






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ImageGPTImageProcessor


class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     def __init__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Tuple=7 ,	UpperCamelCase_       :			Optional[int]=3 ,	UpperCamelCase_       :			Union[str, Any]=18 ,	UpperCamelCase_       :			int=30 ,	UpperCamelCase_       :			Optional[Any]=400 ,	UpperCamelCase_       :			Any=True ,	UpperCamelCase_       :			List[Any]=None ,	UpperCamelCase_       :			List[str]=True ,	):


         """simple docstring"""



         __A         =	size if size is not None else {"""height""": 18, """width""": 18}
         __A         =	parent
         __A         =	batch_size
         __A         =	num_channels
         __A         =	image_size
         __A         =	min_resolution
         __A         =	max_resolution
         __A         =	do_resize
         __A         =	size
         __A         =	do_normalize





     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         return {
             # here we create 2 clusters for the sake of simplicity
             "clusters": np.asarray(
                 [
                     [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                     [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
                 ]				),
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
         }


@require_torch
@require_vision
class       __lowercase      (	lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				ImageGPTImageProcessor if is_vision_available() else None




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	ImageGPTImageProcessingTester(self				)




     @property
     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""clusters"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""do_resize"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""size"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""do_normalize"""				)				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size ,	{"""height""": 18, """width""": 18}				)

         __A         =	self.image_processing_class.from_dict(self.image_processor_dict ,	size=42				)
         self.assertEqual(image_processor.size ,	{"""height""": 42, """width""": 42}				)




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


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)
         __A         =	json.loads(image_processor.to_json_string()				)
         for key, value in self.image_processor_dict.items():
             if key == "clusters":
                 self.assertTrue(np.array_equal(UpperCamelCase_ ,	obj[key]				)				)
             else:
                 self.assertEqual(obj[key] ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
             __A         =	os.path.join(UpperCamelCase_ ,	"""image_processor.json"""				)
             image_processor_first.to_json_file(UpperCamelCase_				)
             __A         =	self.image_processing_class.from_json_file(UpperCamelCase_				).to_dict()

         __A         =	image_processor_first.to_dict()
         for key, value in image_processor_first.items():
             if key == "clusters":
                 self.assertTrue(np.array_equal(UpperCamelCase_ ,	image_processor_second[key]				)				)
             else:
                 self.assertEqual(image_processor_first[key] ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(UpperCamelCase_				)
             __A         =	self.image_processing_class.from_pretrained(UpperCamelCase_				).to_dict()

         __A         =	image_processor_first.to_dict()
         for key, value in image_processor_first.items():
             if key == "clusters":
                 self.assertTrue(np.array_equal(UpperCamelCase_ ,	image_processor_second[key]				)				)
             else:
                 self.assertEqual(image_processor_first[key] ,	UpperCamelCase_				)





     @unittest.skip("""ImageGPT requires clusters at initialization"""				)
     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         pass



def 			_SCREAMING_SNAKE_CASE						( )						->      Dict:





    """simple docstring"""

    __A         =	load_dataset("""hf-internal-testing/fixtures_image_utils"""			,				split="""test"""    )

    __A         =	Image.open(dataset[4]["""file"""]    )
    __A         =	Image.open(dataset[5]["""file"""]    )

    __A         =	[imagea, imagea]

    return images






@require_vision
@require_torch
class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""				)

         __A         =	prepare_images()

         # test non-batched
         __A         =	image_processing(images[0] ,	return_tensors="""pt"""				)

         self.assertIsInstance(encoding.input_ids ,	torch.LongTensor				)
         self.assertEqual(encoding.input_ids.shape ,	(1, 1_024)				)

         __A         =	[306, 191, 191]
         self.assertEqual(encoding.input_ids[0, :3].tolist() ,	UpperCamelCase_				)

         # test batched
         __A         =	image_processing(UpperCamelCase_ ,	return_tensors="""pt"""				)

         self.assertIsInstance(encoding.input_ids ,	torch.LongTensor				)
         self.assertEqual(encoding.input_ids.shape ,	(2, 1_024)				)

         __A         =	[303, 13, 13]
         self.assertEqual(encoding.input_ids[1, -3:].tolist() ,	UpperCamelCase_				)












from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42


class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''




     @register_to_config
     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			Tuple[str] = ("DownEncoderBlock2D",) ,	UpperCamelCase_       :			Tuple[str] = ("UpDecoderBlock2D",) ,	UpperCamelCase_       :			Tuple[int] = (64,) ,	UpperCamelCase_       :			int = 1 ,	UpperCamelCase_       :			str = "silu" ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			int = 256 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			float = 0.18215 ,	UpperCamelCase_       :			str = "group" ,	):


         """simple docstring"""



         super().__init__()

         # pass init params to Encoder
         __A         =	Encoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	down_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	double_z=UpperCamelCase_ ,	)

         __A         =	vq_embed_dim if vq_embed_dim is not None else latent_channels

         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
         __A         =	VectorQuantizer(UpperCamelCase_ ,	UpperCamelCase_ ,	beta=0.25 ,	remap=UpperCamelCase_ ,	sane_index_shape=UpperCamelCase_				)
         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)

         # pass init params to Decoder
         __A         =	Decoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	up_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	norm_type=UpperCamelCase_ ,	)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	self.encoder(UpperCamelCase_				)
         __A         =	self.quant_conv(UpperCamelCase_				)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=UpperCamelCase_				)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         if not force_not_quantize:
             __A    ,	__A    ,	__A         =	self.quantize(UpperCamelCase_				)
         else:
             __A         =	h
         __A         =	self.post_quant_conv(UpperCamelCase_				)
         __A         =	self.decoder(UpperCamelCase_ ,	quant if self.config.norm_type == """spatial""" else None				)

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	sample
         __A         =	self.encode(UpperCamelCase_				).latents
         __A         =	self.decode(UpperCamelCase_				).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)




