
import os
import sys
import unittest


lowerCamelCase__     :		List[Any]        = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase__     :		List[Any]        = os.path.join(git_repo_path, """src""", """diffusers""")



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   find_backend("""    if not is_torch_available():""")
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	"""torch""")

   # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
   # self.assertEqual(backend_with_underscore, "tensorflow_text")

   lowercase__	: int     		=   find_backend("""    if not (is_torch_available() and is_transformers_available()):""")
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	"""torch_and_transformers""")

   # double_backend_with_underscore = find_backend(
   #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
   # )
   # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

   lowercase__	: Tuple     		=   find_backend(
       """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""")
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	"""torch_and_transformers_and_onnx""")


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   read_init()
   # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
   self.assertIn("""torch"""       ,	SCREAMING_SNAKE_CASE_)
   self.assertIn("""torch_and_transformers"""       ,	SCREAMING_SNAKE_CASE_)
   self.assertIn("""flax_and_transformers"""       ,	SCREAMING_SNAKE_CASE_)
   self.assertIn("""torch_and_transformers_and_onnx"""       ,	SCREAMING_SNAKE_CASE_)

   # Likewise, we can't assert on the exact content of a key
   self.assertIn("""UNet2DModel"""       ,	objects["""torch"""])
   self.assertIn("""FlaxUNet2DConditionModel"""       ,	objects["""flax"""])
   self.assertIn("""StableDiffusionPipeline"""       ,	objects["""torch_and_transformers"""])
   self.assertIn("""FlaxStableDiffusionPipeline"""       ,	objects["""flax_and_transformers"""])
   self.assertIn("""LMSDiscreteScheduler"""       ,	objects["""torch_and_scipy"""])
   self.assertIn("""OnnxStableDiffusionPipeline"""       ,	objects["""torch_and_transformers_and_onnx"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   create_dummy_object("""CONSTANT"""       ,	"""'torch'""")
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	"""\nCONSTANT = None\n""")

   lowercase__	: Any     		=   create_dummy_object("""function"""       ,	"""'torch'""")
   self.assertEqual(
       SCREAMING_SNAKE_CASE_       ,	"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n""")

   lowercase__	: Tuple     		=   """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
   lowercase__	: List[str]     		=   create_dummy_object("""FakeClass"""       ,	"""'torch'""")
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
   lowercase__	: List[Any]     		=   create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]})
   self.assertEqual(dummy_files["""torch"""]       ,	SCREAMING_SNAKE_CASE_)








from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 8       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Tuple     		=   do_pad
   lowercase__	: Optional[Any]     		=   pad_size


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return rescale(SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   get_image_size(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   (old_height // size + 1) * size - old_height
   lowercase__	: str     		=   (old_width // size + 1) * size - old_width

   return pad(SCREAMING_SNAKE_CASE_       ,	((0, pad_height), (0, pad_width))       ,	mode="""symmetric"""       ,	data_format=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: int     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: Union[str, Any]     		=   do_pad if do_pad is not None else self.do_pad
   lowercase__	: Optional[Any]     		=   pad_size if pad_size is not None else self.pad_size

   lowercase__	: str     		=   make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray.""")

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

   # All transformations expect numpy arrays.
   lowercase__	: List[Any]     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__	: str     		=   [self.rescale(image=SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_) for image in images]

   if do_pad:
     lowercase__	: List[str]     		=   [self.pad(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)






