





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

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


__A				:    Optional[Any]       =   logging.get_logger(__name__)


__A				:    str       =   {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''codegen'''
	UpperCAmelCase__					=			{
	    '''max_position_embeddings''': '''n_positions''',
	    '''hidden_size''': '''n_embd''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}
	def __init__(    self							,					_A=5_0_4_0_0							,					_A=2_0_4_8							,					_A=2_0_4_8							,					_A=4_0_9_6							,					_A=2_8							,					_A=1_6							,					_A=6_4							,					_A=None							,					_A="gelu_new"							,					_A=0.0							,					_A=0.0							,					_A=0.0							,					_A=1E-5							,					_A=0.02							,					_A=True							,					_A=5_0_2_5_6							,					_A=5_0_2_5_6							,					_A=False							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				n_ctx
				UpperCAmelCase   		=				n_positions
				UpperCAmelCase   		=				n_embd
				UpperCAmelCase   		=				n_layer
				UpperCAmelCase   		=				n_head
				UpperCAmelCase   		=				n_inner
				UpperCAmelCase   		=				rotary_dim
				UpperCAmelCase   		=				activation_function
				UpperCAmelCase   		=				resid_pdrop
				UpperCAmelCase   		=				embd_pdrop
				UpperCAmelCase   		=				attn_pdrop
				UpperCAmelCase   		=				layer_norm_epsilon
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				use_cache

				UpperCAmelCase   		=				bos_token_id
				UpperCAmelCase   		=				eos_token_id

				super().__init__(
				    bos_token_id=_A							,					eos_token_id=_A							,					tie_word_embeddings=_A							,					**_A		)

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A = "default"							,					_A = None							,					_A = False							,					):






				'''simple docstring'''







				super().__init__(_A							,					task=_A							,					patching_specs=_A							,					use_past=_A		)
				if not getattr(self._config							,					'''pad_token_id'''							,					_A		):
							# TODO: how to do that better?
							UpperCAmelCase   		=				0
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
				if self.use_past:
							self.fill_with_past_key_values_(_A							,					direction='''inputs'''		)
							UpperCAmelCase   		=				{0: '''batch''', 1: '''past_sequence + sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}

				return common_inputs
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self._config.n_layer
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self._config.n_head
	def      _lowercase				(    self							,					_A							,					_A = -1							,					_A = -1							,					_A = False							,					_A = None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				super(_A							,					self		).generate_dummy_inputs(
				    _A							,					batch_size=_A							,					seq_length=_A							,					is_pair=_A							,					framework=_A		)

				# We need to order the input in the way they appears in the forward()
				UpperCAmelCase   		=				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

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

				UpperCAmelCase   		=				common_inputs['''attention_mask''']
				if self.use_past:
							UpperCAmelCase   		=				ordered_inputs['''attention_mask'''].dtype
							UpperCAmelCase   		=				torch.cat(
							    [ordered_inputs['''attention_mask'''], torch.ones(_A							,					_A							,					dtype=_A		)]							,					dim=1		)

				return ordered_inputs







	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return 1_3










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 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				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      _lowercase				(    self							,					_A=1		):






				'''simple docstring'''







				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=_A							,					instance_type=self.instance_type							,					debugger_hook_config=_A							,					hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}							,					metric_definitions=self.env.metric_definitions							,					py_version='''py36'''							,					)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				TrainingJobAnalytics(_A		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.create_estimator()

				# run training
				estimator.fit()

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

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



