



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      UpperCAmelCase_						(     unittest.TestCase ):

      '''simple docstring'''




      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      tempfile.mkdtemp()

             # fmt: off
             __SCREAMING_SNAKE_CASE								=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
             # fmt: on
             __SCREAMING_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]	)	)

             __SCREAMING_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,
             }

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






      def  _A			(					self   ,							**_A	):







             '''simple docstring'''

             return BertTokenizer.from_pretrained(self.tmpdirname   ,							**lowerCamelCase__	)






      def  _A			(					self   ,							**_A	):







             '''simple docstring'''

             return BertTokenizerFast.from_pretrained(self.tmpdirname   ,							**lowerCamelCase__	)






      def  _A			(					self   ,							**_A	):







             '''simple docstring'''

             return FlavaImageProcessor.from_pretrained(self.tmpdirname   ,							**lowerCamelCase__	)






      def  _A			(					self	):







             '''simple docstring'''

             shutil.rmtree(self.tmpdirname	)






      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      [np.random.randint(255   ,							size=(3, 30, 400)   ,							dtype=np.uinta	)]

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

             return image_inputs






      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      self.get_tokenizer()
             __SCREAMING_SNAKE_CASE								=      self.get_rust_tokenizer()
             __SCREAMING_SNAKE_CASE								=      self.get_image_processor()

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=lowerCamelCase__   ,							image_processor=lowerCamelCase__	)
             processor_slow.save_pretrained(self.tmpdirname	)
             __SCREAMING_SNAKE_CASE								=      FlavaProcessor.from_pretrained(self.tmpdirname   ,							use_fast=lowerCamelCase__	)

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=lowerCamelCase__   ,							image_processor=lowerCamelCase__	)
             processor_fast.save_pretrained(self.tmpdirname	)
             __SCREAMING_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   ,							lowerCamelCase__	)
             self.assertIsInstance(processor_fast.tokenizer   ,							lowerCamelCase__	)

             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   ,							lowerCamelCase__	)
             self.assertIsInstance(processor_fast.image_processor   ,							lowerCamelCase__	)






      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=self.get_tokenizer()   ,							image_processor=self.get_image_processor()	)
             processor.save_pretrained(self.tmpdirname	)

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

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

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

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






      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      self.get_image_processor()
             __SCREAMING_SNAKE_CASE								=      self.get_tokenizer()

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=lowerCamelCase__   ,							image_processor=lowerCamelCase__	)

             __SCREAMING_SNAKE_CASE								=      self.prepare_image_inputs()

             __SCREAMING_SNAKE_CASE								=      image_processor(lowerCamelCase__   ,							return_tensors='np'	)
             __SCREAMING_SNAKE_CASE								=      processor(images=lowerCamelCase__   ,							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(1_234	)
             __SCREAMING_SNAKE_CASE								=      image_processor(
                 lowerCamelCase__   ,							return_image_mask=lowerCamelCase__   ,							return_codebook_pixels=lowerCamelCase__   ,							return_tensors='np'	)
             random.seed(1_234	)
             __SCREAMING_SNAKE_CASE								=      processor(
                 images=lowerCamelCase__   ,							return_image_mask=lowerCamelCase__   ,							return_codebook_pixels=lowerCamelCase__   ,							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  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      self.get_image_processor()
             __SCREAMING_SNAKE_CASE								=      self.get_tokenizer()

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=lowerCamelCase__   ,							image_processor=lowerCamelCase__	)

             __SCREAMING_SNAKE_CASE								=      "lower newer"

             __SCREAMING_SNAKE_CASE								=      processor(text=lowerCamelCase__	)

             __SCREAMING_SNAKE_CASE								=      tokenizer(lowerCamelCase__	)

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






      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      self.get_image_processor()
             __SCREAMING_SNAKE_CASE								=      self.get_tokenizer()

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=lowerCamelCase__   ,							image_processor=lowerCamelCase__	)

             __SCREAMING_SNAKE_CASE								=      "lower newer"
             __SCREAMING_SNAKE_CASE								=      self.prepare_image_inputs()

             __SCREAMING_SNAKE_CASE								=      processor(text=lowerCamelCase__   ,							images=lowerCamelCase__	)

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

             # add extra args
             __SCREAMING_SNAKE_CASE								=      processor(text=lowerCamelCase__   ,							images=lowerCamelCase__   ,							return_codebook_pixels=lowerCamelCase__   ,							return_image_mask=lowerCamelCase__	)

             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(lowerCamelCase__	):
                    processor()






      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      self.get_image_processor()
             __SCREAMING_SNAKE_CASE								=      self.get_tokenizer()

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=lowerCamelCase__   ,							image_processor=lowerCamelCase__	)

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

             __SCREAMING_SNAKE_CASE								=      processor.batch_decode(lowerCamelCase__	)
             __SCREAMING_SNAKE_CASE								=      tokenizer.batch_decode(lowerCamelCase__	)

             self.assertListEqual(lowerCamelCase__   ,							lowerCamelCase__	)






      def  _A			(					self	):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE								=      self.get_image_processor()
             __SCREAMING_SNAKE_CASE								=      self.get_tokenizer()

             __SCREAMING_SNAKE_CASE								=      FlavaProcessor(tokenizer=lowerCamelCase__   ,							image_processor=lowerCamelCase__	)

             __SCREAMING_SNAKE_CASE								=      "lower newer"
             __SCREAMING_SNAKE_CASE								=      self.prepare_image_inputs()

             __SCREAMING_SNAKE_CASE								=      processor(text=lowerCamelCase__   ,							images=lowerCamelCase__	)

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











from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class      UpperCAmelCase_						:

						'''simple docstring'''




						UpperCamelCase__    :				List[str]       						=						('''_front''', '''_back''', '''_len''')

						@dataclass
						class      UpperCAmelCase_						:

												'''simple docstring'''




												UpperCamelCase__    :				Any       						=						None
												UpperCamelCase__    :				Deque._Node | None       						=						None
												UpperCamelCase__    :				Deque._Node | None       						=						None







						class      UpperCAmelCase_						:

												'''simple docstring'''




												UpperCamelCase__    :				Optional[int]       						=						('''_cur''',)






												def __init__(					self   ,							_A	):







																			'''simple docstring'''

																			__SCREAMING_SNAKE_CASE								=      cur






												def __iter__(					self	):







																			'''simple docstring'''

																			return self






												def  _A			(					self	):







																			'''simple docstring'''

																			if self._cur is None:
																										# finished iterating
																										raise StopIteration
																			__SCREAMING_SNAKE_CASE								=      self._cur.val
																			__SCREAMING_SNAKE_CASE								=      self._cur.next_node

																			return val







						def __init__(					self   ,							_A = None	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      0

													if iterable is not None:
																				# append every value to the deque
																				for val in iterable:
																											self.append(_A	)






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self._Node(_A   ,							_A   ,							_A	)
													if self.is_empty():
																				# front = back
																				__SCREAMING_SNAKE_CASE								=      __SCREAMING_SNAKE_CASE								=      node
																				__SCREAMING_SNAKE_CASE								=      1
													else:
																				# connect nodes
																				__SCREAMING_SNAKE_CASE								=      node
																				__SCREAMING_SNAKE_CASE								=      self._back
																				__SCREAMING_SNAKE_CASE								=      node  # assign new back to the new node

																				self._len += 1

																				# make sure there were no errors
																				assert not self.is_empty(), "Error on appending value."






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self._Node(_A   ,							_A   ,							_A	)
													if self.is_empty():
																				# front = back
																				__SCREAMING_SNAKE_CASE								=      __SCREAMING_SNAKE_CASE								=      node
																				__SCREAMING_SNAKE_CASE								=      1
													else:
																				# connect nodes
																				__SCREAMING_SNAKE_CASE								=      self._front
																				__SCREAMING_SNAKE_CASE								=      node
																				__SCREAMING_SNAKE_CASE								=      node  # assign new front to the new node

																				self._len += 1

																				# make sure there were no errors
																				assert not self.is_empty(), "Error on appending value."






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													for val in iterable:
																				self.append(_A	)






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													for val in iterable:
																				self.appendleft(_A	)






						def  _A			(					self	):







													'''simple docstring'''

													assert not self.is_empty(), "Deque is empty."

													__SCREAMING_SNAKE_CASE								=      self._back
													__SCREAMING_SNAKE_CASE								=      self._back.prev_node  # set new back
													# drop the last node - python will deallocate memory automatically
													__SCREAMING_SNAKE_CASE								=      None

													self._len -= 1

													return topop.val






						def  _A			(					self	):







													'''simple docstring'''

													assert not self.is_empty(), "Deque is empty."

													__SCREAMING_SNAKE_CASE								=      self._front
													__SCREAMING_SNAKE_CASE								=      self._front.next_node  # set new front and drop the first node
													__SCREAMING_SNAKE_CASE								=      None

													self._len -= 1

													return topop.val






						def  _A			(					self	):







													'''simple docstring'''

													return self._front is None






						def __len__(					self	):







													'''simple docstring'''

													return self._len






						def __eq__(					self   ,							_A	):







													'''simple docstring'''

													if not isinstance(_A   ,							_A	):
																				return NotImplemented

													__SCREAMING_SNAKE_CASE								=      self._front
													__SCREAMING_SNAKE_CASE								=      other._front

													# if the length of the dequeues are not the same, they are not equal
													if len(self	) != len(_A	):
																				return False

													while me is not None and oth is not None:
																				# compare every value
																				if me.val != oth.val:
																											return False
																				__SCREAMING_SNAKE_CASE								=      me.next_node
																				__SCREAMING_SNAKE_CASE								=      oth.next_node

													return True






						def __iter__(					self	):







													'''simple docstring'''

													return Deque._Iterator(self._front	)






						def __repr__(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      []
													__SCREAMING_SNAKE_CASE								=      self._front
													while aux is not None:
																				# append the values in a list to display
																				values_list.append(aux.val	)
																				__SCREAMING_SNAKE_CASE								=      aux.next_node

													return f"""[{", ".join(repr(_A	) for val in values_list	)}]"""


if __name__ == "__main__":
			import doctest

			doctest.testmod()






