         __UpperCamelCase						:						Optional[Any]		     =	True

         __UpperCamelCase						:						str		     =	['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
         __UpperCamelCase						:						List[str]		     =	'SaulLu/dummy-tokenizer-bytelevel-bpe'

         # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
         __UpperCamelCase						:						Any		     =	[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

         __UpperCamelCase						:						str		     =	PreTrainedTokenizerFast.from_pretrained(model_paths[0]     )
         tokenizer.save_pretrained(self.tmpdirname     )







     def lowerCAmelCase  (		self     ):
         # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
         # model
         pass







     def lowerCAmelCase  (		self     ):
         # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
         # model
         pass







     def lowerCAmelCase  (		self     ):
         # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
         # model
         pass







     def lowerCAmelCase  (		self     ):
         # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
         pass







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						str		     =	self.tmpdirname
         # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                 try:
                     __UpperCamelCase						:						Tuple		     =	tempfile.mkdtemp()
                     __UpperCamelCase						:						List[str]		     =	self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,      **lowerCAmelCase_     )

                     tokenizer.save_pretrained(self.tmpdirname     )
                     super().test_training_new_tokenizer()
                 finally:
                     # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                     # is restored
                     shutil.rmtree(self.tmpdirname     )
                     __UpperCamelCase						:						Tuple		     =	tmpdirname_orig







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	self.tmpdirname
         # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                 try:
                     __UpperCamelCase						:						Union[str, Any]		     =	tempfile.mkdtemp()
                     __UpperCamelCase						:						Any		     =	self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,      **lowerCAmelCase_     )

                     tokenizer.save_pretrained(self.tmpdirname     )
                     super().test_training_new_tokenizer_with_special_tokens_change()
                 finally:
                     # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                     # is restored
                     shutil.rmtree(self.tmpdirname     )
                     __UpperCamelCase						:						List[Any]		     =	tmpdirname_orig







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						str		     =	self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

         __UpperCamelCase						:						List[str]		     =	('a' for _ in range(1_0_0_0     ))
         __UpperCamelCase						:						Dict		     =	tokenizer.train_new_from_iterator(text_iterator=lowerCAmelCase_  ,      length=1_0_0_0  ,      vocab_size=5_0     )

         __UpperCamelCase						:						Dict		     =	new_tokenizer.encode('a🤗'     )
         self.assertEqual(lowerCAmelCase_  ,      [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]     )






@require_tokenizers
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	AutoTokenizer.from_pretrained('bert-base-cased'     )
         __UpperCamelCase						:						str		     =	json.loads(tokenizer._tokenizer.to_str()     )
         __UpperCamelCase						:						Optional[int]		     =	len(lowerCAmelCase_     )

         with tempfile.TemporaryDirectory() as tmp_dir:
             # Hack to save this in the tokenizer_config.json
             __UpperCamelCase						:						int		     =	['tokenizer.4.0.0.json']
             tokenizer.save_pretrained(lowerCAmelCase_     )
             json.dump(lowerCAmelCase_  ,      open(os.path.join(lowerCAmelCase_  ,      'tokenizer.4.0.0.json'     )  ,      'w'     )     )

             # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
             __UpperCamelCase						:						Optional[Any]		     =	AutoTokenizer.from_pretrained(lowerCAmelCase_     )
             self.assertEqual(len(lowerCAmelCase_     )  ,      len(lowerCAmelCase_     ) + 1     )
             __UpperCamelCase						:						Optional[Any]		     =	json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertIn('huggingface'  ,      json_tokenizer['model']['vocab']     )

             # Will need to be adjusted if we reach v42 and this test is still here.
             # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
             shutil.move(os.path.join(lowerCAmelCase_  ,      'tokenizer.4.0.0.json'     )  ,      os.path.join(lowerCAmelCase_  ,      'tokenizer.42.0.0.json'     )     )
             __UpperCamelCase						:						Optional[Any]		     =	['tokenizer.42.0.0.json']
             tokenizer.save_pretrained(lowerCAmelCase_     )
             __UpperCamelCase						:						Dict		     =	AutoTokenizer.from_pretrained(lowerCAmelCase_     )
             self.assertEqual(len(lowerCAmelCase_     )  ,      len(lowerCAmelCase_     )     )
             __UpperCamelCase						:						Any		     =	json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertNotIn('huggingface'  ,      json_tokenizer['model']['vocab']     )







     def lowerCAmelCase  (		self     ):
         # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
         __UpperCamelCase						:						Tuple		     =	'hf-internal-testing/test-two-tokenizers'

         # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
         __UpperCamelCase						:						Optional[int]		     =	AutoTokenizer.from_pretrained(lowerCAmelCase_     )
         self.assertEqual(len(lowerCAmelCase_     )  ,      2_8_9_9_7     )
         __UpperCamelCase						:						str		     =	json.loads(tokenizer._tokenizer.to_str()     )
         self.assertIn('huggingface'  ,      json_tokenizer['model']['vocab']     )

         # Testing an older version by monkey-patching the version in the module it's used.
         import transformers as old_transformers

         __UpperCamelCase						:						List[Any]		     =	'3.0.0'
         __UpperCamelCase						:						Any		     =	old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCAmelCase_     )
         self.assertEqual(len(lowerCAmelCase_     )  ,      2_8_9_9_6     )
         __UpperCamelCase						:						Tuple		     =	json.loads(old_tokenizer._tokenizer.to_str()     )
         self.assertNotIn('huggingface'  ,      json_tokenizer['model']['vocab']     )






@require_tokenizers
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         # See https://github.com/huggingface/transformers/pull/12550
         # and https://github.com/huggingface/tokenizers/issues/537
         __UpperCamelCase						:						Optional[Any]		     =	PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'     )
         __UpperCamelCase						:						Tuple		     =	'The Matrix is a 1999 science fiction action film.'

         with concurrent.futures.ThreadPoolExecutor() as executor:
             __UpperCamelCase						:						Optional[Any]		     =	[executor.submit(self.fetch  ,      lowerCAmelCase_  ,      lowerCAmelCase_     ) for i in range(1_0     )]
             __UpperCamelCase						:						Union[str, Any]		     =	[future.result() for future in futures]
             self.assertEqual(lowerCAmelCase_  ,      [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0     )]     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         return tokenizer.encode(lowerCAmelCase_  ,      truncation='longest_first'  ,      padding='longest'     )





'''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 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]=sys.maxsize     ) ->							Tuple:
        snake_case_    							=				"bilinear"
        snake_case_    							=				max_size
        snake_case_    							=				short_edge_length


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

              if img.dtype == np.uinta:
                    snake_case_    							=				Image.fromarray(lowerCAmelCase_     )
                    snake_case_    							=				pil_image.resize((neww, newh)						,			PILImageResampling.BILINEAR     )
                    snake_case_    							=				np.asarray(lowerCAmelCase_     )
              else:
                    snake_case_    							=				img.permute(2						,			0						,			1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
                    snake_case_    							=				nn.functional.interpolate(
                        lowerCAmelCase_						,			(newh, neww)						,			mode=self.interp_method						,			align_corners=lowerCAmelCase_     ).squeeze(0     )
              img_augs.append(lowerCAmelCase_     )

        return img_augs



class 			lowerCAmelCase__ :



  '''simple docstring'''






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

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple     ) ->							Any:
        snake_case_    							=				tuple(max(lowerCAmelCase_     ) for s in zip(*[img.shape for img in images]     )     )
        snake_case_    							=				[im.shape[-2:] for im in images]
        snake_case_    							=				[
            nn.functional.pad(
                lowerCAmelCase_						,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,			value=self.pad_value						,			)
            for size, im in zip(lowerCAmelCase_						,			lowerCAmelCase_     )
        ]

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


  def __call__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]=False     ) ->							int:
        with torch.no_grad():
              if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				[images]
              if single_image:
                    assert len(lowerCAmelCase_     ) == 1
              for i in range(len(lowerCAmelCase_     )     ):
                    if isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(lowerCAmelCase_						,			images.pop(lowerCAmelCase_     ).to(self.device     ).float()     )
                    elif not isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(
                              lowerCAmelCase_						,			torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_     )						,			input_format=self.input_format     )     )
                              .to(self.device     )
                              .float()						,			)
            # resize smallest edge
              snake_case_    							=				torch.tensor([im.shape[:2] for im in images]     )
              snake_case_    							=				self.aug(lowerCAmelCase_     )
              # 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
              snake_case_    							=				[self.normalizer(lowerCAmelCase_     ) for x in images]
              # now pad them to do the following operations
              snake_case_							,						snake_case_    							=				self.pad(lowerCAmelCase_     )
              # Normalize

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

def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[Any]      ):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes




def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Tuple[int, int]      ):
      assert torch.isfinite(_a      ).all(), "Box tensor contains infinite or NaN!"
      snake_case_							,						snake_case_    							=				box_size
      tensor[:, 0].clamp_(min=0			,       max=_a      )
      tensor[:, 1].clamp_(min=0			,       max=_a      )
      tensor[:, 2].clamp_(min=0			,       max=_a      )
      tensor[:, 3].clamp_(min=0			,       max=_a      )

