


"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      str       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		dataset
														snake_case_           =		process
														snake_case_           =		params



							def __len__(				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return len(self.dataset       )



							def __getitem__(				self							:      List[str]		,       UpperCAmelCase_							:      Any       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.dataset[i]
														snake_case_           =		self.process(UpperCAmelCase_		,       **self.params       )
														return processed





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      str=None       )					->Dict:



														"""simple docstring"""



														snake_case_           =		loader
														snake_case_           =		infer
														snake_case_           =		params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					snake_case_           =		None
														snake_case_           =		loader_batch_size

														# Internal bookkeeping
														snake_case_           =		None
														snake_case_           =		None



							def __len__(				self							:      int       )					->Optional[int]:



														"""simple docstring"""



														return len(self.loader       )



							def __iter__(				self							:      Dict       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		iter(self.loader       )
														return self



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Dict:



														"""simple docstring"""



														if isinstance(self._loader_batch_data		,       torch.Tensor       ):
																					# Batch data is simple tensor, just fetch the slice
																					snake_case_           =		self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					snake_case_           =		{}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																																			# Convert ModelOutput to tuple first
																																			snake_case_           =		element.to_tuple()
																																			if isinstance(element[0]		,       torch.Tensor       ):
																																										snake_case_           =		tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
																																			elif isinstance(element[0]		,       np.ndarray       ):
																																										snake_case_           =		tuple(np.expand_dims(el[self._loader_batch_index]		,       0       ) for el in element       )
																																			continue
																												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0]		,       torch.Tensor       ):
																																										snake_case_           =		tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
																																			elif isinstance(element[0]		,       np.ndarray       ):
																																										snake_case_           =		tuple(np.expand_dims(el[self._loader_batch_index]		,       0       ) for el in element       )
																																			continue
																												if element is None:
																																			# This can happen for optional data that get passed around
																																			snake_case_           =		None
																												elif isinstance(element[self._loader_batch_index]		,       torch.Tensor       ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers

																																			snake_case_           =		element[self._loader_batch_index].unsqueeze(0       )
																												elif isinstance(element[self._loader_batch_index]		,       np.ndarray       ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers
																																			snake_case_           =		np.expand_dims(element[self._loader_batch_index]		,       0       )
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			snake_case_           =		element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					snake_case_           =		self._loader_batch_data.__class__(UpperCAmelCase_       )
														self._loader_batch_index += 1
														return result



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Any:



														"""simple docstring"""



														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					# We are currently unrolling a batch so we just need to return
																					# the current item within a batch
																					return self.loader_batch_item()

														# We're out of items within a batch
														snake_case_           =		next(self.iterator       )
														snake_case_           =		self.infer(UpperCAmelCase_		,       **self.params       )
														# We now have a batch of "inferred things".
														if self.loader_batch_size is not None:
																					# Try to infer the size of the batch
																					if isinstance(UpperCAmelCase_		,       torch.Tensor       ):
																												snake_case_           =		processed
																					else:
																												snake_case_           =		list(processed.keys()       )[0]
																												snake_case_           =		processed[key]
																					if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																												snake_case_           =		len(UpperCAmelCase_       )
																					else:
																												snake_case_           =		first_tensor.shape[0]
																					if 0 < observed_batch_size < self.loader_batch_size:
																												# could be last batch so we can't unroll as many
																												# elements.
																												snake_case_           =		observed_batch_size
																					# Setting internal index to unwrap the batch
																					snake_case_           =		processed
																					snake_case_           =		0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]=None       )					->str:



														"""simple docstring"""



														super().__init__(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )



							def __iter__(				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		iter(self.loader       )
														snake_case_           =		None
														return self



							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[Any]:



														"""simple docstring"""



														if self.subiterator is None:
																					snake_case_           =		self.infer(next(self.iterator       )		,       **self.params       )
														try:
																					# Try to return next item
																					snake_case_           =		next(self.subiterator       )
														except StopIteration:
																					# When a preprocess iterator ends, we can start lookig at the next item
																					# ChunkIterator will keep feeding until ALL elements of iterator
																					# all have created their subiterator and have been iterating against.
																					#
																					# Another way to look at it, is we're basically flattening lists of lists
																					# into a single list, but with generators
																					snake_case_           =		self.infer(next(self.iterator       )		,       **self.params       )
																					snake_case_           =		next(self.subiterator       )
														return processed





class      __A     (snake_case__):





							'''simple docstring'''

							def __iter__(				self							:      List[Any]       )					->int:



														"""simple docstring"""



														snake_case_           =		iter(self.loader       )
														return self



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		False
														snake_case_           =		[]
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					while self._loader_batch_index < self.loader_batch_size:
																												snake_case_           =		self.loader_batch_item()
																												snake_case_           =		item.pop("""is_last"""       )
																												accumulator.append(UpperCAmelCase_       )
																												if is_last:
																																			return accumulator

														while not is_last:
																					snake_case_           =		self.infer(next(self.iterator       )		,       **self.params       )
																					if self.loader_batch_size is not None:
																												if isinstance(UpperCAmelCase_		,       torch.Tensor       ):
																																			snake_case_           =		processed
																												else:
																																			snake_case_           =		list(processed.keys()       )[0]
																																			snake_case_           =		processed[key]
																												if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																																			snake_case_           =		len(UpperCAmelCase_       )
																												else:
																																			snake_case_           =		first_tensor.shape[0]
																												if 0 < observed_batch_size < self.loader_batch_size:
																																			# could be last batch so we can't unroll as many
																																			# elements.
																																			snake_case_           =		observed_batch_size
																												snake_case_           =		processed
																												snake_case_           =		0
																												while self._loader_batch_index < self.loader_batch_size:
																																			snake_case_           =		self.loader_batch_item()
																																			snake_case_           =		item.pop("""is_last"""       )
																																			accumulator.append(UpperCAmelCase_       )
																																			if is_last:
																																										return accumulator
																					else:
																												snake_case_           =		processed
																												snake_case_           =		item.pop("""is_last"""       )
																												accumulator.append(UpperCAmelCase_       )
														return accumulator





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      Dataset		,       UpperCAmelCase_							:      str       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		dataset
														snake_case_           =		key



							def __len__(				self							:      Union[str, Any]       )					->int:



														"""simple docstring"""



														return len(self.dataset       )



							def __getitem__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														return self.dataset[i][self.key]





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Any		,       UpperCAmelCase_							:      Dataset		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str       )					->Dict:



														"""simple docstring"""



														snake_case_           =		dataset
														snake_case_           =		keya
														snake_case_           =		keya



							def __len__(				self							:      Dict       )					->List[str]:



														"""simple docstring"""



														return len(self.dataset       )



							def __getitem__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Tuple:



														"""simple docstring"""



														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







"""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


__SCREAMING_SNAKE_CASE							:    Union[str, 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)

__SCREAMING_SNAKE_CASE							:    Dict		=    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

__SCREAMING_SNAKE_CASE							:    Dict		=    'zero2'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    'zero3'
__SCREAMING_SNAKE_CASE							:    int		=    [ZEROa, ZEROa]







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							# 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
							snake_case_           =		parameterized.to_safe_name("""_""".join(str(_SCREAMING_SNAKE_CASE				) for x in param.args				)				)
							return f"""{func.__name__}_{param_based_name}"""


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





@slow
@require_deepspeed
@require_torch_gpu
class      __A     (snake_case__):





							'''simple docstring'''

							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Union[str, Any]       )					->Any:



														"""simple docstring"""



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



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



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



							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



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



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		models[model]

														snake_case_           =		self.run_trainer(
														    stage=UpperCAmelCase_		,       model_name=UpperCAmelCase_		,       eval_steps=UpperCAmelCase_		,       num_train_epochs=1		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )

														self.do_checks(UpperCAmelCase_       )

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.get_auto_remove_tmp_dir("""./xxx"""		,       after=UpperCAmelCase_       )
														snake_case_           =		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(UpperCAmelCase_       )}
            --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
														snake_case_           =		F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
														snake_case_           =		[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
														snake_case_           =		self.get_launcher(UpperCAmelCase_       )

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

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Any=False       )					->Tuple:



														"""simple docstring"""



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



