


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor


@require_vision
class 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									a__ :List[Any]						  =    tempfile.mkdtemp()

									# fmt: off
									a__ :List[str]						  =    ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
									# fmt: on
									a__ :List[Any]						  =    dict(zip(__A   ,     range(len(__A						)						)						)						)
									a__ :Union[str, Any]						  =    ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
									a__ :Union[str, Any]						  =    {"unk_token": "<unk>"}

									a__ :List[Any]						  =    os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["vocab_file"]						)
									a__ :List[str]						  =    os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["merges_file"]						)
									with open(self.vocab_file   ,     "w"   ,     encoding="utf-8"						) as fp:
												fp.write(json.dumps(__A						) + "\n"						)
									with open(self.merges_file   ,     "w"   ,     encoding="utf-8"						) as fp:
												fp.write("\n".join(__A						)						)

									a__ :Tuple						  =    {
									    "do_resize": True,
									    "size": 20,
									    "do_center_crop": True,
									    "crop_size": 18,
									    "do_normalize": True,
									    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
									    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
									}
									a__ :Any						  =    os.path.join(self.tmpdirname   ,     __A						)
									with open(self.image_processor_file   ,     "w"   ,     encoding="utf-8"						) as fp:
												json.dump(__A   ,     __A						)




						def  _snake_case					(	self						:   str   ,     **__A						:   str						)      ->Optional[int]:






									"""simple docstring"""






									return CLIPTokenizer.from_pretrained(self.tmpdirname   ,     **__A						)




						def  _snake_case					(	self						:   Any   ,     **__A						:   int						)      ->Optional[int]:






									"""simple docstring"""






									return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,     **__A						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     **__A						:   Optional[Any]						)      ->List[str]:






									"""simple docstring"""






									return ViTImageProcessor.from_pretrained(self.tmpdirname   ,     **__A						)




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Tuple:






									"""simple docstring"""






									shutil.rmtree(self.tmpdirname						)




						def  _snake_case					(	self						:   Any						)      ->List[Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    [np.random.randint(255   ,     size=(3, 30, 400)   ,     dtype=np.uinta						)]

									a__ :Dict						  =    [Image.fromarray(np.moveaxis(__A   ,     0   ,     -1						)						) for x in image_inputs]

									return image_inputs




						def  _snake_case					(	self						:   Any						)      ->int:






									"""simple docstring"""






									a__ :Optional[Any]						  =    self.get_tokenizer()
									a__ :Any						  =    self.get_rust_tokenizer()
									a__ :List[str]						  =    self.get_image_processor()

									a__ :Dict						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)
									processor_slow.save_pretrained(self.tmpdirname						)
									a__ :Optional[int]						  =    CLIPSegProcessor.from_pretrained(self.tmpdirname   ,     use_fast=__A						)

									a__ :str						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)
									processor_fast.save_pretrained(self.tmpdirname						)
									a__ :str						  =    CLIPSegProcessor.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  _snake_case					(	self						:   Tuple						)      ->Any:






									"""simple docstring"""






									a__ :int						  =    CLIPSegProcessor(tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									a__ :str						  =    self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"						)
									a__ :Tuple						  =    self.get_image_processor(do_normalize=__A   ,     padding_value=1.0						)

									a__ :int						  =    CLIPSegProcessor.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  _snake_case					(	self						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									a__ :Dict						  =    self.get_image_processor()
									a__ :Optional[Any]						  =    self.get_tokenizer()

									a__ :str						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :str						  =    self.prepare_image_inputs()

									a__ :int						  =    image_processor(__A   ,     return_tensors="np"						)
									a__ :int						  =    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						)




						def  _snake_case					(	self						:   int						)      ->str:






									"""simple docstring"""






									a__ :List[str]						  =    self.get_image_processor()
									a__ :Tuple						  =    self.get_tokenizer()

									a__ :str						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :str						  =    "lower newer"

									a__ :List[str]						  =    processor(text=__A						)

									a__ :Tuple						  =    tokenizer(__A						)

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




						def  _snake_case					(	self						:   str						)      ->str:






									"""simple docstring"""






									a__ :str						  =    self.get_image_processor()
									a__ :Tuple						  =    self.get_tokenizer()

									a__ :Optional[int]						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :Union[str, Any]						  =    "lower newer"
									a__ :Optional[Any]						  =    self.prepare_image_inputs()

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

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

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




						def  _snake_case					(	self						:   Any						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :List[Any]						  =    self.get_image_processor()
									a__ :List[Any]						  =    self.get_tokenizer()

									a__ :int						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :List[str]						  =    self.prepare_image_inputs()
									a__ :List[str]						  =    self.prepare_image_inputs()

									a__ :Union[str, Any]						  =    processor(images=__A   ,     visual_prompt=__A						)

									self.assertListEqual(list(inputs.keys()						)   ,     ["pixel_values", "conditional_pixel_values"]						)

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






						def  _snake_case					(	self						:   Union[str, Any]						)      ->Tuple:






									"""simple docstring"""






									a__ :Tuple						  =    self.get_image_processor()
									a__ :Optional[Any]						  =    self.get_tokenizer()

									a__ :List[str]						  =    CLIPSegProcessor(tokenizer=__A   ,     image_processor=__A						)

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

									a__ :Dict						  =    processor.batch_decode(__A						)
									a__ :Dict						  =    tokenizer.batch_decode(__A						)

									self.assertListEqual(__A   ,     __A						)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__			 =						{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'albert'




						def __init__(	self						:   Any   ,     __A						:   Optional[int]=30000   ,     __A						:   List[str]=128   ,     __A						:   int=4096   ,     __A						:   Any=12   ,     __A						:   Union[str, Any]=1   ,     __A						:   Optional[int]=64   ,     __A						:   Dict=16384   ,     __A						:   List[str]=1   ,     __A						:   Any="gelu_new"   ,     __A						:   List[Any]=0   ,     __A						:   str=0   ,     __A						:   List[str]=512   ,     __A						:   Optional[Any]=2   ,     __A						:   Tuple=0.02   ,     __A						:   int=1E-12   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]="absolute"   ,     __A						:   Tuple=0   ,     __A						:   str=2   ,     __A						:   Union[str, Any]=3   ,     **__A						:   Union[str, Any]   ,     )      ->str:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    embedding_size
									a__ :Union[str, Any]						  =    hidden_size
									a__ :int						  =    num_hidden_layers
									a__ :List[Any]						  =    num_hidden_groups
									a__ :str						  =    num_attention_heads
									a__ :Optional[Any]						  =    inner_group_num
									a__ :Any						  =    hidden_act
									a__ :Optional[int]						  =    intermediate_size
									a__ :Optional[Any]						  =    hidden_dropout_prob
									a__ :Optional[int]						  =    attention_probs_dropout_prob
									a__ :int						  =    max_position_embeddings
									a__ :List[Any]						  =    type_vocab_size
									a__ :Dict						  =    initializer_range
									a__ :Optional[int]						  =    layer_norm_eps
									a__ :Optional[Any]						  =    classifier_dropout_prob
									a__ :Optional[Any]						  =    position_embedding_type






class 					lowerCAmelCase_ (       _a):




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Mapping[str, Mapping[int, str]]:






									"""simple docstring"""






									if self.task == "multiple-choice":
												a__ :List[Any]						  =    {0: "batch", 1: "choice", 2: "sequence"}
									else:
												a__ :Dict						  =    {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]						)


