



"""simple docstring"""


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__A   :							Optional[int]										=   logging.get_logger("transformers.models.speecht5")

__A   :							Optional[Any]										=   {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__A   :							List[Any]										=   {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__A   :							List[str]										=   {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__A   :							int										=   {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__A   :							Optional[int]										=   {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__A   :							Optional[int]										=   {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__A   :							Optional[Any]										=   {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__A   :							Dict										=   {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__A   :							List[str]										=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__A   :							Dict										=   {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A   :							Optional[int]										=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A   :							List[Any]										=   []
__A   :							Tuple										=   [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
__A   :							str										=   IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
__A   :							Dict										=   IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
__A   :							Union[str, Any]										=   IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				for attribute in key.split('''.'''				):
								_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				if weight_type is not None:
								_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).shape
				else:
								_UpperCAmelCase					=    hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
								    f' {value.shape} for {full_name}'				)

				if weight_type == "weight":
								_UpperCAmelCase					=    value
				elif weight_type == "weight_g":
								_UpperCAmelCase					=    value
				elif weight_type == "weight_v":
								_UpperCAmelCase					=    value
				elif weight_type == "bias":
								_UpperCAmelCase					=    value
				elif weight_type == "running_mean":
								_UpperCAmelCase					=    value
				elif weight_type == "running_var":
								_UpperCAmelCase					=    value
				elif weight_type == "num_batches_tracked":
								_UpperCAmelCase					=    value
				else:
								_UpperCAmelCase					=    value

				logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				for key in ignore_keys:
								if key.endswith('''.*'''				):
												if name.startswith(key[:-1]				):
																return True
								elif ".*." in key:
												_UpperCAmelCase		,						_UpperCAmelCase					=    key.split('''.*.'''				)
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    []

				if task == "s2t":
								_UpperCAmelCase					=    hf_model.speechta.encoder.prenet.feature_encoder
								_UpperCAmelCase					=    MAPPING_S2T
								_UpperCAmelCase					=    IGNORE_KEYS_S2T
				elif task == "t2s":
								_UpperCAmelCase					=    None
								_UpperCAmelCase					=    MAPPING_T2S
								_UpperCAmelCase					=    IGNORE_KEYS_T2S
				elif task == "s2s":
								_UpperCAmelCase					=    hf_model.speechta.encoder.prenet.feature_encoder
								_UpperCAmelCase					=    MAPPING_S2S
								_UpperCAmelCase					=    IGNORE_KEYS_S2S
				else:
								raise ValueError(f'Unsupported task: {task}'				)

				for name, value in fairseq_dict.items():
								if should_ignore(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												logger.info(f'{name} was ignored'				)
												continue

								_UpperCAmelCase					=    False
								if "conv_layers" in name:
												load_conv_layer(
												    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     hf_model.config.feat_extract_norm == '''group'''					,     )
												_UpperCAmelCase					=    True
								else:
												for key, mapped_key in MAPPING.items():
																# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																if "*" in key:
																				_UpperCAmelCase		,						_UpperCAmelCase					=    key.split('''.*.'''				)
																				if prefix in name and suffix in name:
																								_UpperCAmelCase					=    suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				_UpperCAmelCase					=    True
																				if "*" in mapped_key:
																								_UpperCAmelCase					=    name.split(_SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
																								_UpperCAmelCase					=    mapped_key.replace('''*'''					,     _SCREAMING_SNAKE_CASE				)
																				if "weight_g" in name:
																								_UpperCAmelCase					=    '''weight_g'''
																				elif "weight_v" in name:
																								_UpperCAmelCase					=    '''weight_v'''
																				elif "bias" in name:
																								_UpperCAmelCase					=    '''bias'''
																				elif "weight" in name:
																								_UpperCAmelCase					=    '''weight'''
																				elif "running_mean" in name:
																								_UpperCAmelCase					=    '''running_mean'''
																				elif "running_var" in name:
																								_UpperCAmelCase					=    '''running_var'''
																				elif "num_batches_tracked" in name:
																								_UpperCAmelCase					=    '''num_batches_tracked'''
																				else:
																								_UpperCAmelCase					=    None
																				set_recursively(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
																continue
								if not is_used:
												unused_weights.append(_SCREAMING_SNAKE_CASE				)

				logger.warning(f'Unused weights: {unused_weights}'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    full_name.split('''conv_layers.'''				)[-1]
				_UpperCAmelCase					=    name.split('''.'''				)
				_UpperCAmelCase					=    int(items[0]				)
				_UpperCAmelCase					=    int(items[1]				)

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    f'{full_name} has size {value.shape}, but'
																    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'				)
												_UpperCAmelCase					=    value
												logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    f'{full_name} has size {value.shape}, but'
																    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'				)
												_UpperCAmelCase					=    value
												logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    f'{full_name} has size {value.shape}, but'
																    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'				)
												_UpperCAmelCase					=    value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    f'{full_name} has size {value.shape}, but'
																    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'				)
												_UpperCAmelCase					=    value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
				else:
								unused_weights.append(_SCREAMING_SNAKE_CASE				)







@torch.no_grad()
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]=None					,     _SCREAMING_SNAKE_CASE   :						Optional[int]=None					,     _SCREAMING_SNAKE_CASE   :						List[str]=None					,     ):






				'''simple docstring'''



				if config_path is not None:
								_UpperCAmelCase					=    SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
				else:
								_UpperCAmelCase					=    SpeechTaConfig()

				if task == "s2t":
								_UpperCAmelCase					=    config.max_text_positions
								_UpperCAmelCase					=    SpeechTaForSpeechToText(_SCREAMING_SNAKE_CASE				)
				elif task == "t2s":
								_UpperCAmelCase					=    1876
								_UpperCAmelCase					=    600
								_UpperCAmelCase					=    config.max_speech_positions
								_UpperCAmelCase					=    SpeechTaForTextToSpeech(_SCREAMING_SNAKE_CASE				)
				elif task == "s2s":
								_UpperCAmelCase					=    1876
								_UpperCAmelCase					=    config.max_speech_positions
								_UpperCAmelCase					=    SpeechTaForSpeechToSpeech(_SCREAMING_SNAKE_CASE				)
				else:
								raise ValueError(f'Unknown task name: {task}'				)

				if vocab_path:
								_UpperCAmelCase					=    SpeechTaTokenizer(_SCREAMING_SNAKE_CASE					,     model_max_length=config.max_text_positions				)

								# Mask token behaves like a normal word, i.e. include the space before it
								_UpperCAmelCase					=    AddedToken('''<mask>'''					,     lstrip=_SCREAMING_SNAKE_CASE					,     rstrip=_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    mask_token
								tokenizer.add_special_tokens({'''mask_token''': mask_token}				)
								tokenizer.add_tokens(['''<ctc_blank>''']				)

				_UpperCAmelCase					=    SpeechTaFeatureExtractor()
				_UpperCAmelCase					=    SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,     feature_extractor=_SCREAMING_SNAKE_CASE				)
				processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    torch.load(_SCREAMING_SNAKE_CASE				)
				recursively_load_weights(fairseq_checkpoint['''model''']					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				model.save_pretrained(_SCREAMING_SNAKE_CASE				)

				if repo_id:
								print('''Pushing to the hub...'''				)
								processor.push_to_hub(_SCREAMING_SNAKE_CASE				)
								model.push_to_hub(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Union[str, Any]										=   argparse.ArgumentParser()
					parser.add_argument(
					    "--task",
					    default="s2t",
					    type=str,
					    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
					)
					parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
					)
					parser.add_argument(
					    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
					)

					__A   :							List[Any]										=   parser.parse_args()
					convert_speechta_checkpoint(
					    args.task,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.vocab_path,
					    args.push_to_hub,
					)








"""simple docstring"""


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__A   :							str										=   [
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    bool				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    list = None   )->int:
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''by_feature'''   )   )
							_UpperCAmelCase					=    os.path.abspath('''examples'''   )
							for item in os.listdir(__UpperCamelCase   ):
											if item not in EXCLUDE_EXAMPLES:
															_UpperCAmelCase					=    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )
															if os.path.isfile(__UpperCamelCase   ) and ".py" in item_path:
																			with self.subTest(
																			    tested_script=__UpperCamelCase				,   feature_script=__UpperCamelCase				,   tested_section='''main()''' if parser_only else '''training_function()'''				,   ):
																							_UpperCAmelCase					=    compare_against_test(
																							    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
																							_UpperCAmelCase					=    '''\n'''.join(__UpperCamelCase   )
																							if special_strings is not None:
																											for string in special_strings:
																															_UpperCAmelCase					=    diff.replace(__UpperCamelCase				,   ''''''   )
																							self.assertEqual(__UpperCamelCase				,   ''''''   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''cv_example.py'''   )   )
							_UpperCAmelCase					=    [
							    ''' ''' * 1_6 + '''{\n\n''',
							    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
							    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
							    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
							    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
							    ''' ''' * 1_6 + '''},\n\n''',
							    ''' ''' * 1_6 + '''step=epoch,\n''',
							    ''' ''' * 1_2,
							    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
							]
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )



@mock.patch.dict(os.environ			,							{"""TESTING_MOCKED_DATALOADERS""": """1"""})
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	False



			@classmethod
			def  lowercase__						(		cls     :    Optional[int]   )->Optional[Any]:
							super().setUpClass()
							_UpperCAmelCase					=    tempfile.mkdtemp()
							_UpperCAmelCase					=    os.path.join(cls._tmpdir				,   '''default_config.yml'''   )

							write_basic_config(save_location=cls.configPath   )
							_UpperCAmelCase					=    ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



			@classmethod
			def  lowercase__						(		cls     :    Dict   )->Any:
							super().tearDownClass()
							shutil.rmtree(cls._tmpdir   )



			def  lowercase__						(		self     :    Optional[int]   )->Any:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
							run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''epoch_0'''   )   )   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''step_2'''   )   )   )



			def  lowercase__						(		self     :    Optional[Any]   )->List[Any]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "epoch_0"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
							self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "step_2"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							if torch.cuda.is_available():
											_UpperCAmelCase					=    torch.cuda.device_count()
							else:
											_UpperCAmelCase					=    1
							if num_processes > 1:
											self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )
							else:
											self.assertIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			@slow
			def  lowercase__						(		self     :    Dict   )->List[Any]:
							_UpperCAmelCase					=    '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
							with mock.patch.dict(os.environ				,   {'''TESTING_MOCKED_DATALOADERS''': '''0'''}   ):
											_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
											_UpperCAmelCase					=    re.findall('''({.+})'''				,   __UpperCamelCase   )
											_UpperCAmelCase					=    [r for r in results if '''accuracy''' in r][-1]
											_UpperCAmelCase					=    ast.literal_eval(__UpperCamelCase   )
											self.assertGreaterEqual(results['''accuracy''']				,   0.7_5   )



			def  lowercase__						(		self     :    Any   )->List[Any]:
							_UpperCAmelCase					=    ['''examples/by_feature/multi_process_metrics.py''']
							run_command(self._launch_args + testargs   )



			@require_trackers
			@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}   )
			def  lowercase__						(		self     :    Optional[int]   )->Union[str, Any]:
							with tempfile.TemporaryDirectory() as tmpdir:
											_UpperCAmelCase					=    F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
											run_command(self._launch_args + testargs   )
											self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase				,   '''tracking'''   )   )   )



			def  lowercase__						(		self     :    Dict   )->Dict:
							_UpperCAmelCase					=    ['''examples/by_feature/gradient_accumulation.py''']
							run_command(self._launch_args + testargs   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    ['''examples/by_feature/local_sgd.py''']
							run_command(self._launch_args + testargs   )



