






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 __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      tempfile.mkdtemp()

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

							_UpperCAmelCase     =      {
							    '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,
							}

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

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							shutil.rmtree(self.tmpdirname   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

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

							return image_inputs

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

							_UpperCAmelCase     =      FlavaProcessor(tokenizer=UpperCAmelCase	,   image_processor=UpperCAmelCase   )
							processor_slow.save_pretrained(self.tmpdirname   )
							_UpperCAmelCase     =      FlavaProcessor.from_pretrained(self.tmpdirname	,   use_fast=UpperCAmelCase   )

							_UpperCAmelCase     =      FlavaProcessor(tokenizer=UpperCAmelCase	,   image_processor=UpperCAmelCase   )
							processor_fast.save_pretrained(self.tmpdirname   )
							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

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

							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_image_processor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      FlavaProcessor(tokenizer=UpperCAmelCase	,   image_processor=UpperCAmelCase   )

							_UpperCAmelCase     =      self.prepare_image_inputs()

							_UpperCAmelCase     =      image_processor(UpperCAmelCase	,   return_tensors='np'   )
							_UpperCAmelCase     =      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   )
							_UpperCAmelCase     =      image_processor(
							    UpperCAmelCase	,   return_image_mask=UpperCAmelCase	,   return_codebook_pixels=UpperCAmelCase	,   return_tensors='np'   )
							random.seed(1234   )
							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_image_processor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      FlavaProcessor(tokenizer=UpperCAmelCase	,   image_processor=UpperCAmelCase   )

							_UpperCAmelCase     =      'lower newer'

							_UpperCAmelCase     =      processor(text=UpperCAmelCase   )

							_UpperCAmelCase     =      tokenizer(UpperCAmelCase   )

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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_image_processor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      FlavaProcessor(tokenizer=UpperCAmelCase	,   image_processor=UpperCAmelCase   )

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

							_UpperCAmelCase     =      processor(text=UpperCAmelCase	,   images=UpperCAmelCase   )

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

							# add extra args
							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_image_processor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      FlavaProcessor(tokenizer=UpperCAmelCase	,   image_processor=UpperCAmelCase   )

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

							_UpperCAmelCase     =      processor.batch_decode(UpperCAmelCase   )
							_UpperCAmelCase     =      tokenizer.batch_decode(UpperCAmelCase   )

							self.assertListEqual(UpperCAmelCase	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_image_processor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      FlavaProcessor(tokenizer=UpperCAmelCase	,   image_processor=UpperCAmelCase   )

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

							_UpperCAmelCase     =      processor(text=UpperCAmelCase	,   images=UpperCAmelCase   )

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








import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece.model'   )
							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   0   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   1   )

							self.assertIn(
							    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
							    ' which is not implemented in the fast tokenizers.'	,   str(w[0].message   )	,   )
