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    __snake_case   (     self    :						List[str]					)	->							Any:
								_UpperCamelCase       :    List[Any]					  =					tempfile.mkdtemp()

								# fmt: off
								_UpperCamelCase       :    Tuple					  =					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
								# fmt: on
								_UpperCamelCase       :    Optional[Any]					  =					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       :    str					  =					{
								    'image_mean': FLAVA_IMAGE_MEAN,
								    'image_std': FLAVA_IMAGE_STD,
								    'do_normalize': True,
								    'do_resize': True,
								    'size': 2_2_4,
								    'do_center_crop': True,
								    'crop_size': 2_2_4,
								    'input_size_patches': 1_4,
								    'total_mask_patches': 7_5,
								    'mask_group_max_patches': None,
								    'mask_group_min_patches': 1_6,
								    'mask_group_min_aspect_ratio': 0.3,
								    'mask_group_max_aspect_ratio': None,
								    'codebook_do_resize': True,
								    'codebook_size': 1_1_2,
								    'codebook_do_center_crop': True,
								    'codebook_crop_size': 1_1_2,
								    'codebook_do_map_pixels': True,
								    'codebook_do_normalize': True,
								    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
								    'codebook_image_std': FLAVA_CODEBOOK_STD,
								}

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

	def    __snake_case   (     self    :						Tuple    ,     **lowerCamelCase    :						List[Any]					)	->							List[Any]:
								return BertTokenizer.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					)

	def    __snake_case   (     self    :						Tuple    ,     **lowerCamelCase    :						List[Any]					)	->							List[Any]:
								return BertTokenizerFast.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					)

	def    __snake_case   (     self    :						Dict    ,     **lowerCamelCase    :						Optional[Any]					)	->							Any:
								return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					)

	def    __snake_case   (     self    :						List[Any]					)	->							List[Any]:
								shutil.rmtree(self.tmpdirname					)

	def    __snake_case   (     self    :						List[str]					)	->							List[str]:
								_UpperCamelCase       :    Union[str, Any]					  =					[np.random.randint(2_5_5    ,     size=(3, 3_0, 4_0_0)    ,     dtype=np.uinta					)]

								_UpperCamelCase       :    Tuple					  =					[Image.fromarray(np.moveaxis(lowerCamelCase    ,     0    ,     -1					)					) for x in image_inputs]

								return image_inputs

	def    __snake_case   (     self    :						Union[str, Any]					)	->							Tuple:
								_UpperCamelCase       :    List[str]					  =					self.get_tokenizer()
								_UpperCamelCase       :    List[str]					  =					self.get_rust_tokenizer()
								_UpperCamelCase       :    Tuple					  =					self.get_image_processor()

								_UpperCamelCase       :    List[Any]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)
								processor_slow.save_pretrained(self.tmpdirname					)
								_UpperCamelCase       :    int					  =					FlavaProcessor.from_pretrained(self.tmpdirname    ,     use_fast=lowerCamelCase					)

								_UpperCamelCase       :    Optional[Any]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)
								processor_fast.save_pretrained(self.tmpdirname					)
								_UpperCamelCase       :    Optional[int]					  =					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    __snake_case   (     self    :						Any					)	->							Union[str, Any]:
								_UpperCamelCase       :    Optional[Any]					  =					FlavaProcessor(tokenizer=self.get_tokenizer()    ,     image_processor=self.get_image_processor()					)
								processor.save_pretrained(self.tmpdirname					)

								_UpperCamelCase       :    Optional[Any]					  =					self.get_tokenizer(bos_token='(BOS)'    ,     eos_token='(EOS)'					)
								_UpperCamelCase       :    Dict					  =					self.get_image_processor(do_normalize=lowerCamelCase    ,     padding_value=1.0					)

								_UpperCamelCase       :    Dict					  =					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    __snake_case   (     self    :						Optional[Any]					)	->							int:
								_UpperCamelCase       :    Optional[int]					  =					self.get_image_processor()
								_UpperCamelCase       :    Union[str, Any]					  =					self.get_tokenizer()

								_UpperCamelCase       :    Optional[int]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

								_UpperCamelCase       :    List[Any]					  =					self.prepare_image_inputs()

								_UpperCamelCase       :    Any					  =					image_processor(lowerCamelCase    ,     return_tensors='np'					)
								_UpperCamelCase       :    str					  =					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_2_3_4					)
								_UpperCamelCase       :    Optional[Any]					  =					image_processor(
								    lowerCamelCase    ,     return_image_mask=lowerCamelCase    ,     return_codebook_pixels=lowerCamelCase    ,     return_tensors='np'					)
								random.seed(1_2_3_4					)
								_UpperCamelCase       :    str					  =					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    __snake_case   (     self    :						List[Any]					)	->							Union[str, Any]:
								_UpperCamelCase       :    Optional[int]					  =					self.get_image_processor()
								_UpperCamelCase       :    Tuple					  =					self.get_tokenizer()

								_UpperCamelCase       :    Dict					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

								_UpperCamelCase       :    Any					  =					'lower newer'

								_UpperCamelCase       :    Union[str, Any]					  =					processor(text=lowerCamelCase					)

								_UpperCamelCase       :    Tuple					  =					tokenizer(lowerCamelCase					)

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

	def    __snake_case   (     self    :						Dict					)	->							Tuple:
								_UpperCamelCase       :    Dict					  =					self.get_image_processor()
								_UpperCamelCase       :    Union[str, Any]					  =					self.get_tokenizer()

								_UpperCamelCase       :    List[str]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

								_UpperCamelCase       :    List[Any]					  =					'lower newer'
								_UpperCamelCase       :    Dict					  =					self.prepare_image_inputs()

								_UpperCamelCase       :    str					  =					processor(text=lowerCamelCase    ,     images=lowerCamelCase					)

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

								# add extra args
								_UpperCamelCase       :    Dict					  =					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    __snake_case   (     self    :						Dict					)	->							Any:
								_UpperCamelCase       :    Optional[int]					  =					self.get_image_processor()
								_UpperCamelCase       :    Any					  =					self.get_tokenizer()

								_UpperCamelCase       :    str					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

								_UpperCamelCase       :    Union[str, Any]					  =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								_UpperCamelCase       :    Union[str, Any]					  =					processor.batch_decode(lowerCamelCase					)
								_UpperCamelCase       :    List[str]					  =					tokenizer.batch_decode(lowerCamelCase					)

								self.assertListEqual(lowerCamelCase    ,     lowerCamelCase					)

	def    __snake_case   (     self    :						Union[str, Any]					)	->							str:
								_UpperCamelCase       :    Dict					  =					self.get_image_processor()
								_UpperCamelCase       :    Dict					  =					self.get_tokenizer()

								_UpperCamelCase       :    str					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

								_UpperCamelCase       :    List[str]					  =					'lower newer'
								_UpperCamelCase       :    int					  =					self.prepare_image_inputs()

								_UpperCamelCase       :    Union[str, Any]					  =					processor(text=lowerCamelCase    ,     images=lowerCamelCase					)

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



