





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class 					lowerCAmelCase							(   lowercase_					):







  def __init__(			self  :Dict    ,							_lowercase  :AutoencoderKL    ,							_lowercase  :CLIPTextModel    ,							_lowercase  :CLIPTokenizer    ,							_lowercase  :UNetaDConditionModel    ,							_lowercase  :DDIMScheduler    ,							_lowercase  :float = 0.93    ,							_lowercase  :bool = True    ,							):






     '''simple docstring'''





     lowercase__    =  mixing_coeff
     lowercase__    =  leapfrog_steps

     super().__init__()
     self.register_modules(
         vae=_lowercase    ,							text_encoder=_lowercase    ,							tokenizer=_lowercase    ,							unet=_lowercase    ,							scheduler=_lowercase    ,							)

     lowercase__    =  2 ** (len(self.vae.config.block_out_channels					) - 1)
     lowercase__    =  VaeImageProcessor(vae_scale_factor=self.vae_scale_factor					)







  def   UpperCAmelCase     (			self  :Optional[Any]    ,							_lowercase  :str    ,							_lowercase  :Optional[str] = None    ,							_lowercase  :bool = False					):






     '''simple docstring'''





     lowercase__    =  self.tokenizer(
         _lowercase    ,							padding="max_length"    ,							max_length=self.tokenizer.model_max_length    ,							truncation=_lowercase    ,							return_tensors="pt"    ,							)

     lowercase__    =  self.text_encoder(text_inputs.input_ids.to(self.device					)					).last_hidden_state

     lowercase__    =  prompt_embeds.to(dtype=self.text_encoder.dtype    ,							device=self.device					)

     if do_classifier_free_guidance:
        lowercase__    =  "" if negative_prompt is None else negative_prompt

        lowercase__    =  self.tokenizer(
            _lowercase    ,							padding="max_length"    ,							max_length=self.tokenizer.model_max_length    ,							truncation=_lowercase    ,							return_tensors="pt"    ,							)

        lowercase__    =  self.text_encoder(uncond_input.input_ids.to(self.device					)					).last_hidden_state

        lowercase__    =  torch.cat([negative_prompt_embeds, prompt_embeds]					)

     return prompt_embeds







  def   UpperCAmelCase     (			self  :str    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor					):






     '''simple docstring'''





     lowercase__    =  self.mixing_coeff * x + (1 - self.mixing_coeff) * y
     lowercase__    =  self.mixing_coeff * y + (1 - self.mixing_coeff) * x

     return [x, y]







  def   UpperCAmelCase     (			self  :Dict    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor					):






     '''simple docstring'''





     lowercase__    =  (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
     lowercase__    =  (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

     return [x, y]







  def   UpperCAmelCase     (			self  :Tuple    ,							_lowercase  :torch.Tensor					):






     '''simple docstring'''





     lowercase__    =  int(_lowercase					)

     lowercase__    =  self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

     return alpha_prod, 1 - alpha_prod







  def   UpperCAmelCase     (			self  :Union[str, Any]    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor    ,							):






     '''simple docstring'''





     lowercase__    =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     lowercase__   ,							lowercase__    =  self._get_alpha_and_beta(_lowercase					)
     lowercase__   ,							lowercase__    =  self._get_alpha_and_beta(_lowercase					)

     lowercase__    =  (alpha_prod_t_prev / alpha_prod_t) ** 0.5
     lowercase__    =  -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

     lowercase__    =  (base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype					)







  def   UpperCAmelCase     (			self  :Optional[int]    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor    ,							):






     '''simple docstring'''





     lowercase__    =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     lowercase__   ,							lowercase__    =  self._get_alpha_and_beta(_lowercase					)
     lowercase__   ,							lowercase__    =  self._get_alpha_and_beta(_lowercase					)

     lowercase__    =  (alpha_prod_t_prev / alpha_prod_t) ** 0.5
     lowercase__    =  -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
     lowercase__    =  a_t * base + b_t * model_output

     return model_input, next_model_input.to(base.dtype					)







  @torch.no_grad()
  def   UpperCAmelCase     (			self  :List[str]    ,							_lowercase  :torch.Tensor					):






     '''simple docstring'''





     lowercase__    =  1 / self.vae.config.scaling_factor * latents
     lowercase__    =  self.vae.decode(_lowercase					).sample
     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     return image







  @torch.no_grad()
  def   UpperCAmelCase     (			self  :List[str]    ,							_lowercase  :Image.Image    ,							_lowercase  :torch.Tensor    ,							_lowercase  :torch.Tensor    ,							_lowercase  :float    ,							_lowercase  :Optional[torch.Generator] = None    ,							):






     '''simple docstring'''





     lowercase__    =  guidance_scale > 1.0

     lowercase__    =  image.to(device=self.device    ,							dtype=text_embeds.dtype					)
     lowercase__    =  self.vae.encode(_lowercase					).latent_dist.sample(_lowercase					)

     lowercase__    =  self.vae.config.scaling_factor * latent

     lowercase__    =  [latent.clone(), latent.clone()]

     for i, t in tqdm(enumerate(_lowercase					)    ,							total=len(_lowercase					)					):
        lowercase__    =  self.noise_mixing_layer(x=coupled_latents[0]    ,							y=coupled_latents[1]					)

        # j - model_input index, k - base index
        for j in range(2					):
           lowercase__    =  j ^ 1

           if self.leapfrog_steps:
              if i % 2 == 0:
                 lowercase__   ,							lowercase__    =  j, k

           lowercase__    =  coupled_latents[j]
           lowercase__    =  coupled_latents[k]

           lowercase__    =  torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

           lowercase__    =  self.unet(_lowercase    ,							_lowercase    ,							encoder_hidden_states=_lowercase					).sample

           if do_classifier_free_guidance:
              lowercase__   ,							lowercase__    =  noise_pred.chunk(2					)
              lowercase__    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           lowercase__   ,							lowercase__    =  self.noise_step(
               base=_lowercase    ,							model_input=_lowercase    ,							model_output=_lowercase    ,							timestep=_lowercase    ,							)

           lowercase__    =  model_input

     return coupled_latents







  @torch.no_grad()
  def __call__(			self  :Dict    ,							_lowercase  :str    ,							_lowercase  :str    ,							_lowercase  :Image.Image    ,							_lowercase  :float = 3.0    ,							_lowercase  :int = 50    ,							_lowercase  :float = 0.8    ,							_lowercase  :Optional[str] = None    ,							_lowercase  :Optional[torch.Generator] = None    ,							_lowercase  :Optional[str] = "pil"    ,							):






     '''simple docstring'''





     lowercase__    =  guidance_scale > 1.0

     lowercase__    =  self.image_processor.preprocess(_lowercase					)

     lowercase__    =  self._encode_prompt(_lowercase    ,							_lowercase    ,							_lowercase					)
     lowercase__    =  self._encode_prompt(_lowercase    ,							_lowercase    ,							_lowercase					)

     self.scheduler.set_timesteps(_lowercase    ,							self.device					)

     lowercase__    =  num_inference_steps - int(num_inference_steps * strength					)
     lowercase__    =  self.scheduler.timesteps[t_limit:]
     lowercase__    =  fwd_timesteps.flip(0					)

     lowercase__    =  self.prepare_latents(_lowercase    ,							_lowercase    ,							_lowercase    ,							_lowercase    ,							_lowercase					)

     for i, t in tqdm(enumerate(_lowercase					)    ,							total=len(_lowercase					)					):
        # j - model_input index, k - base index
        for k in range(2					):
           lowercase__    =  k ^ 1

           if self.leapfrog_steps:
              if i % 2 == 1:
                 lowercase__   ,							lowercase__    =  j, k

           lowercase__    =  coupled_latents[j]
           lowercase__    =  coupled_latents[k]

           lowercase__    =  torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

           lowercase__    =  self.unet(_lowercase    ,							_lowercase    ,							encoder_hidden_states=_lowercase					).sample

           if do_classifier_free_guidance:
              lowercase__   ,							lowercase__    =  noise_pred.chunk(2					)
              lowercase__    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           lowercase__   ,							lowercase__    =  self.denoise_step(
               base=_lowercase    ,							model_input=_lowercase    ,							model_output=_lowercase    ,							timestep=_lowercase    ,							)

           lowercase__    =  model_input

        lowercase__    =  self.denoise_mixing_layer(x=coupled_latents[0]    ,							y=coupled_latents[1]					)

     # either one is fine
     lowercase__    =  coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
        lowercase__    =  (
            f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
            "`pil`, `np`, `pt`, `latent`"
        )
        deprecate("Unsupported output_type"    ,							"1.0.0"    ,							_lowercase    ,							standard_warn=_lowercase					)
        lowercase__    =  "np"

     if output_type == "latent":
        lowercase__    =  final_latent
     else:
        lowercase__    =  self.decode_latents(_lowercase					)
        lowercase__    =  self.image_processor.postprocess(_lowercase    ,							output_type=_lowercase					)

     return image













import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
  from tests.trainer.test_trainer import (  # noqa
      RegressionModelConfig,
      RegressionPreTrainedModel,
      get_regression_trainer,
  )


set_seed(42)

_snake_case				= get_tests_dir("""fixtures""")
_snake_case				= os.path.join(dirname(get_tests_dir()))
_snake_case				= dirname(os.path.abspath(__file__))

# default torch.distributed port
_snake_case				= """10999"""

_snake_case				= """t5-small"""

# *** Working Models ***
_snake_case				= """hf-internal-testing/tiny-albert"""
_snake_case				= """sshleifer/bart-tiny-random"""
_snake_case				= """hf-internal-testing/tiny-bert"""
_snake_case				= """hf-internal-testing/tiny-random-bigbird_pegasus"""
_snake_case				= """hf-internal-testing/tiny-random-big_bird"""
_snake_case				= """hf-internal-testing/tiny-random-blenderbot"""
_snake_case				= """bigscience/bigscience-small-testing"""
_snake_case				= """hf-internal-testing/tiny-random-deberta"""
_snake_case				= """hf-internal-testing/tiny-random-deberta-v2"""
_snake_case				= """sshleifer/tiny-distilbert-base-cased"""
_snake_case				= """hf-internal-testing/tiny-electra"""
_snake_case				= """hf-internal-testing/tiny-random-flaubert"""
_snake_case				= """stas/tiny-wmt19-en-de"""
_snake_case				= """hf-internal-testing/tiny-random-funnel"""
_snake_case				= """sshleifer/tiny-gpt2"""
_snake_case				= """hf-internal-testing/tiny-random-gptj"""
_snake_case				= """hf-internal-testing/tiny-random-gpt_neo"""
_snake_case				= """hf-internal-testing/tiny-layoutlm"""
_snake_case				= """hf-internal-testing/tiny-random-led"""
_snake_case				= """hf-internal-testing/tiny-random-longformer"""
_snake_case				= """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
_snake_case				= """sshleifer/tiny-marian-en-de"""
_snake_case				= """sshleifer/tiny-mbart"""
_snake_case				= """hf-internal-testing/tiny-random-mobilebert"""
_snake_case				= """hf-internal-testing/tiny-random-mpnet"""
_snake_case				= """stas/pegasus-cnn_dailymail-tiny-random"""
_snake_case				= """hf-internal-testing/tiny-random-prophetnet"""
_snake_case				= """sshleifer/tiny-distilroberta-base"""
_snake_case				= """hf-internal-testing/tiny-random-squeezebert"""
_snake_case				= """patrickvonplaten/t5-tiny-random"""
_snake_case				= """hf-internal-testing/tiny-random-t5-v1.1"""
_snake_case				= """hf-internal-testing/tiny-random-vit"""
_snake_case				= """hf-internal-testing/tiny-xlm-roberta"""
_snake_case				= """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_snake_case				= """hf-internal-testing/tiny-random-mt5"""
_snake_case				= """hf-internal-testing/tiny-random-camembert"""
_snake_case				= """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
_snake_case				= """hf-internal-testing/tiny-random-convbert"""
_snake_case				= """hf-internal-testing/tiny-random-layoutlmv2"""
_snake_case				= """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
_snake_case				= """hf-internal-testing/tiny-random-ctrl"""
_snake_case				= """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
_snake_case				= """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
_snake_case				= """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_snake_case				= """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_snake_case				= """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
_snake_case				= """"""
# - "luke" Entities classes - no plan to make such example
_snake_case				= """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
_snake_case				= """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_snake_case				= """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_snake_case				= """hf-internal-testing/tiny-random-tapas"""


def _A   (    __magic_name__=False       ):
   # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
   # - it won't be able to handle that
   # 2. for now testing with just 2 gpus max (since some quality tests may give different
   # results with mode gpus because we use very little data)
   lowercase__    =  min(2  ,					get_gpu_count()       ) if distributed else 1
   lowercase__    =  os.environ.get("DS_TEST_PORT"  ,					__magic_name__       )
   return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def _A   (    ):
   lowercase__    =  f'''{FIXTURE_DIRECTORY}/tests_samples'''
   lowercase__    =  f'''{data_dir_samples}/wmt_en_ro'''
   lowercase__    =  f'''{data_dir_samples}/xsum'''
   lowercase__    =  "\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".split()

   # try to cover as many models as possible once (it's enough to run on one task per model)
   # but need a tiny model for each
   #
   # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
   lowercase__    =  {
       "trans": [
           "bart",
           "fsmt",
           "m2m_100",
           "marian",
           "mbart",
           "t5",
           "t5_v1",
           # "mt5", missing model files
       ],
       "sum": [
           "pegasus",
       ],
       "clm": [
           "big_bird",
           "bigbird_pegasus",
           "blenderbot",
           "bloom",
           "gpt2",
           "gpt_neo",
           "gptj",
           "xlm-roberta",
           "prophetnet",
           # "camembert", missing model files
       ],
       "mlm": [
           "albert",
           "deberta",
           "deberta-v2",
           "distilbert",
           "electra",
           "flaubert",
           "funnel",
           "layoutlm",
           # "reformer", # multiple issues with either mlm/qa/clas
       ],
       "qa": [
           "led",
           "longformer",
           "mobilebert",
           "mpnet",
           "roberta",
           "squeezebert",
           # "convbert", # missing tokenizer files
           # "layoutlmv2", missing model files
       ],
       "clas": [
           "bert",
           "xlnet",
           # "hubert", # missing tokenizer files
           # "ibert", # multiple issues with either mlm/qa/clas
           # "transfo-xl", # tokenizer issues as ctrl
           # "ctrl", # tokenizer issues
           # "openai-gpt", missing model files
           # "tapas", multiple issues
       ],
       "img_clas": [
           "vit",
       ],
   }

   lowercase__    =  f'''{ROOT_DIRECTORY}/examples/pytorch'''

   lowercase__    =  {
       "trans": f'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
       "sum": f'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
       "clm": f'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
       "mlm": f'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
       "qa": f'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
       "clas": f'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
       "img_clas": f'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
   }

   lowercase__    =  get_launcher(distributed=__magic_name__       )

   lowercase__    =  {}
   for task, args in tasks.items():
      lowercase__    =  args.split()
      for model in tasksamodels[task]:
         lowercase__    =  globals()[f'''{model.upper().replace('-'  ,					'_'       )}_TINY''']
         lowercase__    =  f'''--model_name_or_path {model_name}'''.split()
         lowercase__    =  launcher + args + args_model + args_main

         # # generation special case
         # if task == "gen":
         #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
         #     args_model += f"--model_type {model}".split()
         #     cmds[f"{task}_{model}"] = launcher + args + args_model
         # else:

   return cmds


_snake_case				= make_task_cmds()

_snake_case				= """zero2"""
_snake_case				= """zero3"""

_snake_case				= [ZEROa, ZEROa]


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   # customize the test name generator function as we want both params to appear in the sub-test
   # name, as by default it shows only the first param
   lowercase__    =  parameterized.to_safe_name("_".join(str(__magic_name__       ) for x in param.args       )       )
   return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_snake_case				= list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 					lowerCAmelCase							(   lowercase_					):







  def   UpperCAmelCase     (			self  :Tuple    ,							_lowercase  :int    ,							_lowercase  :Any					):






     '''simple docstring'''





     if task not in task_cmds:
        raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''					)

     lowercase__    =  task_cmds[task]
     lowercase__    =  f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

     lowercase__    =  self.get_auto_remove_tmp_dir()
     lowercase__    =  f'''--output_dir {output_dir}'''.split()

     cmd += args_ds + args_out

     return cmd, output_dir







  @parameterized.expand(_lowercase    ,							name_func=_lowercase					)
  def   UpperCAmelCase     (			self  :str    ,							_lowercase  :Dict    ,							_lowercase  :Tuple					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  self.get_task_cmd(_lowercase    ,							_lowercase					)

     # 1. generate the checkpoint
     cmd += "--save_steps 1".split()
     # keep for quick debug
     # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
     execute_subprocess_async(_lowercase    ,							env=self.get_env()					)

     # 2. test that the fp32 weights get reconsolidated
     lowercase__    =  f'''{output_dir}/checkpoint-1'''
     lowercase__    =  f'''{chkpt_dir}/out.bin'''
     lowercase__    =  f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
     # keep for quick debug
     # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
     subprocess.check_call(_lowercase    ,							shell=_lowercase					)
     assert os.path.exists(_lowercase					), f'''{recovered_model_path} was not found'''

     # possibly could also test that the resulting saved model is usable but given that we use
     # random models we won't know if it's any good






