import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class     _A   (    __UpperCamelCase	, __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=AltDiffusionPipeline
  SCREAMING_SNAKE_CASE_	:	Tuple						=TEXT_TO_IMAGE_PARAMS
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=TEXT_TO_IMAGE_BATCH_PARAMS
  SCREAMING_SNAKE_CASE_	:	List[Any]						=TEXT_TO_IMAGE_IMAGE_PARAMS
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=TEXT_TO_IMAGE_IMAGE_PARAMS






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       torch.manual_seed(0	)
       UpperCamelCase__										=      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   ,				)
       UpperCamelCase__										=      DDIMScheduler(
           beta_start=0.00085   ,				beta_end=0.012   ,				beta_schedule='''scaled_linear'''   ,				clip_sample=SCREAMING_SNAKE_CASE_   ,				set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,				)
       torch.manual_seed(0	)
       UpperCamelCase__										=      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   ,				)

       # TODO: address the non-deterministic text encoder (fails for save-load tests)
       # torch.manual_seed(0)
       # text_encoder_config = RobertaSeriesConfig(
       #     hidden_size=32,
       #     project_dim=32,
       #     intermediate_size=37,
       #     layer_norm_eps=1e-05,
       #     num_attention_heads=4,
       #     num_hidden_layers=5,
       #     vocab_size=5002,
       # )
       # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

       torch.manual_seed(0	)
       UpperCamelCase__										=      CLIPTextConfig(
           bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=32   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=5002   ,				)
       UpperCamelCase__										=      CLIPTextModel(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''	)
       UpperCamelCase__										=      77

       UpperCamelCase__										=      {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=0	)		->      int:





       '''simple docstring'''





       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            UpperCamelCase__										=      torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            UpperCamelCase__										=      torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       UpperCamelCase__										=      self.get_dummy_components()
       torch.manual_seed(0	)
       UpperCamelCase__										=      RobertaSeriesConfig(
           hidden_size=32   ,				project_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				vocab_size=5002   ,				)
       # TODO: remove after fixing the non-deterministic text encoder
       UpperCamelCase__										=      RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      text_encoder

       UpperCamelCase__										=      AltDiffusionPipeline(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      alt_pipe.to(SCREAMING_SNAKE_CASE_	)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      '''A photo of an astronaut'''
       UpperCamelCase__										=      alt_pipe(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      output.images
       UpperCamelCase__										=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase__										=      np.array(
           [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]	)

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






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       UpperCamelCase__										=      self.get_dummy_components()
       UpperCamelCase__										=      PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
       torch.manual_seed(0	)
       UpperCamelCase__										=      RobertaSeriesConfig(
           hidden_size=32   ,				project_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				vocab_size=5002   ,				)
       # TODO: remove after fixing the non-deterministic text encoder
       UpperCamelCase__										=      RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      text_encoder
       UpperCamelCase__										=      AltDiffusionPipeline(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      alt_pipe.to(SCREAMING_SNAKE_CASE_	)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      alt_pipe(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      output.images
       UpperCamelCase__										=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase__										=      np.array(
           [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]	)

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



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






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,				safety_checker=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      alt_pipe.to(SCREAMING_SNAKE_CASE_	)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''A painting of a squirrel eating a burger'''
       UpperCamelCase__										=      torch.manual_seed(0	)
       UpperCamelCase__										=      alt_pipe([prompt]   ,				generator=SCREAMING_SNAKE_CASE_   ,				guidance_scale=6.0   ,				num_inference_steps=20   ,				output_type='''np'''	)

       UpperCamelCase__										=      output.images

       UpperCamelCase__										=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__										=      np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]	)

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






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,				subfolder='''scheduler'''	)

       UpperCamelCase__										=      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,				scheduler=SCREAMING_SNAKE_CASE_   ,				safety_checker=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      alt_pipe.to(SCREAMING_SNAKE_CASE_	)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''A painting of a squirrel eating a burger'''
       UpperCamelCase__										=      torch.manual_seed(0	)

       UpperCamelCase__										=      alt_pipe([prompt]   ,				generator=SCREAMING_SNAKE_CASE_   ,				num_inference_steps=2   ,				output_type='''numpy'''	)
       UpperCamelCase__										=      output.images

       UpperCamelCase__										=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__										=      np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]	)

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




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

     return x * tf.tanh(tf.math.softplus(A							)							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)							))







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







def  __UpperCamelCase    (						A							):
     return tf.clip_by_value(_gelu(A							) ,							-10 ,							10							)







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


__magic_name__								={
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}







def  __UpperCamelCase    (						A							):
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}"							)



