
"""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


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

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    parameterized.to_safe_name('''_'''.join(str(__snake_case      ) for x in param.args      )      )
					return F'''{func.__name__}_{param_based_name}'''


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






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



		@parameterized.expand(__a							,						name_func=__a)
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			List[str]:






							'''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 		UpperCAmelCase				(   self							,						__a							,						__a)			->			int:






							'''simple docstring'''
							self.run_and_check(
							    stage=__a							,						model=__a							,						distributed=__a							,						fpaa=__a							,						)



		@parameterized.expand(__a							,						name_func=__a)
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			List[str]:






							'''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 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							self.run_and_check(
							    stage=__a							,						model=__a							,						distributed=__a							,						fpaa=__a							,						)



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							# 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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = 10							,						__a = True							,						__a = True							,						__a = True							,						)			->			Dict:






							'''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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = 10							,						__a = 1							,						__a = True							,						__a = True							,						)			->			int:






							'''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 		UpperCAmelCase				(   self							,						__a=False)			->			Tuple:






							'''simple docstring'''
							# 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)
							_UpperCamelCase									=    min(2							,						get_gpu_count()) if distributed else 1
							return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()













'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






