





"""simple docstring"""
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					:		List[str]							=			get_tests_dir('fixtures')
_snake_case					:		List[str]							=			os.path.join(dirname(get_tests_dir()))
_snake_case					:		Dict							=			dirname(os.path.abspath(__file__))

# default torch.distributed port
_snake_case					:		Union[str, Any]							=			'10999'

_snake_case					:		List[str]							=			't5-small'

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_snake_case					:		Tuple							=			'hf-internal-testing/tiny-random-mt5'
_snake_case					:		List[str]							=			'hf-internal-testing/tiny-random-camembert'
_snake_case					:		List[str]							=			'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
_snake_case					:		Optional[Any]							=			'hf-internal-testing/tiny-random-convbert'
_snake_case					:		List[Any]							=			'hf-internal-testing/tiny-random-layoutlmv2'
_snake_case					:		Any							=			'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
_snake_case					:		Union[str, Any]							=			'hf-internal-testing/tiny-random-ctrl'
_snake_case					:		str							=			'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
_snake_case					:		List[Any]							=			'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
_snake_case					:		str							=			'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					:		str							=			'hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_snake_case					:		List[str]							=			'hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
_snake_case					:		Tuple							=			''
# - "luke" Entities classes - no plan to make such example
_snake_case					:		Optional[Any]							=			'hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
_snake_case					:		List[Any]							=			'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					:		Tuple							=			'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					:		Any							=			'hf-internal-testing/tiny-random-tapas'






def  A__    (      UpperCamelCase=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)
							A		=  min(2 ,  get_gpu_count()	) if distributed else 1
							A		=  os.environ.get("DS_TEST_PORT" ,  UpperCamelCase	)
							return F"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()






def  A__    (      ):
							A		=  F"{FIXTURE_DIRECTORY}/tests_samples"
							A		=  F"{data_dir_samples}/wmt_en_ro"
							A		=  F"{data_dir_samples}/xsum"
							A		=  "\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.
							A		=  {
							    "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",
							    ],
							}

							A		=  F"{ROOT_DIRECTORY}/examples/pytorch"

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

							A		=  get_launcher(distributed=UpperCamelCase	)

							A		=  {}
							for task, args in tasks.items():
														A		=  args.split()
														for model in tasksamodels[task]:
																					A		=  globals()[F"{model.upper().replace('-' ,  '_'	)}_TINY"]
																					A		=  F"--model_name_or_path {model_name}".split()
																					A		=  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					:		Any							=			make_task_cmds()

_snake_case					:		Optional[Any]							=			'zero2'
_snake_case					:		int							=			'zero3'

_snake_case					:		List[Any]							=			[ZEROa, ZEROa]






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							# 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
							A		=  parameterized.to_safe_name("_".join(str(UpperCamelCase	) for x in param.args	)	)
							return F"{func.__name__}_{param_based_name}"


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






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


					def      lowerCamelCase	( self      :int					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :List[Any]		):
												# return a ready to run train cmd
												if task not in task_cmds:
																			raise ValueError(f"don't know of task {task}, have {task_cmds.keys()}"		)

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

												A		=  self.get_auto_remove_tmp_dir()
												A		=  f"--output_dir {output_dir}".split()

												cmd += args_ds + args_out

												return cmd, output_dir


					@parameterized.expand(__UpperCamelCase					,	name_func=__UpperCamelCase		)
					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):
												# testing the ability to do a run followed by recovery of full fp32 weights

												A,     A		=  self.get_task_cmd(__UpperCamelCase					,	__UpperCamelCase		)

												# 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(__UpperCamelCase					,	env=self.get_env()		)

												# 2. test that the fp32 weights get reconsolidated
												A		=  f"{output_dir}/checkpoint-1"
												A		=  f"{chkpt_dir}/out.bin"
												A		=  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(__UpperCamelCase					,	shell=__UpperCamelCase		)
												assert os.path.exists(__UpperCamelCase		), 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









"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_snake_case					:		str							=			logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							42







class  _UpperCAmelCase       (     lowercase_					,							lowercase_			):
					UpperCamelCase								=							1


					@register_to_config
					def __init__( self      :Tuple					,	__UpperCamelCase      :int = 40					,	__UpperCamelCase      :float = 0.002					,	__UpperCamelCase      :float = 80.0					,	__UpperCamelCase      :float = 0.5					,	__UpperCamelCase      :float = 1.0					,	__UpperCamelCase      :float = 7.0					,	__UpperCamelCase      :bool = True					,	):
												# standard deviation of the initial noise distribution
												A		=  sigma_max

												A		=  np.linspace(0					,	1					,	__UpperCamelCase		)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												# setable values
												A		=  None
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  False
												A		=  False


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Dict=None		):
												if schedule_timesteps is None:
																			A		=  self.timesteps

												A		=  (schedule_timesteps == timestep).nonzero()
												return indices.item()


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]		):
												# Get sigma corresponding to timestep
												if isinstance(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)
												A		=  self.index_for_timestep(__UpperCamelCase		)
												A		=  self.sigmas[step_idx]

												A		=  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

												A		=  True
												return sample


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :Union[float, np.ndarray]		):
												if not isinstance(__UpperCamelCase					,	np.ndarray		):
																			A		=  np.array(__UpperCamelCase					,	dtype=np.floataa		)

												A		=  10_00 * 0.25 * np.log(sigmas + 1e-44		)

												return timesteps


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Union[str, torch.device] = None					,	__UpperCamelCase      :Optional[List[int]] = None					,	):
												if num_inference_steps is None and timesteps is None:
																			raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."		)

												if num_inference_steps is not None and timesteps is not None:
																			raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."		)

												# Follow DDPMScheduler custom timesteps logic
												if timesteps is not None:
																			for i in range(1					,	len(__UpperCamelCase		)		):
																										if timesteps[i] >= timesteps[i - 1]:
																																	raise ValueError("`timesteps` must be in descending order."		)

																			if timesteps[0] >= self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`timesteps` must start before `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps}."		)

																			A		=  np.array(__UpperCamelCase					,	dtype=np.intaa		)
																			A		=  True
												else:
																			if num_inference_steps > self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																										    f" maximal {self.config.num_train_timesteps} timesteps."		)

																			A		=  num_inference_steps

																			A		=  self.config.num_train_timesteps // self.num_inference_steps
																			A		=  (np.arange(0					,	__UpperCamelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
																			A		=  False

												# Map timesteps to Karras sigmas directly for multistep sampling
												# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
												A		=  self.config.num_train_timesteps
												A		=  timesteps[::-1].copy()
												A		=  ramp / (num_train_timesteps - 1)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												A		=  np.concatenate([sigmas, [self.sigma_min]]		).astype(np.floataa		)
												A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)

												if str(__UpperCamelCase		).startswith("mps"		):
																			# mps does not support float64
																			A		=  torch.from_numpy(__UpperCamelCase		).to(__UpperCamelCase					,	dtype=torch.floataa		)
												else:
																			A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.config.rho
												A		=  sigma_min ** (1 / rho)
												A		=  sigma_max ** (1 / rho)
												A		=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Dict		):
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / (sigma**2 + sigma_data**2)
												A		=  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Tuple		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
												A		=  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :bool = True					,	):

												if (
												    isinstance(__UpperCamelCase					,	__UpperCamelCase		)
												    or isinstance(__UpperCamelCase					,	torch.IntTensor		)
												    or isinstance(__UpperCamelCase					,	torch.LongTensor		)
												):
																			raise ValueError(
																			    (
																			        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																			        f" `{self.__class__}.step()` is not supported. Make sure to pass"
																			        " one of the `scheduler.timesteps` as a timestep."
																			    )					,	)

												if not self.is_scale_input_called:
																			logger.warning(
																			    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
																			    "See `StableDiffusionPipeline` for a usage example."		)

												if isinstance(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)

												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.index_for_timestep(__UpperCamelCase		)

												# sigma_next corresponds to next_t in original implementation
												A		=  self.sigmas[step_index]
												if step_index + 1 < self.config.num_train_timesteps:
																			A		=  self.sigmas[step_index + 1]
												else:
																			# Set sigma_next to sigma_min
																			A		=  self.sigmas[-1]

												# Get scalings for boundary conditions
												A,     A		=  self.get_scalings_for_boundary_condition(__UpperCamelCase		)

												# 1. Denoise model output using boundary conditions
												A		=  c_out * model_output + c_skip * sample
												if self.config.clip_denoised:
																			A		=  denoised.clamp(-1					,	1		)

												# 2. Sample z ~ N(0, s_noise^2 * I)
												# Noise is not used for onestep sampling.
												if len(self.timesteps		) > 1:
																			A		=  randn_tensor(
																			    model_output.shape					,	dtype=model_output.dtype					,	device=model_output.device					,	generator=__UpperCamelCase		)
												else:
																			A		=  torch.zeros_like(__UpperCamelCase		)
												A		=  noise * self.config.s_noise

												A		=  sigma_next.clamp(min=__UpperCamelCase					,	max=__UpperCamelCase		)

												# 3. Return noisy sample
												# tau = sigma_hat, eps = sigma_min
												A		=  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

												if not return_dict:
																			return (prev_sample,)

												return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												A		=  self.sigmas.to(device=original_samples.device					,	dtype=original_samples.dtype		)
												if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase		):
																			# mps does not support float64
																			A		=  self.timesteps.to(original_samples.device					,	dtype=torch.floataa		)
																			A		=  timesteps.to(original_samples.device					,	dtype=torch.floataa		)
												else:
																			A		=  self.timesteps.to(original_samples.device		)
																			A		=  timesteps.to(original_samples.device		)

												A		=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

												A		=  sigmas[step_indices].flatten()
												while len(sigma.shape		) < len(original_samples.shape		):
																			A		=  sigma.unsqueeze(-1		)

												A		=  original_samples + noise * sigma
												return noisy_samples


					def __len__( self      :str		):
												return self.config.num_train_timesteps


