


"""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 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Dict       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    tempfile.mkdtemp()

						__SCREAMING_SNAKE_CASE						       =    [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "的",
						    "价",
						    "格",
						    "是",
						    "15",
						    "便",
						    "alex",
						    "##andra",
						    "，",
						    "。",
						    "-",
						    "t",
						    "shirt",
						]
						__SCREAMING_SNAKE_CASE						       =    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]       )       )

						__SCREAMING_SNAKE_CASE						       =    {
						    "do_resize": True,
						    "size": {"height": 2_2_4, "width": 2_2_4},
						    "do_center_crop": True,
						    "crop_size": {"height": 1_8, "width": 1_8},
						    "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,
						}
						__SCREAMING_SNAKE_CASE						       =    os.path.join(self.tmpdirname      ,      UpperCAmelCase__       )
						with open(self.image_processor_file      ,      "w"      ,      encoding="utf-8"       ) as fp:
											json.dump(UpperCAmelCase__      ,      UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      **UpperCAmelCase__      :    Any       )	->     Optional[Any]:
						return BertTokenizer.from_pretrained(self.tmpdirname      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      **UpperCAmelCase__      :    Dict       )	->     Optional[Any]:
						return BertTokenizerFast.from_pretrained(self.tmpdirname      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      **UpperCAmelCase__      :    Dict       )	->     List[str]:
						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str       )	->     Union[str, Any]:
						shutil.rmtree(self.tmpdirname       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    [np.random.randint(2_5_5      ,      size=(3, 3_0, 4_0_0)      ,      dtype=np.uinta       )]

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

						return image_inputs




	def   UpperCAmelCase_				(			self      :    Dict       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()
						__SCREAMING_SNAKE_CASE						       =    self.get_rust_tokenizer()
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()

						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,      use_fast=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    List[str]       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer(cls_token="(CLS)"      ,      sep_token="(SEP)"       )
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor(do_normalize=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    self.prepare_image_inputs()

						__SCREAMING_SNAKE_CASE						       =    image_processor(UpperCAmelCase__      ,      return_tensors="np"       )
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    str       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

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

						__SCREAMING_SNAKE_CASE						       =    processor(text=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    tokenizer(UpperCAmelCase__       )

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




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

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

						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    List[str]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

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

						__SCREAMING_SNAKE_CASE						       =    processor.batch_decode(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    tokenizer.batch_decode(UpperCAmelCase__       )

						self.assertListEqual(UpperCAmelCase__      ,      UpperCAmelCase__       )




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

						__SCREAMING_SNAKE_CASE						       =    ChineseCLIPProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

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

						__SCREAMING_SNAKE_CASE						       =    processor(text=UpperCAmelCase__      ,      images=UpperCAmelCase__       )

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




"""simple docstring"""







from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__    :							Tuple            =					logging.get_logger(__name__)

a__    :							List[Any]            =					{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Any    =      "efficientformer"




	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    List[int] = [3, 2, 6, 4]      ,      UpperCAmelCase__      :    List[int] = [4_8, 9_6, 2_2_4, 4_4_8]      ,      UpperCAmelCase__      :    List[bool] = [True, True, True, True]      ,      UpperCAmelCase__      :    int = 4_4_8      ,      UpperCAmelCase__      :    int = 3_2      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    int = 7      ,      UpperCAmelCase__      :    int = 5      ,      UpperCAmelCase__      :    int = 8      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1_6      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 2      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    float = 1E-5      ,      UpperCAmelCase__      :    str = "gelu"      ,      UpperCAmelCase__      :    float = 0.02      ,      UpperCAmelCase__      :    float = 1E-12      ,      UpperCAmelCase__      :    int = 2_2_4      ,      UpperCAmelCase__      :    float = 1E-05      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    hidden_sizes
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    layer_norm_eps
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    depths
						__SCREAMING_SNAKE_CASE						       =    mlp_expansion_ratio
						__SCREAMING_SNAKE_CASE						       =    downsamples
						__SCREAMING_SNAKE_CASE						       =    dim
						__SCREAMING_SNAKE_CASE						       =    key_dim
						__SCREAMING_SNAKE_CASE						       =    attention_ratio
						__SCREAMING_SNAKE_CASE						       =    resolution
						__SCREAMING_SNAKE_CASE						       =    pool_size
						__SCREAMING_SNAKE_CASE						       =    downsample_patch_size
						__SCREAMING_SNAKE_CASE						       =    downsample_stride
						__SCREAMING_SNAKE_CASE						       =    downsample_pad
						__SCREAMING_SNAKE_CASE						       =    drop_path_rate
						__SCREAMING_SNAKE_CASE						       =    num_metaad_blocks
						__SCREAMING_SNAKE_CASE						       =    distillation
						__SCREAMING_SNAKE_CASE						       =    use_layer_scale
						__SCREAMING_SNAKE_CASE						       =    layer_scale_init_value
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    batch_norm_eps
