


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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase_	       =			logging.get_logger(__name__)

lowercase_	       =			{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "gpt_neo"
	SCREAMING_SNAKE_CASE       =       ["past_key_values"]
	SCREAMING_SNAKE_CASE       =       {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



	def __init__(						self							:    Any				,	snake_case_							:    Optional[int]=5_02_57				,	snake_case_							:    str=20_48				,	snake_case_							:    Union[str, Any]=20_48				,	snake_case_							:    Optional[Any]=24				,	snake_case_							:    int=[[["global", "local"], 12]]				,	snake_case_							:    List[Any]=16				,	snake_case_							:    Optional[Any]=None				,	snake_case_							:    Tuple=2_56				,	snake_case_							:    List[Any]="gelu_new"				,	snake_case_							:    str=0.0				,	snake_case_							:    Dict=0.0				,	snake_case_							:    Optional[Any]=0.0				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    Union[str, Any]=1e-5				,	snake_case_							:    Any=0.0_2				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    Union[str, Any]=5_02_56				,	snake_case_							:    Optional[Any]=5_02_56				,	**snake_case_							:    int				,	)->							str:
						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =num_layers
						__lowerCAmelCase	   =num_heads
						__lowerCAmelCase	   =intermediate_size
						__lowerCAmelCase	   =window_size
						__lowerCAmelCase	   =activation_function
						__lowerCAmelCase	   =resid_dropout
						__lowerCAmelCase	   =embed_dropout
						__lowerCAmelCase	   =attention_dropout
						__lowerCAmelCase	   =classifier_dropout
						__lowerCAmelCase	   =layer_norm_epsilon
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =use_cache

						__lowerCAmelCase	   =bos_token_id
						__lowerCAmelCase	   =eos_token_id

						__lowerCAmelCase	   =attention_types
						__lowerCAmelCase	   =self.expand_attention_types_params(snake_case_)

						if len(self.attention_layers) != self.num_layers:
											raise ValueError(
											    """Configuration for convolutional module is incorrect. """
											    """It is required that `len(config.attention_layers)` == `config.num_layers` """
											    F"""but is `len(config.attention_layers) = {len(self.attention_layers)}`, """
											    F"""`config.num_layers = {self.num_layers}`. """
											    """`config.attention_layers` is prepared using `config.attention_types`. """
											    """Please verify the value of `config.attention_types` argument.""")

						super().__init__(bos_token_id=snake_case_				,	eos_token_id=snake_case_				,	**snake_case_)



	@staticmethod
	def 		UpperCamelCase							(						snake_case_							:    Optional[Any])->							Dict:
						__lowerCAmelCase	   =[]
						for item in attention_types:
											for _ in range(item[1]):
																attentions.extend(item[0])
						return attentions






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    Union[str, Any]	)  ->						Optional[Any]:
					import torch

					__lowerCAmelCase	   =input.size()
					__lowerCAmelCase	   =len(__lowerCamelCase	)
					__lowerCAmelCase	   =shape[dimension]

					__lowerCAmelCase	   =torch.arange(0				,   __lowerCamelCase				,   __lowerCamelCase	)
					__lowerCAmelCase	   =torch.div(sizedim - size				,   __lowerCamelCase				,   rounding_mode="""floor"""	) + 1
					__lowerCAmelCase	   =torch.arange(__lowerCamelCase	) + low_indices[:min_length][:, None]

					__lowerCAmelCase	   =[slice(__lowerCamelCase	)] * rank
					__lowerCAmelCase	   =indices
					__lowerCAmelCase	   =input[s]

					__lowerCAmelCase	   =list(range(0				,   rank + 1	)	)
					perm.append(perm.pop(dimension + 1	)	)

					return sliced.permute(__lowerCamelCase	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple				,   __lowerCamelCase	:    Union[str, Any]	)  ->						List[Any]:
					import torch

					__lowerCAmelCase	   =torch.arange(1				,   __lowerCamelCase	)
					__lowerCAmelCase	   =torch.remainder(__lowerCamelCase				,   __lowerCamelCase	)
					__lowerCAmelCase	   =remainders == 0
					__lowerCAmelCase	   =candidates[divisor_indices]
					__lowerCAmelCase	   =torch.max(__lowerCamelCase	)
					return largest_divisor, torch.div(__lowerCamelCase				,   __lowerCamelCase				,   rounding_mode="""floor"""	)






class 						__a (     SCREAMING_SNAKE_CASE				):



	@property
	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Mapping[str, Mapping[int, str]]:
						__lowerCAmelCase	   =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
						if self.use_past:
											self.fill_with_past_key_values_(snake_case_				,	direction="""inputs""")
											__lowerCAmelCase	   ={0: """batch""", 1: """past_sequence + sequence"""}
						else:
											__lowerCAmelCase	   ={0: """batch""", 1: """sequence"""}

						return common_inputs



	@property
	def 		UpperCamelCase							(						self							:    Dict)->							int:
						return self._config.num_heads



	def 		UpperCamelCase							(						self							:    List[str]				,	snake_case_							:    PreTrainedTokenizer				,	snake_case_							:    int = -1				,	snake_case_							:    int = -1				,	snake_case_							:    bool = False				,	snake_case_							:    Optional[TensorType] = None				,	)->							Mapping[str, Any]:
						__lowerCAmelCase	   =super(snake_case_				,	self).generate_dummy_inputs(
						    snake_case_				,	batch_size=snake_case_				,	seq_length=snake_case_				,	is_pair=snake_case_				,	framework=snake_case_)

						# We need to order the input in the way they appears in the forward()
						__lowerCAmelCase	   =OrderedDict({"""input_ids""": common_inputs["""input_ids"""]})

						# Need to add the past_keys
						if self.use_past:
											if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""")
											else:
																import torch

																__lowerCAmelCase   ,	__lowerCAmelCase	   =common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																__lowerCAmelCase	   =seqlen + 2
																__lowerCAmelCase	   =(
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__lowerCAmelCase	   =[
																    (torch.zeros(snake_case_), torch.zeros(snake_case_)) for _ in range(self.num_layers)
																]

						__lowerCAmelCase	   =common_inputs["""attention_mask"""]
						if self.use_past:
											__lowerCAmelCase	   =ordered_inputs["""attention_mask"""].dtype
											__lowerCAmelCase	   =torch.cat(
											    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_				,	snake_case_				,	dtype=snake_case_)]				,	dim=1)

						return ordered_inputs



	@property
	def 		UpperCamelCase							(						self							:    Optional[int])->							int:
						return 13









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": 650, "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": 600, "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": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]				)
class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Dict)->							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=snake_case_				,	)
						assert hasattr(self				,	"""env""")



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    str)->							Dict:
						__lowerCAmelCase	   =F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
						# distributed data settings
						__lowerCAmelCase	   ={"""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=snake_case_				,	instance_count=snake_case_				,	instance_type=self.instance_type				,	debugger_hook_config=snake_case_				,	hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}				,	metric_definitions=self.env.metric_definitions				,	distribution=snake_case_				,	py_version="""py36"""				,	)



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    List[Any])->							int:
						TrainingJobAnalytics(snake_case_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")



	@parameterized.expand([(2,)])
	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Dict)->							Optional[Any]:
						# create estimator
						__lowerCAmelCase	   =self.create_estimator(snake_case_)

						# run training
						estimator.fit()

						# result dataframe
						__lowerCAmelCase	   =TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

						# extract kpis
						__lowerCAmelCase	   =list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
						__lowerCAmelCase	   =list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__lowerCAmelCase	   =(
						    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""				,	99_99_99)
						)

						# 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}				,	snake_case_)




