



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       a							(			unittest.TestCase				):



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      Any     		=	'''ZinengTang/tvlt-base'''
						snake_case__	:      Optional[Any]     		=	tempfile.mkdtemp()



		def 	__lowerCamelCase			(     self     :Dict   ,**__lowercase     :Tuple  ):
						return TvltImageProcessor.from_pretrained(self.checkpoint   ,**__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple   ,**__lowercase     :int  ):
						return TvltFeatureExtractor.from_pretrained(self.checkpoint   ,**__lowercase  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						shutil.rmtree(self.tmpdirname  )



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      List[Any]     		=	self.get_image_processor()
						snake_case__	:      Optional[Any]     		=	self.get_feature_extractor()

						snake_case__	:      List[str]     		=	TvltProcessor(image_processor=__lowercase   ,feature_extractor=__lowercase  )
						processor.save_pretrained(self.tmpdirname  )
						snake_case__	:      str     		=	TvltProcessor.from_pretrained(self.tmpdirname  )

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



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__	:      Optional[int]     		=	self.get_image_processor()
						snake_case__	:      int     		=	self.get_feature_extractor()

						snake_case__	:      Dict     		=	TvltProcessor(image_processor=__lowercase   ,feature_extractor=__lowercase  )

						snake_case__	:      Dict     		=	np.ones([1_2_0_0_0]  )

						snake_case__	:      Optional[int]     		=	feature_extractor(__lowercase   ,return_tensors='''np'''  )
						snake_case__	:      str     		=	processor(audio=__lowercase   ,return_tensors='''np'''  )

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



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Optional[Any]     		=	self.get_image_processor()
						snake_case__	:      str     		=	self.get_feature_extractor()

						snake_case__	:      Optional[int]     		=	TvltProcessor(image_processor=__lowercase   ,feature_extractor=__lowercase  )

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

						snake_case__	:      Optional[Any]     		=	image_processor(__lowercase   ,return_tensors='''np'''  )
						snake_case__	:      List[Any]     		=	processor(images=__lowercase   ,return_tensors='''np'''  )

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



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Tuple     		=	self.get_image_processor()
						snake_case__	:      Any     		=	self.get_feature_extractor()

						snake_case__	:      Dict     		=	TvltProcessor(image_processor=__lowercase   ,feature_extractor=__lowercase  )

						snake_case__	:      Optional[Any]     		=	np.ones([1_2_0_0_0]  )
						snake_case__	:      Optional[int]     		=	np.ones([3, 2_2_4, 2_2_4]  )

						snake_case__	:      Any     		=	processor(audio=__lowercase   ,images=__lowercase  )

						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(__lowercase  ):
										processor()






		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      List[str]     		=	self.get_image_processor()
						snake_case__	:      int     		=	self.get_feature_extractor()

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

						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'''   ,)






from collections import OrderedDict
from typing import Mapping

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


A__								=						logging.get_logger(__name__)

A__								=						{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    str											=					"""distilbert"""
		__lowerCAmelCase	:    str											=					{
		    """hidden_size""": """dim""",
		    """num_attention_heads""": """n_heads""",
		    """num_hidden_layers""": """n_layers""",
		}



		def __init__(     self     :Dict   ,__lowercase     :Tuple=3_0_5_2_2   ,__lowercase     :Optional[Any]=5_1_2   ,__lowercase     :List[str]=False   ,__lowercase     :List[str]=6   ,__lowercase     :Optional[Any]=1_2   ,__lowercase     :Tuple=7_6_8   ,__lowercase     :int=4 * 7_6_8   ,__lowercase     :List[Any]=0.1   ,__lowercase     :List[str]=0.1   ,__lowercase     :Union[str, Any]="gelu"   ,__lowercase     :List[str]=0.02   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :Dict=0.2   ,__lowercase     :Union[str, Any]=0   ,**__lowercase     :Optional[Any]   ,):
						snake_case__	:      List[str]     		=	vocab_size
						snake_case__	:      Tuple     		=	max_position_embeddings
						snake_case__	:      Optional[int]     		=	sinusoidal_pos_embds
						snake_case__	:      str     		=	n_layers
						snake_case__	:      List[Any]     		=	n_heads
						snake_case__	:      Tuple     		=	dim
						snake_case__	:      str     		=	hidden_dim
						snake_case__	:      int     		=	dropout
						snake_case__	:      Dict     		=	attention_dropout
						snake_case__	:      Tuple     		=	activation
						snake_case__	:      int     		=	initializer_range
						snake_case__	:      Optional[Any]     		=	qa_dropout
						snake_case__	:      Union[str, Any]     		=	seq_classif_dropout
						super().__init__(**__lowercase   ,pad_token_id=__lowercase  )


class       a							(			__lowerCamelCase				):



		@property
		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						if self.task == "multiple-choice":
										snake_case__	:      str     		=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
										snake_case__	:      Optional[int]     		=	{0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						    ]  )

