

'''simple docstring'''




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()



@skip_mps
class     lowercase_		(						a	,    a	,    a	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	Union[str, Any]      					=				StableDiffusionModelEditingPipeline
  __lowerCAmelCase   :	List[Any]      					=				TEXT_TO_IMAGE_PARAMS
  __lowerCAmelCase   :	List[str]      					=				TEXT_TO_IMAGE_BATCH_PARAMS
  __lowerCAmelCase   :	Any      					=				TEXT_TO_IMAGE_IMAGE_PARAMS
  __lowerCAmelCase   :	Union[str, Any]      					=				TEXT_TO_IMAGE_IMAGE_PARAMS
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    torch.manual_seed(0       )
    UpperCAmelCase  							=		UNetaDConditionModel(
        block_out_channels=(3_2, 6_4)				,	layers_per_block=2				,	sample_size=3_2				,	in_channels=4				,	out_channels=4				,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,	cross_attention_dim=3_2				,	)
    UpperCAmelCase  							=		DDIMScheduler()
    torch.manual_seed(0       )
    UpperCAmelCase  							=		AutoencoderKL(
        block_out_channels=[3_2, 6_4]				,	in_channels=3				,	out_channels=3				,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,	latent_channels=4				,	)
    torch.manual_seed(0       )
    UpperCAmelCase  							=		CLIPTextConfig(
        bos_token_id=0				,	eos_token_id=2				,	hidden_size=3_2				,	intermediate_size=3_7				,	layer_norm_eps=1E-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=1_0_0_0				,	)
    UpperCAmelCase  							=		CLIPTextModel(a_       )
    UpperCAmelCase  							=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

    UpperCAmelCase  							=		{
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components
  def   snake_case_     (       self				,	a_				,	a_=0       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		torch.manual_seed(a_       )
    UpperCAmelCase  							=		{
        'prompt': 'A field of roses',
        'generator': generator,
        # Setting height and width to None to prevent OOMs on CPU.
        'height': None,
        'width': None,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
    }
    return inputs
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    UpperCAmelCase  							=		sd_pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase  							=		np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    UpperCAmelCase  							=		'french fries'
    UpperCAmelCase  							=		sd_pipe(**a_				,	negative_prompt=a_       )
    UpperCAmelCase  							=		output.images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase  							=		np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		EulerAncestralDiscreteScheduler(
        beta_start=0.0_0085				,	beta_end=0.012				,	beta_schedule='scaled_linear'       )
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    UpperCAmelCase  							=		sd_pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase  							=		np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase  							=		self.get_dummy_components()
    UpperCAmelCase  							=		PNDMScheduler()
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline(**a_       )
    UpperCAmelCase  							=		sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCAmelCase  							=		self.get_dummy_inputs(a_       )
    # the pipeline does not expect pndm so test if it raises error.
    with self.assertRaises(a_       ):
      UpperCAmelCase  							=		sd_pipe(**a_       ).images
  def   snake_case_     (       self       )							->			Optional[Any]:


    """simple docstring"""
    super().test_inference_batch_single_identical(expected_max_diff=5E-3       )





  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )




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

  '''simple docstring'''





  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()
  def   snake_case_     (       self				,	a_=0       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		torch.manual_seed(a_       )
    UpperCAmelCase  							=		{
        'prompt': 'A field of roses',
        'generator': generator,
        'num_inference_steps': 3,
        'guidance_scale': 7.5,
        'output_type': 'numpy',
    }
    return inputs
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		'CompVis/stable-diffusion-v1-4'
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline.from_pretrained(a_				,	safety_checker=a_       )
    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )
    pipe.enable_attention_slicing()

    UpperCAmelCase  							=		self.get_inputs()
    UpperCAmelCase  							=		pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    UpperCAmelCase  							=		np.array(
        [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]       )

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

    # make sure image changes after editing
    pipe.edit_model('A pack of roses'				,	'A pack of blue roses'       )

    UpperCAmelCase  							=		pipe(**a_       ).images
    UpperCAmelCase  							=		image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(expected_slice - image_slice       ).max() > 1E-1





  def   snake_case_     (       self       )							->			List[str]:


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

    UpperCAmelCase  							=		'CompVis/stable-diffusion-v1-4'
    UpperCAmelCase  							=		DDIMScheduler.from_pretrained(a_				,	subfolder='scheduler'       )
    UpperCAmelCase  							=		StableDiffusionModelEditingPipeline.from_pretrained(
        a_				,	scheduler=a_				,	safety_checker=a_       )
    UpperCAmelCase  							=		pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )
    pipe.enable_attention_slicing(1       )
    pipe.enable_sequential_cpu_offload()

    UpperCAmelCase  							=		self.get_inputs()
    UpperCAmelCase  							=		pipe(**a_       )

    UpperCAmelCase  							=		torch.cuda.max_memory_allocated()
    # make sure that less than 4.4 GB is allocated
    assert mem_bytes < 4.4 * 1_0**9





'''simple docstring'''




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				)
  parser.add_argument('--prompt'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''  )
  parser.add_argument('--length'    ,				type=SCREAMING_SNAKE_CASE    ,				default=20  )
  parser.add_argument('--stop_token'    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				help='Token at which text generation is stopped'  )
  parser.add_argument(
      '--temperature'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='temperature of 1.0 has no effect, lower tend toward greedy sampling'    ,				)
  parser.add_argument(
      '--repetition_penalty'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='primarily useful for CTRL model; in that case, use 1.2'  )
  parser.add_argument('--k'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0  )
  parser.add_argument('--penalty_alpha'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.0  )
  parser.add_argument('--p'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.9  )

  parser.add_argument('--prefix'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Text added prior to input.'  )
  parser.add_argument('--padding_text'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Deprecated, the use of `--prefix` is preferred.'  )
  parser.add_argument('--xlm_language'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Optional language when used with the XLM model.'  )

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument('--no_cuda'    ,				action='store_true'    ,				help='Avoid using CUDA when available'  )
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''  )

  set_seed(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path  )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device  )

  if args.fpaa:
    model.half()

  logger.info(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

    # Remove all text after the stop token
    UpperCAmelCase  							=		text[: text.find(args.stop_token  ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    UpperCAmelCase  							=		(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE  )  ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


