




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


__lowerCAmelCase	:       Optional[int]     =	logging.get_logger(__name__)

__lowerCAmelCase	:       Tuple     =	{
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}




class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      """dinat"""

				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}





				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  int=4      ,   __lowerCamelCase       :  int=3      ,   __lowerCamelCase       :  Optional[int]=64      ,   __lowerCamelCase       :  Any=[3, 4, 6, 5]      ,   __lowerCamelCase       :  int=[2, 4, 8, 16]      ,   __lowerCamelCase       :  Optional[int]=7      ,   __lowerCamelCase       :  Any=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]      ,   __lowerCamelCase       :  Dict=3.0      ,   __lowerCamelCase       :  Any=True      ,   __lowerCamelCase       :  Tuple=0.0      ,   __lowerCamelCase       :  Dict=0.0      ,   __lowerCamelCase       :  int=0.1      ,   __lowerCamelCase       :  Tuple="gelu"      ,   __lowerCamelCase       :  Optional[int]=0.02      ,   __lowerCamelCase       :  Union[str, Any]=1e-5      ,   __lowerCamelCase       :  Any=0.0      ,   __lowerCamelCase       :  List[str]=None      ,   __lowerCamelCase       :  Any=None      ,   **__lowerCamelCase       :  List[Any]      ,   )      ->  Union[str, Any]:
							super().__init__(**__lowerCamelCase							)

							a					=    patch_size
							a					=    num_channels
							a					=    embed_dim
							a					=    depths
							a					=    len(__lowerCamelCase							)
							a					=    num_heads
							a					=    kernel_size
							a					=    dilations
							a					=    mlp_ratio
							a					=    qkv_bias
							a					=    hidden_dropout_prob
							a					=    attention_probs_dropout_prob
							a					=    drop_path_rate
							a					=    hidden_act
							a					=    layer_norm_eps
							a					=    initializer_range
							# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							a					=    int(embed_dim * 2 ** (len(__lowerCamelCase							) - 1)							)
							a					=    layer_scale_init_value
							a					=    ["stem"] + [f"""stage{idx}""" for idx in range(1      ,   len(__lowerCamelCase							) + 1							)]
							a					,	a					=    get_aligned_output_features_output_indices(
							    out_features=__lowerCamelCase      ,   out_indices=__lowerCamelCase      ,   stage_names=self.stage_names							)












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 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  int							)      ->  Dict:
							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": 2_24, "width": 2_24},
							    "do_center_crop": True,
							    "crop_size": {"height": 18, "width": 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],
							    "do_convert_rgb": True,
							}
							a					=    os.path.join(self.tmpdirname      ,   __lowerCamelCase							)
							with open(self.image_processor_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										json.dump(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   **__lowerCamelCase       :  Union[str, Any]							)      ->  List[Any]:
							return BertTokenizer.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str      ,   **__lowerCamelCase       :  Optional[int]							)      ->  str:
							return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   **__lowerCamelCase       :  Optional[int]							)      ->  Tuple:
							return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





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





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							a					=    [np.random.randint(2_55      ,   size=(3, 30, 4_00)      ,   dtype=np.uinta							)]

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

							return image_inputs





				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[str]:
							a					=    self.get_tokenizer()
							a					=    self.get_rust_tokenizer()
							a					=    self.get_image_processor()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							processor_slow.save_pretrained(self.tmpdirname							)
							a					=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							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      ,   __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 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[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=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor.from_pretrained(
							    self.tmpdirname      ,   cls_token="(CLS)"      ,   sep_token="(SEP)"      ,   do_normalize=__lowerCamelCase							)

							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 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    self.prepare_image_inputs()

							a					=    image_processor(__lowerCamelCase      ,   return_tensors="np"							)
							a					=    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							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[int]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    "Alexandra，T-shirt的价格是15便士。"

							a					=    processor(text=__lowerCamelCase							)

							a					=    tokenizer(__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    "Alexandra，T-shirt的价格是15便士。"
							a					=    self.prepare_image_inputs()

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

							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(__lowerCamelCase							):
										processor()





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

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor.batch_decode(__lowerCamelCase							)
							a					=    tokenizer.batch_decode(__lowerCamelCase							)

							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  List[str]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    "Alexandra，T-shirt的价格是15便士。"
							a					=    self.prepare_image_inputs()

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

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






