


'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class      snake_case    (    SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  StableUnCLIPPipeline
  _lowerCamelCase		    =  TEXT_TO_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_BATCH_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  32
   lowerCamelCase_      =  embedder_hidden_size

   # prior components

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModelWithProjection(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=__UpperCAmelCase			,  projection_dim=__UpperCAmelCase			,  intermediate_size=37			,  layer_norm_eps=1e-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  PriorTransformer(
       num_attention_heads=2			,  attention_head_dim=12			,  embedding_dim=__UpperCAmelCase			,  num_layers=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDPMScheduler(
       variance_type="fixed_small_log"			,  prediction_type="sample"			,  num_train_timesteps=1000			,  clip_sample=__UpperCAmelCase			,  clip_sample_range=5.0			,  beta_schedule="squaredcos_cap_v2"			,  )

   # regular denoising components

   torch.manual_seed(0     )
   lowerCamelCase_      =  StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase     )
   lowerCamelCase_      =  DDPMScheduler(beta_schedule="squaredcos_cap_v2"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=__UpperCAmelCase			,  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=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  UNetaDConditionModel(
       sample_size=32			,  in_channels=4			,  out_channels=4			,  down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")			,  up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")			,  block_out_channels=(32, 64)			,  attention_head_dim=(2, 4)			,  class_embed_type="projection"			,  projection_class_embeddings_input_dim=embedder_projection_dim * 2			,  cross_attention_dim=__UpperCAmelCase			,  layers_per_block=1			,  upcast_attention=__UpperCAmelCase			,  use_linear_projection=__UpperCAmelCase			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDIMScheduler(
       beta_schedule="scaled_linear"			,  beta_start=0.00_085			,  beta_end=0.012			,  prediction_type="v_prediction"			,  set_alpha_to_one=__UpperCAmelCase			,  steps_offset=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  AutoencoderKL()

   lowerCamelCase_      =  {
       # prior components
       """prior_tokenizer""": prior_tokenizer,
       """prior_text_encoder""": prior_text_encoder,
       """prior""": prior,
       """prior_scheduler""": prior_scheduler,
       # image noising components
       """image_normalizer""": image_normalizer,
       """image_noising_scheduler""": image_noising_scheduler,
       # regular denoising components
       """tokenizer""": tokenizer,
       """text_encoder""": text_encoder,
       """unet""": unet,
       """scheduler""": scheduler,
       """vae""": vae,
   }

   return components






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=0     ):







   """simple docstring"""
   if str(__UpperCAmelCase     ).startswith("mps"     ):
    lowerCamelCase_      =  torch.manual_seed(__UpperCAmelCase     )
   else:
    lowerCamelCase_      =  torch.Generator(device=__UpperCAmelCase     ).manual_seed(__UpperCAmelCase     )
   lowerCamelCase_      =  {
       """prompt""": """A painting of a squirrel eating a burger""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """prior_num_inference_steps""": 2,
       """output_type""": """numpy""",
   }
   return inputs






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device == """cpu"""

   self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device in ["""cpu""", """mps"""]

   self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase     )





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"     )

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   pipe.to(__UpperCAmelCase     )
   pipe.set_progress_bar_config(disable=__UpperCAmelCase     )
   # stable unclip will oom when integration tests are run on a V100,
   # so turn on memory savings
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  torch.Generator(device="cpu"     ).manual_seed(0     )
   lowerCamelCase_      =  pipe("anime turle"			,  generator=__UpperCAmelCase			,  output_type="np"     )

   lowerCamelCase_      =  output.images[0]

   assert image.shape == (768, 768, 3)

   assert_mean_pixel_difference(__UpperCAmelCase			,  __UpperCAmelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   lowerCamelCase_      =  pipe.to(__UpperCAmelCase     )
   pipe.set_progress_bar_config(disable=__UpperCAmelCase     )
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  pipe(
       "anime turtle"			,  prior_num_inference_steps=2			,  num_inference_steps=2			,  output_type="np"			,  )

   lowerCamelCase_      =  torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9








'''simple docstring'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase      						=	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
 import torch

 print('''Torch version:''', torch.__version__)
 print('''Cuda available:''', torch.cuda.is_available())
 print('''Cuda version:''', torch.version.cuda)
 print('''CuDNN version:''', torch.backends.cudnn.version())
 print('''Number of GPUs available:''', torch.cuda.device_count())
 print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
 print('''Torch version:''', None)

try:
 import deepspeed

 print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
 print('''DeepSpeed version:''', None)

try:
 import tensorflow as tf

 print('''TensorFlow version:''', tf.__version__)
 print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
 print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
 print('''TensorFlow version:''', None)


