






"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					1
     __A		     =					3
     __A		     =					8
     __A		     =					8

     __A		     =					torch.rand((batch_size, num_channels, height, width)    )

     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     if isinstance(image[0],			PIL.Image.Image    ):
       return np.stack([np.array(_lowerCamelCase    ) for i in image],			axis=0    )
     elif isinstance(_lowerCamelCase,			torch.Tensor    ):
       return image.cpu().numpy().transpose(0,			2,			3,			1    )
     return image


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample
     __A		     =					self.to_np(_lowerCamelCase    )

     for output_type in ["pt", "np", "pil"]:
       __A		     =					image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase    ),			output_type=_lowerCamelCase,			)
       __A		     =					self.to_np(_lowerCamelCase    )
       __A		     =					(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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )
     __A		     =					self.dummy_sample.cpu().numpy().transpose(0,			2,			3,			1    )

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

       __A		     =					self.to_np(_lowerCamelCase    )
       __A		     =					(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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample.cpu().numpy().transpose(0,			2,			3,			1    )
     __A		     =					image_processor.numpy_to_pil(_lowerCamelCase    )

     for output_type in ["pt", "np", "pil"]:
       __A		     =					image_processor.postprocess(image_processor.preprocess(_lowerCamelCase    ),			output_type=_lowerCamelCase    )
       for i, o in zip(_lowerCamelCase,			_lowerCamelCase    ):
         __A		     =					np.array(_lowerCamelCase    )
         __A		     =					self.to_np(_lowerCamelCase    ) if output_type == '''pil''' else (self.to_np(_lowerCamelCase    ) * 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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample
     __A		     =					input_pt_ad.squeeze(0    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)
     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					self.to_np(self.dummy_sample    )
     __A		     =					input_np_ad.squeeze(0    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)
     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample
     __A		     =					list(_lowerCamelCase    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					self.to_np(self.dummy_sample    )
     list(_lowerCamelCase    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			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











'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




