

'''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 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	Dict)      ->					Tuple:
         lowercase_     =      "ZinengTang/tvlt-base"
         lowercase_     =      tempfile.mkdtemp()


   def __UpperCAmelCase       (   self				:	List[str]   ,		**__lowerCAmelCase				:	Dict)      ->					List[str]:
         return TvltImageProcessor.from_pretrained(self.checkpoint   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					Optional[int]:
         return TvltFeatureExtractor.from_pretrained(self.checkpoint   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Dict)      ->					str:
         shutil.rmtree(self.tmpdirname)


   def __UpperCAmelCase       (   self				:	List[str])      ->					Union[str, Any]:
         lowercase_     =      self.get_image_processor()
         lowercase_     =      self.get_feature_extractor()

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

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


   def __UpperCAmelCase       (   self				:	List[str])      ->					int:
         lowercase_     =      self.get_image_processor()
         lowercase_     =      self.get_feature_extractor()

         lowercase_     =      TvltProcessor(image_processor=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase)

         lowercase_     =      np.ones([1_2000])

         lowercase_     =      feature_extractor(__lowerCAmelCase   ,		return_tensors="np")
         lowercase_     =      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 __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_     =      self.get_image_processor()
         lowercase_     =      self.get_feature_extractor()

         lowercase_     =      TvltProcessor(image_processor=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase)

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

         lowercase_     =      image_processor(__lowerCAmelCase   ,		return_tensors="np")
         lowercase_     =      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 __UpperCAmelCase       (   self				:	List[str])      ->					Optional[Any]:
         lowercase_     =      self.get_image_processor()
         lowercase_     =      self.get_feature_extractor()

         lowercase_     =      TvltProcessor(image_processor=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase)

         lowercase_     =      np.ones([1_2000])
         lowercase_     =      np.ones([3, 224, 224])

         lowercase_     =      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 __UpperCAmelCase       (   self				:	List[str])      ->					Tuple:
         lowercase_     =      self.get_image_processor()
         lowercase_     =      self.get_feature_extractor()

         lowercase_     =      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"   ,		)







'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






