




'''simple docstring'''




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Tuple:
	snake_case__  					=      copy.deepcopy(_UpperCAmelCase       )
	snake_case__  					=      sorted(_UpperCAmelCase       )
	snake_case__  					=      [bisect.bisect_right(_UpperCAmelCase				,							_UpperCAmelCase       ) for y in x]
	return quantized




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_=0       )      ->Dict:
	snake_case__  					=      np.arange(start=3				,							stop=_UpperCAmelCase				,							step=4       ).tolist() if k > 0 else [10]
	snake_case__  					=      _quantize(_UpperCAmelCase				,							_UpperCAmelCase       )
	# count number of elements per group
	snake_case__  					=      np.unique(_UpperCAmelCase				,							return_counts=_UpperCAmelCase       )[1]
	snake_case__  					=      [0] + bins + [np.inf]
	logger.info('Using {} as bins for aspect lengths quantization'.format(_UpperCAmelCase       )       )
	logger.info('Count of instances per bin: {}'.format(_UpperCAmelCase       )       )
	return groups







class 							__snake_case    (				__magic_name__       ):





	def __init__(		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		List[Any]:
		if not isinstance(UpperCAmelCase_			,					UpperCAmelCase_		):
			raise ValueError(
			    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(UpperCAmelCase_		)		)
		snake_case__  					=      sampler
		snake_case__  					=      group_ids
		snake_case__  					=      batch_size





	def __iter__(		self		)			->		Tuple:
		snake_case__  					=      defaultdict(UpperCAmelCase_		)
		snake_case__  					=      defaultdict(UpperCAmelCase_		)

		snake_case__  					=      0
		for idx in self.sampler:
			snake_case__  					=      self.group_ids[idx]
			buffer_per_group[group_id].append(UpperCAmelCase_		)
			samples_per_group[group_id].append(UpperCAmelCase_		)
			if len(buffer_per_group[group_id]		) == self.batch_size:
				yield buffer_per_group[group_id]  # TODO
				num_batches += 1
				del buffer_per_group[group_id]
			assert len(buffer_per_group[group_id]		) < self.batch_size

		# now we have run out of elements that satisfy
		# the group criteria, let's return the remaining
		# elements so that the size of the sampler is
		# deterministic
		snake_case__  					=      len(self		)
		snake_case__  					=      expected_num_batches - num_batches
		if num_remaining > 0:
			# for the remaining batches, group the batches by similar lengths
			snake_case__  					=      []
			for group_id, idxs in sorted(buffer_per_group.items()			,					key=lambda UpperCamelCase_		: x[0]		):
				batch_idx.extend(UpperCAmelCase_		)
				if len(UpperCAmelCase_		) >= self.batch_size:
					yield batch_idx[: self.batch_size]
					snake_case__  					=      batch_idx[self.batch_size :]
					num_remaining -= 1
			if len(UpperCAmelCase_		) > 0:
				yield batch_idx
				num_remaining -= 1
		assert num_remaining == 0





	def __len__(		self		)			->		Tuple:
		return (len(self.sampler		) + self.batch_size - 1) // self.batch_size










'''simple docstring'''




import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import FlavaImageProcessor, FlavaProcessor
			from transformers.models.flava.image_processing_flava import (
			    FLAVA_CODEBOOK_MEAN,
			    FLAVA_CODEBOOK_STD,
			    FLAVA_IMAGE_MEAN,
			    FLAVA_IMAGE_STD,
			)






@require_vision
class 							__snake_case    (				unittest.TestCase       ):





	def      _snake_case      (		self		)			->		Optional[Any]:
		snake_case__  					=      tempfile.mkdtemp()

		# fmt: off
		snake_case__  					=      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
		# fmt: on
		snake_case__  					=      os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['vocab_file']		)

		with open(self.vocab_file			,					'w'			,					encoding='utf-8'		) as fp:
			fp.write(''.join([x + '\n' for x in vocab_tokens]		)		)

		snake_case__  					=      {
		    'image_mean': FLAVA_IMAGE_MEAN,
		    'image_std': FLAVA_IMAGE_STD,
		    'do_normalize': True,
		    'do_resize': True,
		    'size': 224,
		    'do_center_crop': True,
		    'crop_size': 224,
		    'input_size_patches': 14,
		    'total_mask_patches': 75,
		    'mask_group_max_patches': None,
		    'mask_group_min_patches': 16,
		    'mask_group_min_aspect_ratio': 0.3,
		    'mask_group_max_aspect_ratio': None,
		    'codebook_do_resize': True,
		    'codebook_size': 112,
		    'codebook_do_center_crop': True,
		    'codebook_crop_size': 112,
		    'codebook_do_map_pixels': True,
		    'codebook_do_normalize': True,
		    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
		    'codebook_image_std': FLAVA_CODEBOOK_STD,
		}

		snake_case__  					=      os.path.join(self.tmpdirname			,					UpperCamelCase_		)
		with open(self.image_processor_file			,					'w'			,					encoding='utf-8'		) as fp:
			json.dump(UpperCamelCase_			,					UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
		return BertTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[Any]:
		return BertTokenizerFast.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
		return FlavaImageProcessor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Union[str, Any]:
		shutil.rmtree(self.tmpdirname		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      [np.random.randint(255			,					size=(3, 30, 400)			,					dtype=np.uinta		)]

		snake_case__  					=      [Image.fromarray(np.moveaxis(UpperCamelCase_			,					0			,					-1		)		) for x in image_inputs]

		return image_inputs





	def      _snake_case      (		self		)			->		Optional[int]:
		snake_case__  					=      self.get_tokenizer()
		snake_case__  					=      self.get_rust_tokenizer()
		snake_case__  					=      self.get_image_processor()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
		processor_slow.save_pretrained(self.tmpdirname		)
		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname			,					use_fast=UpperCamelCase_		)

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
		processor_fast.save_pretrained(self.tmpdirname		)
		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname		)

		self.assertEqual(processor_slow.tokenizer.get_vocab()			,					tokenizer_slow.get_vocab()		)
		self.assertEqual(processor_fast.tokenizer.get_vocab()			,					tokenizer_fast.get_vocab()		)
		self.assertEqual(tokenizer_slow.get_vocab()			,					tokenizer_fast.get_vocab()		)
		self.assertIsInstance(processor_slow.tokenizer			,					UpperCamelCase_		)
		self.assertIsInstance(processor_fast.tokenizer			,					UpperCamelCase_		)

		self.assertEqual(processor_slow.image_processor.to_json_string()			,					image_processor.to_json_string()		)
		self.assertEqual(processor_fast.image_processor.to_json_string()			,					image_processor.to_json_string()		)
		self.assertIsInstance(processor_slow.image_processor			,					UpperCamelCase_		)
		self.assertIsInstance(processor_fast.image_processor			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      FlavaProcessor(tokenizer=self.get_tokenizer()			,					image_processor=self.get_image_processor()		)
		processor.save_pretrained(self.tmpdirname		)

		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
		snake_case__  					=      self.get_image_processor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)

		snake_case__  					=      FlavaProcessor.from_pretrained(
		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)

		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)

		self.assertEqual(processor.image_processor.to_json_string()			,					image_processor_add_kwargs.to_json_string()		)
		self.assertIsInstance(processor.image_processor			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Union[str, Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      self.prepare_image_inputs()

		snake_case__  					=      image_processor(UpperCamelCase_			,					return_tensors='np'		)
		snake_case__  					=      processor(images=UpperCamelCase_			,					return_tensors='np'		)

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)

		# With rest of the args
		random.seed(1234		)
		snake_case__  					=      image_processor(
		    UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
		random.seed(1234		)
		snake_case__  					=      processor(
		    images=UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      'lower newer'

		snake_case__  					=      processor(text=UpperCamelCase_		)

		snake_case__  					=      tokenizer(UpperCamelCase_		)

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)





	def      _snake_case      (		self		)			->		Union[str, Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      'lower newer'
		snake_case__  					=      self.prepare_image_inputs()

		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)

		self.assertListEqual(list(inputs.keys()		)			,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']		)

		# add extra args
		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_image_mask=UpperCamelCase_		)

		self.assertListEqual(
		    list(inputs.keys()		)			,					[
		        'input_ids',
		        'token_type_ids',
		        'attention_mask',
		        'pixel_values',
		        'codebook_pixel_values',
		        'bool_masked_pos',
		    ]			,					)

		# test if it raises when no input is passed
		with pytest.raises(UpperCamelCase_		):
			processor()





	def      _snake_case      (		self		)			->		List[Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      'lower newer'
		snake_case__  					=      self.prepare_image_inputs()

		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)

		self.assertListEqual(list(inputs.keys()		)			,					processor.model_input_names		)



