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

from ..utils.generic import ModelOutput




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Any     ,   a		:str     ,   a		:Tuple     ,   a		:Dict						)		-> List[Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					dataset
								__UpperCamelCase	:   Any			   =					process
								__UpperCamelCase	:   Any			   =					params







	def __len__(					self		:Optional[int]						)		-> int:
								return len(self.dataset						)







	def __getitem__(					self		:Tuple     ,   a		:Any						)		-> Any:
								__UpperCamelCase	:   str			   =					self.dataset[i]
								__UpperCamelCase	:   Tuple			   =					self.process(a     ,   **self.params						)
								return processed




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:int     ,   a		:List[str]     ,   a		:int     ,   a		:Any     ,   a		:str=None						)		-> Tuple:
								__UpperCamelCase	:   int			   =					loader
								__UpperCamelCase	:   Optional[Any]			   =					infer
								__UpperCamelCase	:   List[str]			   =					params
								if loader_batch_size == 1:
															# Let's spare some time by deactivating altogether
															__UpperCamelCase	:   str			   =					None
								__UpperCamelCase	:   Tuple			   =					loader_batch_size

								# Internal bookkeeping
								__UpperCamelCase	:   List[str]			   =					None
								__UpperCamelCase	:   Optional[int]			   =					None







	def __len__(					self		:Optional[int]						)		-> Tuple:
								return len(self.loader						)







	def __iter__(					self		:Any						)		-> List[Any]:
								__UpperCamelCase	:   int			   =					iter(self.loader						)
								return self







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

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







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Any:
								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
								__UpperCamelCase	:   Optional[int]			   =					next(self.iterator						)
								__UpperCamelCase	:   Any			   =					self.infer(a     ,   **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(a     ,   torch.Tensor						):
																						__UpperCamelCase	:   Optional[Any]			   =					processed
															else:
																						__UpperCamelCase	:   List[Any]			   =					list(processed.keys()						)[0]
																						__UpperCamelCase	:   Optional[int]			   =					processed[key]
															if isinstance(a     ,   a						):
																						__UpperCamelCase	:   Dict			   =					len(a						)
															else:
																						__UpperCamelCase	:   List[Any]			   =					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.
																						__UpperCamelCase	:   Dict			   =					observed_batch_size
															# Setting internal index to unwrap the batch
															__UpperCamelCase	:   Union[str, Any]			   =					processed
															__UpperCamelCase	:   Optional[Any]			   =					0
															return self.loader_batch_item()
								else:
															# We're not unrolling batches
															return processed




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Optional[int]     ,   a		:List[Any]     ,   a		:Tuple     ,   a		:Optional[int]     ,   a		:List[Any]=None						)		-> Any:
								super().__init__(a     ,   a     ,   a						)







	def __iter__(					self		:Any						)		-> Tuple:
								__UpperCamelCase	:   Any			   =					iter(self.loader						)
								__UpperCamelCase	:   int			   =					None
								return self







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Union[str, Any]:
								if self.subiterator is None:
															__UpperCamelCase	:   List[Any]			   =					self.infer(next(self.iterator						)     ,   **self.params						)
								try:
															# Try to return next item
															__UpperCamelCase	:   List[str]			   =					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
															__UpperCamelCase	:   Union[str, Any]			   =					self.infer(next(self.iterator						)     ,   **self.params						)
															__UpperCamelCase	:   Any			   =					next(self.subiterator						)
								return processed




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __iter__(					self		:Union[str, Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					iter(self.loader						)
								return self







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								# Extremely similar to PipelineIterator in its unpacking mechanism
								# BUT, we have an extra required item which is the presence of `is_last`
								# That is because everything is flattened by `PipelineChunkIterator` we
								# need to keep track of how to regroup here in the original `process`
								# boundaries so that `process` and `postprocess` see the same data.

								# This iterator accumulates items (possibly while unbatching) until it
								# its a `is_last` and then just passes it on to the caller.
								__UpperCamelCase	:   Dict			   =					False
								__UpperCamelCase	:   Any			   =					[]
								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:
																						__UpperCamelCase	:   List[str]			   =					self.loader_batch_item()
																						__UpperCamelCase	:   Any			   =					item.pop("is_last"						)
																						accumulator.append(a						)
																						if is_last:
																													return accumulator

								while not is_last:
															__UpperCamelCase	:   Tuple			   =					self.infer(next(self.iterator						)     ,   **self.params						)
															if self.loader_batch_size is not None:
																						if isinstance(a     ,   torch.Tensor						):
																													__UpperCamelCase	:   Dict			   =					processed
																						else:
																													__UpperCamelCase	:   List[str]			   =					list(processed.keys()						)[0]
																													__UpperCamelCase	:   Tuple			   =					processed[key]
																						if isinstance(a     ,   a						):
																													__UpperCamelCase	:   Union[str, Any]			   =					len(a						)
																						else:
																													__UpperCamelCase	:   Optional[Any]			   =					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.
																													__UpperCamelCase	:   Optional[int]			   =					observed_batch_size
																						__UpperCamelCase	:   Any			   =					processed
																						__UpperCamelCase	:   Tuple			   =					0
																						while self._loader_batch_index < self.loader_batch_size:
																													__UpperCamelCase	:   int			   =					self.loader_batch_item()
																													__UpperCamelCase	:   Union[str, Any]			   =					item.pop("is_last"						)
																													accumulator.append(a						)
																													if is_last:
																																				return accumulator
															else:
																						__UpperCamelCase	:   Dict			   =					processed
																						__UpperCamelCase	:   Optional[Any]			   =					item.pop("is_last"						)
																						accumulator.append(a						)
								return accumulator




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Dict     ,   a		:Dataset     ,   a		:str						)		-> List[Any]:
								__UpperCamelCase	:   Tuple			   =					dataset
								__UpperCamelCase	:   Optional[Any]			   =					key







	def __len__(					self		:Optional[Any]						)		-> Union[str, Any]:
								return len(self.dataset						)







	def __getitem__(					self		:Optional[Any]     ,   a		:List[str]						)		-> Optional[int]:
								return self.dataset[i][self.key]




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:Dataset     ,   a		:str     ,   a		:str						)		-> Dict:
								__UpperCamelCase	:   Optional[Any]			   =					dataset
								__UpperCamelCase	:   Optional[int]			   =					keya
								__UpperCamelCase	:   Tuple			   =					keya







	def __len__(					self		:Optional[Any]						)		-> Tuple:
								return len(self.dataset						)







	def __getitem__(					self		:int     ,   a		:Any						)		-> Optional[Any]:
								return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'     ,						'False')) is not True     ,						reason='Skipping test because should only be run when releasing minor transformers version'     ,						)
@pytest.mark.usefixtures('sm_env')
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ])
class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:List[Any]						)		-> Any:
								if self.framework == "pytorch":
															subprocess.run(
															    f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split()     ,   encoding="utf-8"     ,   check=a     ,   )
								assert hasattr(self     ,   "env"						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:Optional[Any]						)		-> Dict:
								__UpperCamelCase	:   str			   =					f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
								# distributed data settings
								__UpperCamelCase	:   Optional[int]			   =					{"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

								# creates estimator
								return HuggingFace(
								    entry_point=self.script     ,   source_dir=self.env.test_path     ,   role=self.env.role     ,   image_uri=self.env.image_uri     ,   base_job_name=a     ,   instance_count=a     ,   instance_type=self.instance_type     ,   debugger_hook_config=a     ,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}     ,   metric_definitions=self.env.metric_definitions     ,   distribution=a     ,   py_version="py36"     ,   )







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Dict						)		-> Optional[int]:
								TrainingJobAnalytics(a						).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'						)







	@parameterized.expand([(2,)]						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Tuple						)		-> List[Any]:
								# create estimator
								__UpperCamelCase	:   int			   =					self.create_estimator(a						)

								# run training
								estimator.fit()

								# result dataframe
								__UpperCamelCase	:   Optional[int]			   =					TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

								# extract kpis
								__UpperCamelCase	:   Optional[Any]			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
								__UpperCamelCase	:   Tuple			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								__UpperCamelCase	:   int			   =					(
								    Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"     ,   9_9_9_9_9_9						)
								)

								# assert kpis
								assert train_runtime <= self.results["train_runtime"]
								assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy						)
								assert all(t <= self.results["eval_loss"] for t in eval_loss						)

								# dump tests result into json file to share in PR
								with open(f'{estimator.latest_training_job.name}.json'     ,   "w"						) as outfile:
															json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}     ,   a						)