"""simple docstring"""







from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 		UpperCamelCase			(    lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_						):
				UpperCAmelCase				:      int    =		[R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']



				@register_to_config
				def __init__(self				:      Tuple	,  _A				:      int	,  _A				:      int	,  _A				:      Optional[int] = None	,  _A				:      int = 5_02_57	,  _A				:      int = 10_24	,  _A				:      int = 7_68	,  _A				:      int = 12	,  _A				:      int = 12	,  _A				:      Optional[int] = None	,  _A				:      str = "gelu_new"	,  _A				:      float = 0.1	,  _A				:      float = 0.1	,  _A				:      float = 0.1	,  _A				:      float = 1E-5	,  _A				:      float = 0.02	,  _A				:      bool = True	,  _A				:      bool = True	,  _A				:      bool = False	,  _A				:      bool = False	,  )    ->	List[Any]:
					super().__init__()

					__snake_case			:							List[Any]      		= prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
						raise ValueError(
						    f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
						    f" `n_embd`: {n_embd} are not equal.")

					__snake_case			:							Any      		= prefix_inner_dim
					__snake_case			:							str      		= prefix_hidden_dim

					__snake_case			:							int      		= (
					    nn.Linear(self.prefix_inner_dim	,  self.prefix_hidden_dim)
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					__snake_case			:							Optional[Any]      		= (
					    nn.Linear(self.prefix_hidden_dim	,  __snake_case) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					__snake_case			:							Union[str, Any]      		= GPTaConfig(
					    vocab_size=__snake_case	,  n_positions=__snake_case	,  n_embd=__snake_case	,  n_layer=__snake_case	,  n_head=__snake_case	,  n_inner=__snake_case	,  activation_function=__snake_case	,  resid_pdrop=__snake_case	,  embd_pdrop=__snake_case	,  attn_pdrop=__snake_case	,  layer_norm_epsilon=__snake_case	,  initializer_range=__snake_case	,  scale_attn_weights=__snake_case	,  use_cache=__snake_case	,  scale_attn_by_inverse_layer_idx=__snake_case	,  reorder_and_upcast_attn=__snake_case	,  )
					__snake_case			:							List[str]      		= GPTaLMHeadModel(__snake_case)



				def 					_lowercase     (self				:      Optional[Any]	,  _A				:      torch.Tensor	,  _A				:      torch.Tensor	,  _A				:      Optional[torch.Tensor] = None	,  _A				:      Optional[torch.Tensor] = None	,  )    ->	Any:
					__snake_case			:							Union[str, Any]      		= self.transformer.transformer.wte(__snake_case)
					__snake_case			:							int      		= self.encode_prefix(__snake_case)
					__snake_case			:							Optional[Any]      		= self.decode_prefix(__snake_case)
					__snake_case			:							List[Any]      		= torch.cat((prefix_embeds, embedding_text)	,  dim=1)

					if labels is not None:
						__snake_case			:							Union[str, Any]      		= self.get_dummy_token(input_ids.shape[0]	,  input_ids.device)
						__snake_case			:							str      		= torch.cat((dummy_token, input_ids)	,  dim=1)
					__snake_case			:							str      		= self.transformer(inputs_embeds=__snake_case	,  labels=__snake_case	,  attention_mask=__snake_case)
					if self.prefix_hidden_dim is not None:
						return out, hidden
					else:
						return out



				def 					_lowercase     (self				:      str	,  _A				:      int	,  _A				:      torch.device)    ->	torch.Tensor:
					return torch.zeros(__snake_case	,  self.prefix_length	,  dtype=torch.intaa	,  device=__snake_case)



				def 					_lowercase     (self				:      List[str]	,  _A				:      List[Any])    ->	Union[str, Any]:
					return self.encode_prefix(__snake_case)



				@torch.no_grad()
				def 					_lowercase     (self				:      Optional[int]	,  _A				:      int	,  _A				:      Any	,  _A				:      List[str])    ->	str:
					__snake_case			:							int      		= torch.split(__snake_case	,  1	,  dim=0)
					__snake_case			:							Tuple      		= []
					__snake_case			:							int      		= []
					for feature in features:
						__snake_case			:							List[Any]      		= self.decode_prefix(feature.to(__snake_case))  # back to the clip feature
						# Only support beam search for now
						__snake_case		,				__snake_case			:							List[str]      		= self.generate_beam(
						    input_embeds=__snake_case	,  device=__snake_case	,  eos_token_id=__snake_case)
						generated_tokens.append(output_tokens[0])
						generated_seq_lengths.append(seq_lengths[0])
					__snake_case			:							List[Any]      		= torch.stack(__snake_case)
					__snake_case			:							Optional[Any]      		= torch.stack(__snake_case)
					return generated_tokens, generated_seq_lengths






				@torch.no_grad()
				def 					_lowercase     (self				:      int	,  _A				:      int=None	,  _A				:      int=None	,  _A				:      Union[str, Any]=None	,  _A				:      int = 5	,  _A				:      int = 67	,  _A				:      float = 1.0	,  _A				:      Optional[int] = None	,  )    ->	List[str]:
					__snake_case			:							str      		= eos_token_id
					__snake_case			:							Any      		= None
					__snake_case			:							Optional[int]      		= None
					__snake_case			:							Dict      		= torch.ones(__snake_case	,  device=__snake_case	,  dtype=torch.int)
					__snake_case			:							Optional[int]      		= torch.zeros(__snake_case	,  device=__snake_case	,  dtype=torch.bool)

					if input_embeds is not None:
						__snake_case			:							Optional[int]      		= input_embeds
					else:
						__snake_case			:							str      		= self.transformer.transformer.wte(__snake_case)

					for i in range(__snake_case):
						__snake_case			:							List[str]      		= self.transformer(inputs_embeds=__snake_case)
						__snake_case			:							Optional[Any]      		= outputs.logits
						__snake_case			:							int      		= logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
						__snake_case			:							int      		= logits.softmax(-1).log()

						if scores is None:
							__snake_case		,				__snake_case			:							Tuple      		= logits.topk(__snake_case	,  -1)
							__snake_case			:							Union[str, Any]      		= generated.expand(__snake_case	,  *generated.shape[1:])
							__snake_case		,				__snake_case			:							Optional[int]      		= next_tokens.permute(1	,  0), scores.squeeze(0)
							if tokens is None:
								__snake_case			:							List[str]      		= next_tokens
							else:
								__snake_case			:							Union[str, Any]      		= tokens.expand(__snake_case	,  *tokens.shape[1:])
								__snake_case			:							Optional[int]      		= torch.cat((tokens, next_tokens)	,  dim=1)
						else:
							__snake_case			:							Optional[Any]      		= -float(np.inf)
							__snake_case			:							Dict      		= 0
							__snake_case			:							Union[str, Any]      		= scores[:, None] + logits
							seq_lengths[~is_stopped] += 1
							__snake_case			:							str      		= scores_sum / seq_lengths[:, None]
							__snake_case		,				__snake_case			:							Union[str, Any]      		= scores_sum_average.view(-1).topk(__snake_case	,  -1)
							__snake_case			:							List[Any]      		= next_tokens // scores_sum.shape[1]
							__snake_case			:							Tuple      		= seq_lengths[next_tokens_source]
							__snake_case			:							List[Any]      		= next_tokens % scores_sum.shape[1]
							__snake_case			:							str      		= next_tokens.unsqueeze(1)
							__snake_case			:							Optional[Any]      		= tokens[next_tokens_source]
							__snake_case			:							Optional[int]      		= torch.cat((tokens, next_tokens)	,  dim=1)
							__snake_case			:							List[Any]      		= generated[next_tokens_source]
							__snake_case			:							Any      		= scores_sum_average * seq_lengths
							__snake_case			:							List[Any]      		= is_stopped[next_tokens_source]

						__snake_case			:							Tuple      		= self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0]	,  1	,  -1)
						__snake_case			:							List[Any]      		= torch.cat((generated, next_token_embed)	,  dim=1)
						__snake_case			:							Union[str, Any]      		= is_stopped + next_tokens.eq(__snake_case).squeeze()
						if is_stopped.all():
							break

					__snake_case			:							List[Any]      		= scores / seq_lengths
					__snake_case			:							Optional[int]      		= scores.argsort(descending=__snake_case)
					# tokens tensors are already padded to max_seq_length
					__snake_case			:							Tuple      		= [tokens[i] for i in order]
					__snake_case			:							Tuple      		= torch.stack(__snake_case	,  dim=0)
					__snake_case			:							Dict      		= torch.tensor([seq_lengths[i] for i in order]	,  dtype=seq_lengths.dtype)
					return output_texts, seq_lengths









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

import pytest
from parameterized import 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.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]      )
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, 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  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Optional[Any]=1      )					->    int:
												# 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=F'{self.env.base_job_name}-single'			,instance_count=__snake_case			,instance_type=self.instance_type			,debugger_hook_config=__snake_case			,hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}			,metric_definitions=self.env.metric_definitions			,py_version='py36'			,)
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :str      )					->    Optional[int]:
												TrainingJobAnalytics(__snake_case      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )



					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												# create estimator
												a__             =     self.create_estimator()

												# run training
												estimator.fit()

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

												# extract kpis
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												a__             =     (
												    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      )




