





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     ):



      """simple docstring"""






      def     UpperCAmelCase		(			self )							-> Union[str, Any]:






        '''simple docstring'''






        __snake_case :					int          =							'''ZinengTang/tvlt-base'''
        __snake_case :					Optional[Any]          =							tempfile.mkdtemp()







      def     UpperCAmelCase		(			self					,    **UpperCAmelCase )							-> Tuple:






        '''simple docstring'''






        return TvltImageProcessor.from_pretrained(self.checkpoint					,    **__SCREAMING_SNAKE_CASE )







      def     UpperCAmelCase		(			self					,    **UpperCAmelCase )							-> str:






        '''simple docstring'''






        return TvltFeatureExtractor.from_pretrained(self.checkpoint					,    **__SCREAMING_SNAKE_CASE )







      def     UpperCAmelCase		(			self )							-> Optional[int]:






        '''simple docstring'''






        shutil.rmtree(self.tmpdirname )







      def     UpperCAmelCase		(			self )							-> Any:






        '''simple docstring'''






        __snake_case :					Optional[Any]          =							self.get_image_processor()
        __snake_case :					Tuple          =							self.get_feature_extractor()

        __snake_case :					Dict          =							TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,    feature_extractor=__SCREAMING_SNAKE_CASE )
        processor.save_pretrained(self.tmpdirname )
        __snake_case :					Optional[Any]          =							TvltProcessor.from_pretrained(self.tmpdirname )

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







      def     UpperCAmelCase		(			self )							-> List[str]:






        '''simple docstring'''






        __snake_case :					Union[str, Any]          =							self.get_image_processor()
        __snake_case :					int          =							self.get_feature_extractor()

        __snake_case :					Union[str, Any]          =							TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,    feature_extractor=__SCREAMING_SNAKE_CASE )

        __snake_case :					str          =							np.ones([12000] )

        __snake_case :					Dict          =							feature_extractor(__SCREAMING_SNAKE_CASE					,    return_tensors="np" )
        __snake_case :					Dict          =							processor(audio=__SCREAMING_SNAKE_CASE					,    return_tensors="np" )

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







      def     UpperCAmelCase		(			self )							-> Optional[int]:






        '''simple docstring'''






        __snake_case :					Tuple          =							self.get_image_processor()
        __snake_case :					Tuple          =							self.get_feature_extractor()

        __snake_case :					str          =							TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,    feature_extractor=__SCREAMING_SNAKE_CASE )

        __snake_case :					List[Any]          =							np.ones([3, 224, 224] )

        __snake_case :					Optional[int]          =							image_processor(__SCREAMING_SNAKE_CASE					,    return_tensors="np" )
        __snake_case :					Tuple          =							processor(images=__SCREAMING_SNAKE_CASE					,    return_tensors="np" )

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







      def     UpperCAmelCase		(			self )							-> str:






        '''simple docstring'''






        __snake_case :					List[str]          =							self.get_image_processor()
        __snake_case :					Optional[Any]          =							self.get_feature_extractor()

        __snake_case :					Union[str, Any]          =							TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,    feature_extractor=__SCREAMING_SNAKE_CASE )

        __snake_case :					Dict          =							np.ones([12000] )
        __snake_case :					str          =							np.ones([3, 224, 224] )

        __snake_case :					List[Any]          =							processor(audio=__SCREAMING_SNAKE_CASE					,    images=__SCREAMING_SNAKE_CASE )

        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(__SCREAMING_SNAKE_CASE ):
          processor()







      def     UpperCAmelCase		(			self )							-> List[Any]:






        '''simple docstring'''






        __snake_case :					int          =							self.get_image_processor()
        __snake_case :					str          =							self.get_feature_extractor()

        __snake_case :					Dict          =							TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,    feature_extractor=__SCREAMING_SNAKE_CASE )

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


lowerCamelCase  =logging.get_logger(__name__)

lowerCamelCase  ={
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	'''gpt_neox_japanese'''






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE=3_2_0_0_0    ,    __SCREAMING_SNAKE_CASE=2_5_6_0    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=1.00    ,    __SCREAMING_SNAKE_CASE=1_0_0_0_0    ,    __SCREAMING_SNAKE_CASE=2_0_4_8    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=1e-5    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=3_1_9_9_6    ,    __SCREAMING_SNAKE_CASE=3_1_9_9_9    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.0    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> List[str]:




           """simple docstring"""







           super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,    eos_token_id=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					vocab_size
           UpperCamelCase__     : int											=					max_position_embeddings
           UpperCamelCase__     : Union[str, Any]											=					hidden_size
           UpperCamelCase__     : Any											=					num_hidden_layers
           UpperCamelCase__     : List[Any]											=					num_attention_heads
           UpperCamelCase__     : int											=					intermediate_multiple_size
           UpperCamelCase__     : List[Any]											=					hidden_act
           UpperCamelCase__     : Tuple											=					rotary_pct
           UpperCamelCase__     : Optional[int]											=					rotary_emb_base
           UpperCamelCase__     : Optional[Any]											=					initializer_range
           UpperCamelCase__     : Optional[int]											=					layer_norm_eps
           UpperCamelCase__     : Optional[Any]											=					use_cache
           UpperCamelCase__     : List[str]											=					attention_dropout
           UpperCamelCase__     : Optional[int]											=					hidden_dropout


