





# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


__A				:    Optional[Any]       =   Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

__A				:    int       =   {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

__A				:    Tuple       =   "zero2"
__A				:    Tuple       =   "zero3"
__A				:    List[Any]       =   [ZEROa, ZEROa]





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				List[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				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
__A				:    Optional[Any]       =   list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 	A_       (a_			):
	@parameterized.expand(_A							,					name_func=_A		)
	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				self.run_and_check(
				    stage=_A							,					model=_A							,					distributed=_A							,					fpaa=_A							,					)
	@require_torch_multi_gpu
	@parameterized.expand(_A							,					name_func=_A		)
	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				self.run_and_check(
				    stage=_A							,					model=_A							,					distributed=_A							,					fpaa=_A							,					)
	@parameterized.expand(_A							,					name_func=_A		)
	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				self.run_and_check(
				    stage=_A							,					model=_A							,					distributed=_A							,					fpaa=_A							,					)
	@require_torch_multi_gpu
	@parameterized.expand(_A							,					name_func=_A		)
	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				self.run_and_check(
				    stage=_A							,					model=_A							,					distributed=_A							,					fpaa=_A							,					)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self							,					_A							,					_A							,					_A = 1_0							,					_A = True							,					_A = True							,					_A = True							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				models[model]

				UpperCAmelCase   		=				self.run_trainer(
				    stage=_A							,					model_name=_A							,					eval_steps=_A							,					num_train_epochs=1							,					distributed=_A							,					fpaa=_A							,					)

				self.do_checks(_A		)

				return output_dir
	def      _lowercase				(    self							,					_A							,					_A							,					_A = 1_0							,					_A = 1							,					_A = True							,					_A = True							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_auto_remove_tmp_dir('''./xxx'''							,					after=_A		)
				UpperCAmelCase   		=				F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_A		)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

				if fpaa:
							args.extend(['''--fp16''']		)

				# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
				# hence the separate config files
				UpperCAmelCase   		=				F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
				UpperCAmelCase   		=				[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
				UpperCAmelCase   		=				self.get_launcher(_A		)

				UpperCAmelCase   		=				launcher + script + args + ds_args
				# keep for quick debug
				# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
				execute_subprocess_async(_A							,					env=self.get_env()		)

				return output_dir







	def      _lowercase				(    self							,					_A=False		):






				'''simple docstring'''







				UpperCAmelCase   		=				min(2							,					get_gpu_count()		) if distributed else 1
				return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()










from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=_A							,					scheduler=_A		)







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



