'''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    UpperCAmelCase__	(							unittest.TestCase      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							Optional[int]  					=       tempfile.mkdtemp()

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

        lowercase_       :							Optional[Any]  					=       {
            '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,
        }

        lowercase_       :							Tuple  					=       os.path.join(self.tmpdirname							,					_a					)
        with open(self.image_processor_file							,					'w'							,					encoding='utf-8'					) as fp:
             json.dump(_a							,					_a					)
   def 		_lowerCamelCase			(self							,					**_a					)     ->  int:
        return BertTokenizer.from_pretrained(self.tmpdirname							,					**_a					)
   def 		_lowerCamelCase			(self							,					**_a					)     ->  Optional[Any]:
        return BertTokenizerFast.from_pretrained(self.tmpdirname							,					**_a					)
   def 		_lowerCamelCase			(self							,					**_a					)     ->  Tuple:
        return FlavaImageProcessor.from_pretrained(self.tmpdirname							,					**_a					)
   def 		_lowerCamelCase			(self					)     ->  Tuple:
        shutil.rmtree(self.tmpdirname					)
   def 		_lowerCamelCase			(self					)     ->  List[str]:
        lowercase_       :							Any  					=       [np.random.randint(255							,					size=(3, 30, 400)							,					dtype=np.uinta					)]

        lowercase_       :							List[str]  					=       [Image.fromarray(np.moveaxis(_a							,					0							,					-1					)					) for x in image_inputs]

        return image_inputs
   def 		_lowerCamelCase			(self					)     ->  List[str]:
        lowercase_       :							List[Any]  					=       self.get_tokenizer()
        lowercase_       :							Tuple  					=       self.get_rust_tokenizer()
        lowercase_       :							Tuple  					=       self.get_image_processor()

        lowercase_       :							str  					=       FlavaProcessor(tokenizer=_a							,					image_processor=_a					)
        processor_slow.save_pretrained(self.tmpdirname					)
        lowercase_       :							Tuple  					=       FlavaProcessor.from_pretrained(self.tmpdirname							,					use_fast=_a					)

        lowercase_       :							Any  					=       FlavaProcessor(tokenizer=_a							,					image_processor=_a					)
        processor_fast.save_pretrained(self.tmpdirname					)
        lowercase_       :							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							,					_a					)
        self.assertIsInstance(processor_fast.tokenizer							,					_a					)

        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							,					_a					)
        self.assertIsInstance(processor_fast.image_processor							,					_a					)
   def 		_lowerCamelCase			(self					)     ->  Tuple:
        lowercase_       :							Tuple  					=       FlavaProcessor(tokenizer=self.get_tokenizer()							,					image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

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

        lowercase_       :							Any  					=       FlavaProcessor.from_pretrained(
            self.tmpdirname							,					bos_token='(BOS)'							,					eos_token='(EOS)'							,					do_normalize=_a							,					padding_value=1.0					)

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

        self.assertEqual(processor.image_processor.to_json_string()							,					image_processor_add_kwargs.to_json_string()					)
        self.assertIsInstance(processor.image_processor							,					_a					)
   def 		_lowerCamelCase			(self					)     ->  int:
        lowercase_       :							List[Any]  					=       self.get_image_processor()
        lowercase_       :							Optional[int]  					=       self.get_tokenizer()

        lowercase_       :							int  					=       FlavaProcessor(tokenizer=_a							,					image_processor=_a					)

        lowercase_       :							Optional[int]  					=       self.prepare_image_inputs()

        lowercase_       :							Tuple  					=       image_processor(_a							,					return_tensors='np'					)
        lowercase_       :							str  					=       processor(images=_a							,					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					)
        lowercase_       :							Dict  					=       image_processor(
            _a							,					return_image_mask=_a							,					return_codebook_pixels=_a							,					return_tensors='np'					)
        random.seed(1_234					)
        lowercase_       :							List[str]  					=       processor(
            images=_a							,					return_image_mask=_a							,					return_codebook_pixels=_a							,					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 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							Optional[int]  					=       self.get_image_processor()
        lowercase_       :							str  					=       self.get_tokenizer()

        lowercase_       :							Tuple  					=       FlavaProcessor(tokenizer=_a							,					image_processor=_a					)

        lowercase_       :							List[str]  					=       'lower newer'

        lowercase_       :							Optional[Any]  					=       processor(text=_a					)

        lowercase_       :							Dict  					=       tokenizer(_a					)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]					)
   def 		_lowerCamelCase			(self					)     ->  int:
        lowercase_       :							Optional[int]  					=       self.get_image_processor()
        lowercase_       :							Any  					=       self.get_tokenizer()

        lowercase_       :							List[Any]  					=       FlavaProcessor(tokenizer=_a							,					image_processor=_a					)

        lowercase_       :							int  					=       'lower newer'
        lowercase_       :							Union[str, Any]  					=       self.prepare_image_inputs()

        lowercase_       :							List[Any]  					=       processor(text=_a							,					images=_a					)

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

        # add extra args
        lowercase_       :							Optional[int]  					=       processor(text=_a							,					images=_a							,					return_codebook_pixels=_a							,					return_image_mask=_a					)

        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(_a					):
             processor()
   def 		_lowerCamelCase			(self					)     ->  int:
        lowercase_       :							List[Any]  					=       self.get_image_processor()
        lowercase_       :							Optional[Any]  					=       self.get_tokenizer()

        lowercase_       :							Optional[int]  					=       FlavaProcessor(tokenizer=_a							,					image_processor=_a					)

        lowercase_       :							Optional[int]  					=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_       :							Union[str, Any]  					=       processor.batch_decode(_a					)
        lowercase_       :							Tuple  					=       tokenizer.batch_decode(_a					)

        self.assertListEqual(_a							,					_a					)





   def 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_       :							List[str]  					=       self.get_image_processor()
        lowercase_       :							List[Any]  					=       self.get_tokenizer()

        lowercase_       :							Any  					=       FlavaProcessor(tokenizer=_a							,					image_processor=_a					)

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

        lowercase_       :							List[Any]  					=       processor(text=_a							,					images=_a					)

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





'''simple docstring'''




from typing import TYPE_CHECKING

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


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

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




