



"""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       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Tuple					):
							# 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		:		str									=       parameterized.to_safe_name("""_""".join(str(_lowerCamelCase					) 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 SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

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



						@require_torch_multi_gpu
						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

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



						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

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



						@require_torch_multi_gpu
						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													pass



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase = 10						,     _lowercase = True						,     _lowercase = True						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		Dict									=       models[model]

													__a		:		List[str]									=       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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase = 10						,     _lowercase = 1						,     _lowercase = True						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		int									=       self.get_auto_remove_tmp_dir("""./xxx"""						,     after=_lowercase							)
													__a		:		List[Any]									=       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
													__a		:		List[Any]									=       F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
													__a		:		Any									=       [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
													__a		:		Dict									=       self.get_launcher(_lowercase							)

													__a		:		Tuple									=       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     lowerCAmelCase__(self						,     _lowercase=False							):





													'''simple docstring'''

													__a		:		Tuple									=       min(2						,     get_gpu_count()							) if distributed else 1
													return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()






"""simple docstring"""

from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=0							):





													'''simple docstring'''

													__a		:		Any									=       1.0 if scale is None else scale
													__a		:		str									=       0.0 if loc is None else loc

													super().__init__(_lowercase						,     [AffineTransform(loc=self.loc						,     scale=self.scale						,     event_dim=_lowercase							)]							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.base_dist.mean * self.scale + self.loc



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.base_dist.variance * self.scale**2





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.variance.sqrt()







class SCREAMING_SNAKE_CASE__					(		nn.Module      ):



						def __init__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     **_lowercase							):





													'''simple docstring'''

													super().__init__(**_lowercase							)
													__a		:		str									=       args_dim
													__a		:		List[Any]									=       nn.ModuleList([nn.Linear(_lowercase						,     _lowercase							) for dim in args_dim.values()]							)
													__a		:		Dict									=       domain_map





						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		List[Any]									=       [proj(_lowercase							) for proj in self.proj]

													return self.domain_map(*_lowercase							)







class SCREAMING_SNAKE_CASE__					(		nn.Module      ):



						def __init__(self						,     _lowercase							):





													'''simple docstring'''

													super().__init__()
													__a		:		Optional[int]									=       function





						def     lowerCAmelCase__(self						,     _lowercase						,     *_lowercase							):





													'''simple docstring'''

													return self.function(_lowercase						,     *_lowercase							)







class SCREAMING_SNAKE_CASE__					:
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42



						def __init__(self						,     _lowercase = 1							):





													'''simple docstring'''

													__a		:		Optional[int]									=       dim
													__a		:		str									=       {k: dim * self.args_dim[k] for k in self.args_dim}



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													if self.dim == 1:
																				return self.distribution_class(*_lowercase							)
													else:
																				return Independent(self.distribution_class(*_lowercase							)						,     1							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None						,     ):





													'''simple docstring'''

													__a		:		Tuple									=       self._base_distribution(_lowercase							)
													if loc is None and scale is None:
																				return distr
													else:
																				return AffineTransformed(_lowercase						,     loc=_lowercase						,     scale=_lowercase						,     event_dim=self.event_dim							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return () if self.dim == 1 else (self.dim,)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return len(self.event_shape							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 0.0



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													return ParameterProjection(
													    in_features=_lowercase						,     args_dim=self.args_dim						,     domain_map=LambdaLayer(self.domain_map							)						,     )



						def     lowerCAmelCase__(self						,     *_lowercase							):





													'''simple docstring'''

													raise NotImplementedError()





						@staticmethod
						def     lowerCAmelCase__(_lowercase							):





													'''simple docstring'''

													return (x + torch.sqrt(torch.square(_lowercase							) + 4.0							)) / 2.0







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"df": 1, "loc": 1, "scale": 1}
						_lowerCAmelCase							=					StudentT



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		int									=       cls.squareplus(_lowercase							).clamp_min(torch.finfo(scale.dtype							).eps							)
													__a		:		Optional[Any]									=       2.0 + cls.squareplus(_lowercase							)
													return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"loc": 1, "scale": 1}
						_lowerCAmelCase							=					Normal



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		str									=       cls.squareplus(_lowercase							).clamp_min(torch.finfo(scale.dtype							).eps							)
													return loc.squeeze(-1							), scale.squeeze(-1							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"total_count": 1, "logits": 1}
						_lowerCAmelCase							=					NegativeBinomial



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       cls.squareplus(_lowercase							)
													return total_count.squeeze(-1							), logits.squeeze(-1							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a							,     __a		:		Optional[Any]									=       distr_args
													if self.dim == 1:
																				return self.distribution_class(total_count=_lowercase						,     logits=_lowercase							)
													else:
																				return Independent(self.distribution_class(total_count=_lowercase						,     logits=_lowercase							)						,     1							)





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None							):





													'''simple docstring'''

													__a							,     __a		:		List[Any]									=       distr_args

													if scale is not None:
																				# See scaling property of Gamma.
																				logits += scale.log()

													return self._base_distribution((total_count, logits)							)

