

import json
import os
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 AlignProcessor, EfficientNetImageProcessor

@require_vision
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Tuple         =		tempfile.mkdtemp()

												_a	:    Optional[Any]         =		[
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''[PAD]''',
												    '''[MASK]''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												_a	:    Dict         =		os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''vocab_file''']       )
												with open(self.vocab_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

												_a	:    Tuple         =		{
												    '''do_resize''': True,
												    '''size''': 2_0,
												    '''do_center_crop''': True,
												    '''crop_size''': 1_8,
												    '''do_normalize''': True,
												    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
												    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
												}
												_a	:    List[Any]         =		os.path.join(self.tmpdirname       ,							_a       )
												with open(self.image_processor_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
																	json.dump(_a       ,							_a       )


							def 				__lowercase				(			self       ,							**_a       )    ->    Optional[int]:
												return BertTokenizer.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							**_a       )    ->    Union[str, Any]:
												return BertTokenizerFast.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							**_a       )    ->    Optional[Any]:
												return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												shutil.rmtree(self.tmpdirname       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Dict         =		[np.random.randint(2_5_5       ,							size=(3, 3_0, 4_0_0)       ,							dtype=np.uinta       )]
												_a	:    Union[str, Any]         =		[Image.fromarray(np.moveaxis(_a       ,							0       ,							-1       )       ) for x in image_inputs]
												return image_inputs


							def 				__lowercase				(			self       )    ->    int:
												_a	:    str         =		self.get_tokenizer()
												_a	:    int         =		self.get_rust_tokenizer()
												_a	:    Dict         =		self.get_image_processor()

												_a	:    int         =		AlignProcessor(tokenizer=_a       ,							image_processor=_a       )
												processor_slow.save_pretrained(self.tmpdirname       )
												_a	:    List[Any]         =		AlignProcessor.from_pretrained(self.tmpdirname       ,							use_fast=_a       )

												_a	:    str         =		AlignProcessor(tokenizer=_a       ,							image_processor=_a       )
												processor_fast.save_pretrained(self.tmpdirname       )
												_a	:    List[Any]         =		AlignProcessor.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 				__lowercase				(			self       )    ->    Any:
												_a	:    Dict         =		AlignProcessor(tokenizer=self.get_tokenizer()       ,							image_processor=self.get_image_processor()       )
												processor.save_pretrained(self.tmpdirname       )

												_a	:    int         =		self.get_tokenizer(bos_token='''(BOS)'''       ,							eos_token='''(EOS)'''       )
												_a	:    str         =		self.get_image_processor(do_normalize=_a       ,							padding_value=1.0       )

												_a	:    Tuple         =		AlignProcessor.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 				__lowercase				(			self       )    ->    Any:
												_a	:    Optional[Any]         =		self.get_image_processor()
												_a	:    Optional[Any]         =		self.get_tokenizer()

												_a	:    Tuple         =		AlignProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    Tuple         =		self.prepare_image_inputs()

												_a	:    Any         =		image_processor(_a       ,							return_tensors='''np'''       )
												_a	:    List[str]         =		processor(images=_a       ,							return_tensors='''np'''       )

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


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Any         =		self.get_image_processor()
												_a	:    Dict         =		self.get_tokenizer()

												_a	:    Optional[Any]         =		AlignProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    Dict         =		'''lower newer'''

												_a	:    List[str]         =		processor(text=_a       )

												_a	:    int         =		tokenizer(_a       ,							padding='''max_length'''       ,							max_length=6_4       )

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


							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    Optional[int]         =		self.get_image_processor()
												_a	:    str         =		self.get_tokenizer()

												_a	:    Any         =		AlignProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    Any         =		'''lower newer'''
												_a	:    Tuple         =		self.prepare_image_inputs()

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

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

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


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Optional[int]         =		self.get_image_processor()
												_a	:    Union[str, Any]         =		self.get_tokenizer()

												_a	:    Tuple         =		AlignProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    List[Any]         =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												_a	:    Any         =		processor.batch_decode(_a       )
												_a	:    Optional[int]         =		tokenizer.batch_decode(_a       )

												self.assertListEqual(_a       ,							_a       )




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Optional[int]         =		self.get_image_processor()
												_a	:    Optional[int]         =		self.get_tokenizer()

												_a	:    Optional[Any]         =		AlignProcessor(tokenizer=_a       ,							image_processor=_a       )

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

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

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__				=		logging.get_logger(__name__)

a__				=		{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


							def __init__(			self       ,							_a=3       ,							_a=6_4       ,							_a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,							_a=[3, 4, 6, 3]       ,							_a="preactivation"       ,							_a="relu"       ,							_a=None       ,							_a=3_2       ,							_a=0.0       ,							_a=False       ,							_a=3_2       ,							_a=1       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    Union[str, Any]:
												super().__init__(**_a       )
												if layer_type not in self.layer_types:
																	raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types       )}"""       )
												if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																						_a	:    Any         =		global_padding.upper()
																	else:
																						raise ValueError(F"""Padding strategy {global_padding} not supported"""       )
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		embedding_size
												_a	:    Any         =		hidden_sizes
												_a	:    int         =		depths
												_a	:    Dict         =		layer_type
												_a	:    int         =		hidden_act
												_a	:    Optional[Any]         =		global_padding
												_a	:    Optional[Any]         =		num_groups
												_a	:    Union[str, Any]         =		drop_path_rate
												_a	:    Tuple         =		embedding_dynamic_padding
												_a	:    Union[str, Any]         =		output_stride
												_a	:    Any         =		width_factor

												_a	:    Any         =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       ,							len(_a       ) + 1       )]
												_a      ,					_a	:    List[str]         =		get_aligned_output_features_output_indices(
												    out_features=_a       ,							out_indices=_a       ,							stage_names=self.stage_names       )



