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

from ..utils.generic import ModelOutput







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								__a										=    dataset
								__a										=    process
								__a										=    params




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


								'''simple docstring'''




								return len(self.dataset				)




				def __getitem__(     self		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.dataset[i]
								__a										=    self.process(_snake_case		,     **self.params				)
								return processed







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case=None				)    ->      Dict:


								'''simple docstring'''




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

								# Internal bookkeeping
								__a										=    None
								__a										=    None




				def __len__(     self				)    ->      int:


								'''simple docstring'''




								return len(self.loader				)




				def __iter__(     self				)    ->      str:


								'''simple docstring'''




								__a										=    iter(self.loader				)
								return self




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


								'''simple docstring'''




								if isinstance(self._loader_batch_data		,     torch.Tensor				):
												# Batch data is simple tensor, just fetch the slice
												__a										=    self._loader_batch_data[self._loader_batch_index]
								else:
												# Batch data is assumed to be BaseModelOutput (or dict)
												__a										=    {}
												for k, element in self._loader_batch_data.items():
																if isinstance(_snake_case		,     _snake_case				):
																				# Convert ModelOutput to tuple first
																				__a										=    element.to_tuple()
																				if isinstance(element[0]		,     torch.Tensor				):
																								__a										=    tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
																				elif isinstance(element[0]		,     np.ndarray				):
																								__a										=    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(_snake_case		,     _snake_case				):
																				# Those are stored as lists of tensors so need specific unbatching.
																				if isinstance(element[0]		,     torch.Tensor				):
																								__a										=    tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
																				elif isinstance(element[0]		,     np.ndarray				):
																								__a										=    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
																				__a										=    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

																				__a										=    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
																				__a										=    np.expand_dims(element[self._loader_batch_index]		,     0				)
																else:
																				# This is typically a list, so no need to `unsqueeze`.
																				__a										=    element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
												__a										=    self._loader_batch_data.__class__(_snake_case				)
								self._loader_batch_index += 1
								return result




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''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
								__a										=    next(self.iterator				)
								__a										=    self.infer(_snake_case		,     **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(_snake_case		,     torch.Tensor				):
																__a										=    processed
												else:
																__a										=    list(processed.keys()				)[0]
																__a										=    processed[key]
												if isinstance(_snake_case		,     _snake_case				):
																__a										=    len(_snake_case				)
												else:
																__a										=    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.
																__a										=    observed_batch_size
												# Setting internal index to unwrap the batch
												__a										=    processed
												__a										=    0
												return self.loader_batch_item()
								else:
												# We're not unrolling batches
												return processed







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case=None				)    ->      Dict:


								'''simple docstring'''




								super().__init__(_snake_case		,     _snake_case		,     _snake_case				)




				def __iter__(     self				)    ->      Tuple:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								if self.subiterator is None:
												__a										=    self.infer(next(self.iterator				)		,     **self.params				)
								try:
												# Try to return next item
												__a										=    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
												__a										=    self.infer(next(self.iterator				)		,     **self.params				)
												__a										=    next(self.subiterator				)
								return processed







class 			__A( a			):




				def __iter__(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    iter(self.loader				)
								return self




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    False
								__a										=    []
								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:
																__a										=    self.loader_batch_item()
																__a										=    item.pop('''is_last'''				)
																accumulator.append(_snake_case				)
																if is_last:
																				return accumulator

								while not is_last:
												__a										=    self.infer(next(self.iterator				)		,     **self.params				)
												if self.loader_batch_size is not None:
																if isinstance(_snake_case		,     torch.Tensor				):
																				__a										=    processed
																else:
																				__a										=    list(processed.keys()				)[0]
																				__a										=    processed[key]
																if isinstance(_snake_case		,     _snake_case				):
																				__a										=    len(_snake_case				)
																else:
																				__a										=    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.
																				__a										=    observed_batch_size
																__a										=    processed
																__a										=    0
																while self._loader_batch_index < self.loader_batch_size:
																				__a										=    self.loader_batch_item()
																				__a										=    item.pop('''is_last'''				)
																				accumulator.append(_snake_case				)
																				if is_last:
																								return accumulator
												else:
																__a										=    processed
																__a										=    item.pop('''is_last'''				)
																accumulator.append(_snake_case				)
								return accumulator







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    dataset
								__a										=    key




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


								'''simple docstring'''




								return len(self.dataset				)




				def __getitem__(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




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







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    dataset
								__a										=    keya
								__a										=    keya




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


								'''simple docstring'''




								return len(self.dataset				)




				def __getitem__(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




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

# 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     :	Optional[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(4_2)

A     :	List[str]    = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

A     :	Optional[int]    = 'zero2'
A     :	str    = 'zero3'
A     :	Tuple    = [ZEROa, ZEROa]


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  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										=    parameterized.to_safe_name('''_'''.join(str(a__  ) for x in param.args  )  )
				return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
A     :	Union[str, Any]    = list(itertools.product(stages, models.keys()))







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




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




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




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      int:


								'''simple docstring'''




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




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      str:


								'''simple docstring'''




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




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = True		,     _snake_case = True		,     _snake_case = True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    models[model]

								__a										=    self.run_trainer(
								    stage=_snake_case		,     model_name=_snake_case		,     eval_steps=_snake_case		,     num_train_epochs=1		,     distributed=_snake_case		,     fpaa=_snake_case		,     )

								self.do_checks(_snake_case				)

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = 1		,     _snake_case = True		,     _snake_case = True		,     )    ->      Union[str, Any]:


								'''simple docstring'''




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

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

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      List[str]:


								'''simple docstring'''




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