





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE	 =							{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure)









'''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    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      "ZinengTang/tvlt-base"
     _lowerCAmelCase				=      tempfile.mkdtemp()




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Any:
     return TvltImageProcessor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							str:
     return TvltFeatureExtractor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     shutil.rmtree(self.tmpdirname		)




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

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

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




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)

     _lowerCAmelCase				=      feature_extractor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

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

     _lowerCAmelCase				=      image_processor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)
     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      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"			,     )


