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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE__		:       List[str]       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       List[Any]       						=       {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_    ):
  lowercase_								=  'swin'

  lowercase_								=  {
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }




  def __init__(   self  :						str  ,			a_  :						Dict=224  ,			a_  :						Optional[int]=4  ,			a_  :						Optional[Any]=3  ,			a_  :						Optional[int]=96  ,			a_  :						List[str]=[2, 2, 6, 2]  ,			a_  :						Union[str, Any]=[3, 6, 12, 24]  ,			a_  :						int=7  ,			a_  :						List[Any]=4.0  ,			a_  :						Any=True  ,			a_  :						str=0.0  ,			a_  :						str=0.0  ,			a_  :						List[str]=0.1  ,			a_  :						Union[str, Any]="gelu"  ,			a_  :						List[str]=False  ,			a_  :						List[str]=0.02  ,			a_  :						Union[str, Any]=1e-5  ,			a_  :						int=32  ,			a_  :						Optional[Any]=None  ,			a_  :						List[Any]=None  ,			**a_  :						Dict  ,			)->							Any:




       """simple docstring"""
       super().__init__(**a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		image_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		patch_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		num_channels
       SCREAMING_SNAKE_CASE__					:					Dict      =		embed_dim
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		depths
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		len(a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		num_heads
       SCREAMING_SNAKE_CASE__					:					Any      =		window_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		mlp_ratio
       SCREAMING_SNAKE_CASE__					:					Any      =		qkv_bias
       SCREAMING_SNAKE_CASE__					:					Tuple      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					int      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Dict      =		drop_path_rate
       SCREAMING_SNAKE_CASE__					:					Dict      =		hidden_act
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		use_absolute_embeddings
       SCREAMING_SNAKE_CASE__					:					Dict      =		layer_norm_eps
       SCREAMING_SNAKE_CASE__					:					Tuple      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					List[str]      =		encoder_stride
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		int(embed_dim * 2 ** (len(a_					) - 1)					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		['stem'] + [F'''stage{idx}''' for idx in range(1  ,			len(a_					) + 1					)]
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		get_aligned_output_features_output_indices(
           out_features=a_  ,			out_indices=a_  ,			stage_names=self.stage_names					)




class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  version.parse('1.11'    )




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




       """simple docstring"""
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]					)




  @property
  def 						__lowercase(   self  :						List[str]					)->							float:




       """simple docstring"""
       return 1e-4






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







							def 							__lowercase							(     self			)       ->       int:
												_a      :       Dict   =						'''ZinengTang/tvlt-base'''
												_a      :       List[str]   =						tempfile.mkdtemp()


							def 							__lowercase							(     self			,				**_a			)       ->       int:
												return TvltImageProcessor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			,				**_a			)       ->       List[Any]:
												return TvltFeatureExtractor.from_pretrained(self.checkpoint			,				**_a			)


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


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Optional[int]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)
												processor.save_pretrained(self.tmpdirname			)
												_a      :       Any   =						TvltProcessor.from_pretrained(self.tmpdirname			)

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


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       Union[str, Any]   =						np.ones([1_2_0_0_0]			)

												_a      :       Dict   =						feature_extractor(_a			,				return_tensors='''np'''			)
												_a      :       Tuple   =						processor(audio=_a			,				return_tensors='''np'''			)

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


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Optional[Any]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[Any]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						image_processor(_a			,				return_tensors='''np'''			)
												_a      :       Optional[int]   =						processor(images=_a			,				return_tensors='''np'''			)

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


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Any   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[str]   =						np.ones([1_2_0_0_0]			)
												_a      :       Optional[int]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						processor(audio=_a			,				images=_a			)

												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(_a			):
																	processor()




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

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												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'''			,				)



