
'''simple docstring'''
import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 			__A					(			unittest.TestCase      ):
 @property
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		1
   __A		:  int       				=		3
   __A		:  Optional[Any]       				=		8
   __A		:  str       				=		8

   __A		:  Optional[Any]       				=		torch.rand((batch_size, num_channels, height, width)							)

   return sample
 def 		lowercase_( self      :     int					, lowerCamelCase      :     Dict							):



   """simple docstring"""





   if isinstance(image[0]					, PIL.Image.Image							):
     return np.stack([np.array(lowercase_							) for i in image]					, axis=0							)
   elif isinstance(lowercase_					, torch.Tensor							):
     return image.cpu().numpy().transpose(0					, 2					, 3					, 1							)
   return image
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Dict       				=		VaeImageProcessor(do_resize=lowercase_					, do_normalize=lowercase_							)

   __A		:  List[str]       				=		self.dummy_sample
   __A		:  int       				=		self.to_np(lowercase_							)

   for output_type in ["pt", "np", "pil"]:
     __A		:  Union[str, Any]       				=		image_processor.postprocess(
         image_processor.preprocess(lowercase_							)					, output_type=lowercase_					, )
     __A		:  Union[str, Any]       				=		self.to_np(lowercase_							)
     __A		:  Union[str, Any]       				=		(input_np * 2_55).round() if output_type == """pil""" else input_np
     assert (
         np.abs(in_np - out_np							).max() < 1e-6
     ), f"decoded output does not match input for output_type {output_type}"
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   __A		:  List[Any]       				=		VaeImageProcessor(do_resize=lowercase_					, do_normalize=lowercase_							)
   __A		:  Dict       				=		self.dummy_sample.cpu().numpy().transpose(0					, 2					, 3					, 1							)

   for output_type in ["pt", "np", "pil"]:
     __A		:  List[Any]       				=		image_processor.postprocess(image_processor.preprocess(lowercase_							)					, output_type=lowercase_							)

     __A		:  int       				=		self.to_np(lowercase_							)
     __A		:  List[str]       				=		(input_np * 2_55).round() if output_type == """pil""" else input_np
     assert (
         np.abs(in_np - out_np							).max() < 1e-6
     ), f"decoded output does not match input for output_type {output_type}"
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Tuple       				=		VaeImageProcessor(do_resize=lowercase_					, do_normalize=lowercase_							)

   __A		:  str       				=		self.dummy_sample.cpu().numpy().transpose(0					, 2					, 3					, 1							)
   __A		:  Any       				=		image_processor.numpy_to_pil(lowercase_							)

   for output_type in ["pt", "np", "pil"]:
     __A		:  List[str]       				=		image_processor.postprocess(image_processor.preprocess(lowercase_							)					, output_type=lowercase_							)
     for i, o in zip(lowercase_					, lowercase_							):
       __A		:  Tuple       				=		np.array(lowercase_							)
       __A		:  Tuple       				=		self.to_np(lowercase_							) if output_type == """pil""" else (self.to_np(lowercase_							) * 2_55).round()
       assert (
           np.abs(in_np - out_np							).max() < 1e-6
       ), f"decoded output does not match input for output_type {output_type}"
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  Dict       				=		VaeImageProcessor(do_resize=lowercase_					, do_normalize=lowercase_							)

   __A		:  Tuple       				=		self.dummy_sample
   __A		:  List[str]       				=		input_pt_ad.squeeze(0							)

   __A		:  Tuple       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )
   __A		:  int       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )

   __A		:  List[Any]       				=		self.to_np(self.dummy_sample							)
   __A		:  List[Any]       				=		input_np_ad.squeeze(0							)

   __A		:  List[str]       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )
   __A		:  Any       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )

   assert np.abs(out_pt_ad - out_pt_ad							).max() < 1e-6
   assert np.abs(out_np_ad - out_np_ad							).max() < 1e-6






 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  Dict       				=		VaeImageProcessor(do_resize=lowercase_					, do_normalize=lowercase_							)

   __A		:  Optional[int]       				=		self.dummy_sample
   __A		:  Tuple       				=		list(lowercase_							)

   __A		:  Union[str, Any]       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )

   __A		:  List[Any]       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )

   __A		:  int       				=		self.to_np(self.dummy_sample							)
   list(lowercase_							)

   __A		:  Any       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )

   __A		:  Union[str, Any]       				=		image_processor.postprocess(
       image_processor.preprocess(lowercase_							)					, output_type="""np"""					, )

   assert np.abs(out_pt_ad - out_pt_list							).max() < 1e-6
   assert np.abs(out_np_ad - out_np_list							).max() < 1e-6








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase					:				Optional[int]   	={
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				int   	=[
		    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2Text2ForCausalLM',
		    'Speech2Text2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
		from .processing_speech_to_text_a import SpeechaTextaProcessor
		from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text_a import (
				    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextaForCausalLM,
				    SpeechaTextaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase					:				Union[str, Any]   	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

