





'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}

class    lowerCAmelCase_		(		_snake_case		):
 __lowerCamelCase						:							Optional[Any]         =					"levit"




 def __init__(	self			,     _lowerCAmelCase=224			,     _lowerCAmelCase=3			,     _lowerCAmelCase=3			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase=16			,     _lowerCAmelCase=[128, 256, 384]			,     _lowerCAmelCase=[4, 8, 12]			,     _lowerCAmelCase=[4, 4, 4]			,     _lowerCAmelCase=[16, 16, 16]			,     _lowerCAmelCase=0			,     _lowerCAmelCase=[2, 2, 2]			,     _lowerCAmelCase=[2, 2, 2]			,     _lowerCAmelCase=0.02			,     **_lowerCAmelCase			,     )    ->							str:
     super().__init__(**lowerCAmelCase__		)
     _lowerCAmelCase				=      image_size
     _lowerCAmelCase				=      num_channels
     _lowerCAmelCase				=      kernel_size
     _lowerCAmelCase				=      stride
     _lowerCAmelCase				=      padding
     _lowerCAmelCase				=      hidden_sizes
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      depths
     _lowerCAmelCase				=      key_dim
     _lowerCAmelCase				=      drop_path_rate
     _lowerCAmelCase				=      patch_size
     _lowerCAmelCase				=      attention_ratio
     _lowerCAmelCase				=      mlp_ratio
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      [
         ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
         ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
     ]







class    lowerCAmelCase_		(		_snake_case		):
 __lowerCamelCase						:							Dict         =					version.parse("1.11"		)




 @property
 def 							_snake_case   (	self		)    ->							Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)





 @property
 def 							_snake_case   (	self		)    ->							float:
     return 1E-4









'''simple docstring'''


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
       from transformers import TvltImageProcessor

if is_speech_available():
       from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      "ZinengTang/tvlt-base"
     _lowerCAmelCase				=      tempfile.mkdtemp()




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Any:
     return TvltImageProcessor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							str:
     return TvltFeatureExtractor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




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




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)
     processor.save_pretrained(self.tmpdirname		)
     _lowerCAmelCase				=      TvltProcessor.from_pretrained(self.tmpdirname		)

     self.assertIsInstance(processor.feature_extractor			,     _lowerCAmelCase		)
     self.assertIsInstance(processor.image_processor			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)

     _lowerCAmelCase				=      feature_extractor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     return_tensors="np"		)

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




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      image_processor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(images=_lowerCAmelCase			,     return_tensors="np"		)

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




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)
     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     images=_lowerCAmelCase		)

     self.assertListEqual(list(inputs.keys()		)			,     ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

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





 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     self.assertListEqual(
         processor.model_input_names			,     image_processor.model_input_names + feature_extractor.model_input_names			,     msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"			,     )


