




"""simple docstring"""






# 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


lowercase__  		=		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)

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

lowercase__  		=		"""zero2"""
lowercase__  		=		"""zero3"""
lowercase__  		=		[ZEROa, ZEROa]







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				# 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
				_lowerCamelCase  :    Optional[Any]					=					parameterized.to_safe_name('_'.join(str(lowercase__	) for x in param.args	)	)
				return f'''{func.__name__}_{param_based_name}'''


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






@slow
@require_deepspeed
@require_torch_gpu
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@parameterized.expand(lowercase						,   name_func=lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						):
							self.run_and_check(
							    stage=lowercase						,   model=lowercase						,   distributed=lowercase						,   fpaa=lowercase						,   )
			@require_torch_multi_gpu
			@parameterized.expand(lowercase						,   name_func=lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						):
							self.run_and_check(
							    stage=lowercase						,   model=lowercase						,   distributed=lowercase						,   fpaa=lowercase						,   )
			@parameterized.expand(lowercase						,   name_func=lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						):
							self.run_and_check(
							    stage=lowercase						,   model=lowercase						,   distributed=lowercase						,   fpaa=lowercase						,   )
			@require_torch_multi_gpu
			@parameterized.expand(lowercase						,   name_func=lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						):
							self.run_and_check(
							    stage=lowercase						,   model=lowercase						,   distributed=lowercase						,   fpaa=lowercase						,   )
			def       A_					(						self						,   lowercase						):
							# XXX: run_asr is premature and doesn't save any results
							# so all we check for now is that the process didn't fail
							pass
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = 10						,   lowercase = True						,   lowercase = True						,   lowercase = True						,   ):
							_lowerCamelCase  :    str					=					models[model]

							_lowerCamelCase  :    Optional[int]					=					self.run_trainer(
							    stage=lowercase						,   model_name=lowercase						,   eval_steps=lowercase						,   num_train_epochs=1						,   distributed=lowercase						,   fpaa=lowercase						,   )

							self.do_checks(lowercase						)

							return output_dir
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = 10						,   lowercase = 1						,   lowercase = True						,   lowercase = True						,   ):
							_lowerCamelCase  :    Optional[int]					=					self.get_auto_remove_tmp_dir('./xxx'						,   after=lowercase						)
							_lowerCamelCase  :    Optional[int]					=					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(lowercase						)}
            --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
							_lowerCamelCase  :    str					=					F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
							_lowerCamelCase  :    List[str]					=					[F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
							_lowerCamelCase  :    int					=					self.get_launcher(lowercase						)

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

							return output_dir






			def       A_					(						self						,   lowercase=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)
							_lowerCamelCase  :    Union[str, Any]					=					min(2						,   get_gpu_count()						) if distributed else 1
							return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available






@dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    42


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipeline_text_to_video_synth import TextToVideoSDPipeline
			from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
			from .pipeline_text_to_video_zero import TextToVideoZeroPipeline