import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class    lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	def 			SCREAMING_SNAKE_CASE_				(							self				)						->str:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Union[str, Any]:
								lowerCAmelCase			=		1
								lowerCAmelCase			=		3
								lowerCAmelCase			=		(32, 32)

								lowerCAmelCase			=		floats_tensor((batch_size, num_channels) + sizes		,			rng=random.Random(0				)				).to(__SCREAMING_SNAKE_CASE				)
								return image


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Dict:
								torch.manual_seed(0				)
								lowerCAmelCase			=		UNetaDConditionModel(
								    block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=4		,			out_channels=4		,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,			cross_attention_dim=32		,			)
								return model


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[int]:
								torch.manual_seed(0				)
								lowerCAmelCase			=		AutoencoderKL(
								    block_out_channels=[32, 64]		,			in_channels=3		,			out_channels=3		,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,			latent_channels=4		,			)
								return model


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[Any]:
								torch.manual_seed(0				)
								lowerCAmelCase			=		RobertaSeriesConfig(
								    hidden_size=32		,			project_dim=32		,			intermediate_size=37		,			layer_norm_eps=1e-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=5006		,			)
								return RobertaSeriesModelWithTransformation(__SCREAMING_SNAKE_CASE				)


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[str]:



								def extract(*__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				):




															class    lowercase_  :



																"""simple docstring"""





																def __init__(							self				)						->Any:
																							lowerCAmelCase			=		torch.ones([0]				)




																def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Tuple:
																							self.pixel_values.to(__SCREAMING_SNAKE_CASE				)
																							return self
															return Out()

								return extract


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->str:
								lowerCAmelCase			=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCAmelCase			=		self.dummy_cond_unet
								lowerCAmelCase			=		PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		self.dummy_vae
								lowerCAmelCase			=		self.dummy_text_encoder
								lowerCAmelCase			=		XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''				)
								lowerCAmelCase			=		77

								lowerCAmelCase			=		self.dummy_image.to(__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		init_image / 2 + 0.5

								# make sure here that pndm scheduler skips prk
								lowerCAmelCase			=		AltDiffusionImgaImgPipeline(
								    unet=__SCREAMING_SNAKE_CASE		,			scheduler=__SCREAMING_SNAKE_CASE		,			vae=__SCREAMING_SNAKE_CASE		,			text_encoder=__SCREAMING_SNAKE_CASE		,			tokenizer=__SCREAMING_SNAKE_CASE		,			safety_checker=__SCREAMING_SNAKE_CASE		,			feature_extractor=self.dummy_extractor		,			)
								lowerCAmelCase			=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor		,			do_normalize=__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		alt_pipe.to(__SCREAMING_SNAKE_CASE				)
								alt_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

								lowerCAmelCase			=		'''A painting of a squirrel eating a burger'''
								lowerCAmelCase			=		torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(0				)
								lowerCAmelCase			=		alt_pipe(
								    [prompt]		,			generator=__SCREAMING_SNAKE_CASE		,			guidance_scale=6.0		,			num_inference_steps=2		,			output_type='''np'''		,			image=__SCREAMING_SNAKE_CASE		,			)

								lowerCAmelCase			=		output.images

								lowerCAmelCase			=		torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(0				)
								lowerCAmelCase			=		alt_pipe(
								    [prompt]		,			generator=__SCREAMING_SNAKE_CASE		,			guidance_scale=6.0		,			num_inference_steps=2		,			output_type='''np'''		,			image=__SCREAMING_SNAKE_CASE		,			return_dict=__SCREAMING_SNAKE_CASE		,			)[0]

								lowerCAmelCase			=		image[0, -3:, -3:, -1]
								lowerCAmelCase			=		image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCAmelCase			=		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 5e-3
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 5e-3


	@unittest.skipIf(torch_device != '''cuda'''		,			'''This test requires a GPU'''				)
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								lowerCAmelCase			=		self.dummy_cond_unet
								lowerCAmelCase			=		PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		self.dummy_vae
								lowerCAmelCase			=		self.dummy_text_encoder
								lowerCAmelCase			=		XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''				)
								lowerCAmelCase			=		77

								lowerCAmelCase			=		self.dummy_image.to(__SCREAMING_SNAKE_CASE				)

								# put models in fp16
								lowerCAmelCase			=		unet.half()
								lowerCAmelCase			=		vae.half()
								lowerCAmelCase			=		bert.half()

								# make sure here that pndm scheduler skips prk
								lowerCAmelCase			=		AltDiffusionImgaImgPipeline(
								    unet=__SCREAMING_SNAKE_CASE		,			scheduler=__SCREAMING_SNAKE_CASE		,			vae=__SCREAMING_SNAKE_CASE		,			text_encoder=__SCREAMING_SNAKE_CASE		,			tokenizer=__SCREAMING_SNAKE_CASE		,			safety_checker=__SCREAMING_SNAKE_CASE		,			feature_extractor=self.dummy_extractor		,			)
								lowerCAmelCase			=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor		,			do_normalize=__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		alt_pipe.to(__SCREAMING_SNAKE_CASE				)
								alt_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

								lowerCAmelCase			=		'''A painting of a squirrel eating a burger'''
								lowerCAmelCase			=		torch.manual_seed(0				)
								lowerCAmelCase			=		alt_pipe(
								    [prompt]		,			generator=__SCREAMING_SNAKE_CASE		,			num_inference_steps=2		,			output_type='''np'''		,			image=__SCREAMING_SNAKE_CASE		,			).images

								assert image.shape == (1, 32, 32, 3)




	@unittest.skipIf(torch_device != '''cuda'''		,			'''This test requires a GPU'''				)
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								lowerCAmelCase			=		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/img2img/sketch-mountains-input.jpg'''				)
								# resize to resolution that is divisible by 8 but not 16 or 32
								lowerCAmelCase			=		init_image.resize((760, 504)				)

								lowerCAmelCase			=		'''BAAI/AltDiffusion'''
								lowerCAmelCase			=		AltDiffusionImgaImgPipeline.from_pretrained(
								    __SCREAMING_SNAKE_CASE		,			safety_checker=__SCREAMING_SNAKE_CASE		,			)
								pipe.to(__SCREAMING_SNAKE_CASE				)
								pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)
								pipe.enable_attention_slicing()

								lowerCAmelCase			=		'''A fantasy landscape, trending on artstation'''

								lowerCAmelCase			=		torch.manual_seed(0				)
								lowerCAmelCase			=		pipe(
								    prompt=__SCREAMING_SNAKE_CASE		,			image=__SCREAMING_SNAKE_CASE		,			strength=0.7_5		,			guidance_scale=7.5		,			generator=__SCREAMING_SNAKE_CASE		,			output_type='''np'''		,			)
								lowerCAmelCase			=		output.images[0]

								lowerCAmelCase			=		image[255:258, 383:386, -1]

								assert image.shape == (504, 760, 3)
								lowerCAmelCase			=		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2




@slow
@require_torch_gpu
class    lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	def 			SCREAMING_SNAKE_CASE_				(							self				)						->str:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Dict:
								lowerCAmelCase			=		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/img2img/sketch-mountains-input.jpg'''				)
								lowerCAmelCase			=		init_image.resize((768, 512)				)
								lowerCAmelCase			=		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''				)

								lowerCAmelCase			=		'''BAAI/AltDiffusion'''
								lowerCAmelCase			=		AltDiffusionImgaImgPipeline.from_pretrained(
								    __SCREAMING_SNAKE_CASE		,			safety_checker=__SCREAMING_SNAKE_CASE		,			)
								pipe.to(__SCREAMING_SNAKE_CASE				)
								pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)
								pipe.enable_attention_slicing()

								lowerCAmelCase			=		'''A fantasy landscape, trending on artstation'''

								lowerCAmelCase			=		torch.manual_seed(0				)
								lowerCAmelCase			=		pipe(
								    prompt=__SCREAMING_SNAKE_CASE		,			image=__SCREAMING_SNAKE_CASE		,			strength=0.7_5		,			guidance_scale=7.5		,			generator=__SCREAMING_SNAKE_CASE		,			output_type='''np'''		,			)
								lowerCAmelCase			=		output.images[0]

								assert image.shape == (512, 768, 3)
								# img2img is flaky across GPUs even in fp32, so using MAE here
								assert np.abs(expected_image - image				).max() < 1e-2








'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

      super().__init__(
          bos_token_id=A ,   eos_token_id=A ,   tie_word_embeddings=A ,   **A       )






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(A ,   task=A ,   patching_specs=A ,   use_past=A       )
      if not getattr(self._config ,   '''pad_token_id''' ,   A       ):
           # TODO: how to do that better?
           _a      =  0



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



 def 					a__  (self ,   A ,   A = -1 ,   A = -1 ,   A = False ,   A = None ,   )    ->     Mapping[str, Any]:


      """simple docstring"""



      _a      =  super(A ,   self       ).generate_dummy_inputs(
          A ,   batch_size=A ,   seq_length=A ,   is_pair=A ,   framework=A       )

      # We need to order the input in the way they appears in the forward()
      _a      =  OrderedDict({'''input_ids''': common_inputs['''input_ids''']}       )

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
           else:
                import torch

                _a  ,    _a      =  common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a      =  seqlen + 2
                _a      =  (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a      =  [
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



