




"""simple docstring"""



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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''fnet'''




    def __init__(   self						:	str     ,			snake_case_						:	str=32_000     ,			snake_case_						:	Tuple=768     ,			snake_case_						:	Dict=12     ,			snake_case_						:	List[Any]=3_072     ,			snake_case_						:	Dict="gelu_new"     ,			snake_case_						:	Optional[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	List[str]=4     ,			snake_case_						:	Any=0.02     ,			snake_case_						:	int=1e-12     ,			snake_case_						:	Optional[Any]=False     ,			snake_case_						:	Optional[Any]=512     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Optional[int]=1     ,			snake_case_						:	Dict=2     ,			**snake_case_						:	List[Any]     ,			)				->							Optional[Any]:




     '''simple docstring'''

     super().__init__(pad_token_id=snake_case_     ,			bos_token_id=snake_case_     ,			eos_token_id=snake_case_     ,			**snake_case_				)

     A__	=    vocab_size
     A__	=    max_position_embeddings
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    initializer_range
     A__	=    type_vocab_size
     A__	=    layer_norm_eps
     A__	=    use_tpu_fourier_optimizations
     A__	=    tpu_short_seq_length











"""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      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    "ZinengTang/tvlt-base"
     A__	=    tempfile.mkdtemp()




    def 							__magic_name__							(   self						:	int     ,			**snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[int]     ,			**snake_case_						:	str				)				->							List[str]:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Any:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

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

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




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




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([12_000]				)

     A__	=    feature_extractor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(audio=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 							__magic_name__							(   self						:	Tuple				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

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

     A__	=    image_processor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(images=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 							__magic_name__							(   self						:	Tuple				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([12_000]				)
     A__	=    np.ones([3, 224, 224]				)

     A__	=    processor(audio=snake_case_     ,			images=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(snake_case_				):
      processor()




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=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"     ,			)





