


'''simple docstring'''





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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[int]:
						__A=	tempfile.mkdtemp()

						__A=	[
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    '的',
						    '价',
						    '格',
						    '是',
						    '15',
						    '便',
						    'alex',
						    '##andra',
						    '，',
						    '。',
						    '-',
						    't',
						    'shirt',
						]
						__A=	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=	{
						    'do_resize': True,
						    'size': {'height': 224, 'width': 224},
						    'do_center_crop': True,
						    'crop_size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
						    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
						    'do_convert_rgb': True,
						}
						__A=	os.path.join(self.tmpdirname			,    _UpperCamelCase	)
						with open(self.image_processor_file			,    'w'			,    encoding='utf-8'	) as fp:
									json.dump(_UpperCamelCase			,    _UpperCamelCase	)
			def 					lowerCAmelCase     (  self							:			Dict			,    **lowerCAmelCase_							:			str	)      ->		Optional[Any]:
						return BertTokenizer.from_pretrained(self.tmpdirname			,    **_UpperCamelCase	)
			def 					lowerCAmelCase     (  self							:			str			,    **lowerCAmelCase_							:			str	)      ->		List[Any]:
						return BertTokenizerFast.from_pretrained(self.tmpdirname			,    **_UpperCamelCase	)
			def 					lowerCAmelCase     (  self							:			List[str]			,    **lowerCAmelCase_							:			Optional[int]	)      ->		List[Any]:
						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname			,    **_UpperCamelCase	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		str:
						shutil.rmtree(self.tmpdirname	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Any:
						__A=	[np.random.randint(255			,    size=(3, 30, 400)			,    dtype=np.uinta	)]

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

						return image_inputs
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						__A=	self.get_tokenizer()
						__A=	self.get_rust_tokenizer()
						__A=	self.get_image_processor()

						__A=	ChineseCLIPProcessor(tokenizer=_UpperCamelCase			,    image_processor=_UpperCamelCase	)
						processor_slow.save_pretrained(self.tmpdirname	)
						__A=	ChineseCLIPProcessor.from_pretrained(self.tmpdirname			,    use_fast=_UpperCamelCase	)

						__A=	ChineseCLIPProcessor(tokenizer=_UpperCamelCase			,    image_processor=_UpperCamelCase	)
						processor_fast.save_pretrained(self.tmpdirname	)
						__A=	ChineseCLIPProcessor.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 					lowerCAmelCase     (  self							:			str	)      ->		Any:
						__A=	ChineseCLIPProcessor(tokenizer=self.get_tokenizer()			,    image_processor=self.get_image_processor()	)
						processor.save_pretrained(self.tmpdirname	)

						__A=	self.get_tokenizer(cls_token='(CLS)'			,    sep_token='(SEP)'	)
						__A=	self.get_image_processor(do_normalize=_UpperCamelCase	)

						__A=	ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname			,    cls_token='(CLS)'			,    sep_token='(SEP)'			,    do_normalize=_UpperCamelCase	)

						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 					lowerCAmelCase     (  self							:			str	)      ->		Dict:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	ChineseCLIPProcessor(tokenizer=_UpperCamelCase			,    image_processor=_UpperCamelCase	)

						__A=	self.prepare_image_inputs()

						__A=	image_processor(_UpperCamelCase			,    return_tensors='np'	)
						__A=	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	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[str]:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	ChineseCLIPProcessor(tokenizer=_UpperCamelCase			,    image_processor=_UpperCamelCase	)

						__A=	'Alexandra，T-shirt的价格是15便士。'

						__A=	processor(text=_UpperCamelCase	)

						__A=	tokenizer(_UpperCamelCase	)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]			,    encoded_processor[key]	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	ChineseCLIPProcessor(tokenizer=_UpperCamelCase			,    image_processor=_UpperCamelCase	)

						__A=	'Alexandra，T-shirt的价格是15便士。'
						__A=	self.prepare_image_inputs()

						__A=	processor(text=_UpperCamelCase			,    images=_UpperCamelCase	)

						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(_UpperCamelCase	):
									processor()
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[Any]:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	ChineseCLIPProcessor(tokenizer=_UpperCamelCase			,    image_processor=_UpperCamelCase	)

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

						__A=	processor.batch_decode(_UpperCamelCase	)
						__A=	tokenizer.batch_decode(_UpperCamelCase	)

						self.assertListEqual(_UpperCamelCase			,    _UpperCamelCase	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[int]:
						__A=	self.get_image_processor()
						__A=	self.get_tokenizer()

						__A=	ChineseCLIPProcessor(tokenizer=_UpperCamelCase			,    image_processor=_UpperCamelCase	)

						__A=	'Alexandra，T-shirt的价格是15便士。'
						__A=	self.prepare_image_inputs()

						__A=	processor(text=_UpperCamelCase			,    images=_UpperCamelCase	)

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, TensorType

_A  =	logging.get_logger(__name__)

_A  =	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''imagegpt'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(   self							,							_UpperCamelCase=512 + 1							,							_UpperCamelCase=32 * 32							,							_UpperCamelCase=512							,							_UpperCamelCase=24							,							_UpperCamelCase=8							,							_UpperCamelCase=None							,							_UpperCamelCase="quick_gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> Tuple:
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      n_positions
							lowerCAmelCase_         =      n_embd
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      n_inner
							lowerCAmelCase_         =      activation_function
							lowerCAmelCase_         =      resid_pdrop
							lowerCAmelCase_         =      embd_pdrop
							lowerCAmelCase_         =      attn_pdrop
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scale_attn_weights
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      scale_attn_by_inverse_layer_idx
							lowerCAmelCase_         =      reorder_and_upcast_attn
							lowerCAmelCase_         =      tie_word_embeddings

							super().__init__(tie_word_embeddings=_UpperCamelCase							,							**_UpperCamelCase	)





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "sequence"}),
							    ]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = 1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = 3							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      self._generate_dummy_images(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      dict(preprocessor(images=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)	)

							return inputs



