



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
							import torch







class       __lowerCAmelCase   :



			def __init__(						self       :  List[Any]			,		A       :  int			,		A       :  Dict=14			,		A       :  Tuple=7			,		A       :  Optional[Any]=True			,		A       :  Any=True			,		A       :  Optional[Any]=False			,		A       :  str=True			,		A       :  Any=99			,		A       :  List[Any]=32			,		A       :  List[str]=4			,		A       :  Any=4			,		A       :  List[Any]=4			,		A       :  Optional[Any]=37			,		A       :  str="gelu"			,		A       :  int=0.1			,		A       :  Union[str, Any]=0.1			,		A       :  Union[str, Any]=5_12			,		A       :  int=0.0_2			,		)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_input_mask
						_UpperCAmelCase			=       use_token_type_ids
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       rotary_dim
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       vocab_size - 1
						_UpperCAmelCase			=       vocab_size - 1
						_UpperCAmelCase			=       vocab_size - 1



			def    _lowerCamelCase	(						self       :  Any)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

						_UpperCAmelCase			=       None
						if self.use_input_mask:
									_UpperCAmelCase			=       random_attention_mask([self.batch_size, self.seq_length])

						_UpperCAmelCase			=       GPTJConfig(
						    vocab_size=self.vocab_size			,		n_embd=self.hidden_size			,		n_layer=self.num_hidden_layers			,		n_head=self.num_attention_heads			,		n_positions=self.max_position_embeddings			,		use_cache=A			,		bos_token_id=self.bos_token_id			,		eos_token_id=self.eos_token_id			,		pad_token_id=self.pad_token_id			,		rotary_dim=self.rotary_dim			,		)

						return (config, input_ids, input_mask)



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       config_and_inputs
						_UpperCAmelCase			=       {'input_ids': input_ids, 'attention_mask': attention_mask}
						return config, inputs_dict



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Optional[Any]			,		A       :  str			,		A       :  Any			,		A       :  List[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       20
						_UpperCAmelCase			=       model_class_name(A)

						_UpperCAmelCase			=       model.init_cache(input_ids.shape[0]			,		A)
						_UpperCAmelCase			=       jnp.ones((input_ids.shape[0], max_decoder_length)			,		dtype='i4')

						_UpperCAmelCase			=       jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1)[None, :]			,		(input_ids.shape[0], input_ids.shape[-1] - 1))
						_UpperCAmelCase			=       model(
						    input_ids[:, :-1]			,		attention_mask=A			,		past_key_values=A			,		position_ids=A			,		)

						_UpperCAmelCase			=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,		dtype='i4')
						_UpperCAmelCase			=       model(
						    input_ids[:, -1:]			,		attention_mask=A			,		past_key_values=outputs_cache.past_key_values			,		position_ids=A			,		)

						_UpperCAmelCase			=       model(A)

						_UpperCAmelCase			=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
						self.parent.assertTrue(diff < 1E-3			,		msg=F"Max diff is {diff}")



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Any			,		A       :  Dict			,		A       :  Union[str, Any]			,		A       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       20
						_UpperCAmelCase			=       model_class_name(A)

						_UpperCAmelCase			=       jnp.concatenate(
						    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]			,		axis=-1			,		)

						_UpperCAmelCase			=       model.init_cache(input_ids.shape[0]			,		A)
						_UpperCAmelCase			=       jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1)[None, :]			,		(input_ids.shape[0], input_ids.shape[-1] - 1))

						_UpperCAmelCase			=       model(
						    input_ids[:, :-1]			,		attention_mask=A			,		past_key_values=A			,		position_ids=A			,		)
						_UpperCAmelCase			=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,		dtype='i4')
						_UpperCAmelCase			=       model(
						    input_ids[:, -1:]			,		past_key_values=outputs_cache.past_key_values			,		attention_mask=A			,		position_ids=A			,		)

						_UpperCAmelCase			=       model(A			,		attention_mask=A)

						_UpperCAmelCase			=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
						self.parent.assertTrue(diff < 1E-3			,		msg=F"Max diff is {diff}")







@require_flax
class       __lowerCAmelCase   (      A    ,						A    ,						unittest.TestCase						):
			UpperCamelCase        =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			UpperCamelCase        =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxGPTJModelTester(self)



			def    _lowerCamelCase	(						self       :  int)       ->   Any:



						"""simple docstring"""






						for model_class_name in self.all_model_classes:
									_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(A			,		A			,		A			,		A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						for model_class_name in self.all_model_classes:
									_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    A			,		A			,		A			,		A)



			@tooslow
			def    _lowerCamelCase	(						self       :  Dict)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       GPTaTokenizer.from_pretrained('gpt2'			,		pad_token='<|endoftext|>'			,		padding_side='left')
						_UpperCAmelCase			=       tokenizer(['Hello this is a long string', 'Hey']			,		return_tensors='np'			,		padding=A			,		truncation=A)

						_UpperCAmelCase			=       FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B')
						_UpperCAmelCase			=       False
						_UpperCAmelCase			=       model.config.eos_token_id

						_UpperCAmelCase			=       jax.jit(model.generate)

						_UpperCAmelCase			=       jit_generate(
						    inputs['input_ids']			,		attention_mask=inputs['attention_mask']			,		pad_token_id=tokenizer.pad_token_id).sequences

						_UpperCAmelCase			=       tokenizer.batch_decode(A			,		skip_special_tokens=A)

						_UpperCAmelCase			=       [
						    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
						    'Hey, I\'m a little late to the party. I\'m going to',
						]

						self.assertListEqual(A			,		A)



			@is_pt_flax_cross_test
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
												# prepare inputs
												_UpperCAmelCase			=       self._prepare_for_class(A			,		A)
												_UpperCAmelCase			=       {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

												# load corresponding PyTorch class
												_UpperCAmelCase			=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
												_UpperCAmelCase			=       getattr(A			,		A)

												_UpperCAmelCase	,					_UpperCAmelCase			=       pt_inputs['input_ids'].shape
												_UpperCAmelCase			=       np.random.randint(0			,		seq_length - 1			,		size=(batch_size,))
												for batch_idx, start_index in enumerate(A):
															_UpperCAmelCase			=       0
															_UpperCAmelCase			=       1
															_UpperCAmelCase			=       0
															_UpperCAmelCase			=       1
												_UpperCAmelCase			=       pt_model_class(A).eval()
												_UpperCAmelCase			=       model_class(A			,		dtype=jnp.floataa)

												_UpperCAmelCase			=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,		A)
												_UpperCAmelCase			=       fx_state

												with torch.no_grad():
															_UpperCAmelCase			=       pt_model(**A).to_tuple()

												_UpperCAmelCase			=       fx_model(**A).to_tuple()
												self.assertEqual(len(A)			,		len(A)			,		'Output lengths differ between Flax and PyTorch')
												for fx_output, pt_output in zip(A			,		A):
															self.assert_almost_equals(fx_output[:, -1]			,		pt_output[:, -1].numpy()			,		4E-2)

												with tempfile.TemporaryDirectory() as tmpdirname:
															pt_model.save_pretrained(A)
															_UpperCAmelCase			=       model_class.from_pretrained(A			,		from_pt=A)

												_UpperCAmelCase			=       fx_model_loaded(**A).to_tuple()
												self.assertEqual(
												    len(A)			,		len(A)			,		'Output lengths differ between Flax and PyTorch')
												for fx_output_loaded, pt_output in zip(A			,		A):
															self.assert_almost_equals(fx_output_loaded[:, -1]			,		pt_output[:, -1].numpy()			,		4E-2)



			@is_pt_flax_cross_test
			def    _lowerCamelCase	(						self       :  int)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
												# prepare inputs
												_UpperCAmelCase			=       self._prepare_for_class(A			,		A)
												_UpperCAmelCase			=       {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

												# load corresponding PyTorch class
												_UpperCAmelCase			=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
												_UpperCAmelCase			=       getattr(A			,		A)

												_UpperCAmelCase			=       pt_model_class(A).eval()
												_UpperCAmelCase			=       model_class(A			,		dtype=jnp.floataa)

												_UpperCAmelCase			=       load_flax_weights_in_pytorch_model(A			,		fx_model.params)
												_UpperCAmelCase	,					_UpperCAmelCase			=       pt_inputs['input_ids'].shape
												_UpperCAmelCase			=       np.random.randint(0			,		seq_length - 1			,		size=(batch_size,))
												for batch_idx, start_index in enumerate(A):
															_UpperCAmelCase			=       0
															_UpperCAmelCase			=       1
															_UpperCAmelCase			=       0
															_UpperCAmelCase			=       1

												# make sure weights are tied in PyTorch
												pt_model.tie_weights()

												with torch.no_grad():
															_UpperCAmelCase			=       pt_model(**A).to_tuple()

												_UpperCAmelCase			=       fx_model(**A).to_tuple()
												self.assertEqual(len(A)			,		len(A)			,		'Output lengths differ between Flax and PyTorch')
												for fx_output, pt_output in zip(A			,		A):
															self.assert_almost_equals(fx_output[:, -1]			,		pt_output[:, -1].numpy()			,		4E-2)

												with tempfile.TemporaryDirectory() as tmpdirname:
															fx_model.save_pretrained(A)
															_UpperCAmelCase			=       pt_model_class.from_pretrained(A			,		from_flax=A)

												with torch.no_grad():
															_UpperCAmelCase			=       pt_model_loaded(**A).to_tuple()

												self.assertEqual(
												    len(A)			,		len(A)			,		'Output lengths differ between Flax and PyTorch')
												for fx_output, pt_output in zip(A			,		A):
															self.assert_almost_equals(fx_output[:, -1]			,		pt_output[:, -1].numpy()			,		4E-2)



			@tooslow
			def    _lowerCamelCase	(						self       :  Any)       ->   Tuple:



						"""simple docstring"""






						for model_class_name in self.all_model_classes:
									_UpperCAmelCase			=       model_class_name.from_pretrained('EleutherAI/gpt-j-6B')
									_UpperCAmelCase			=       model(np.ones((1, 1)))
									self.assertIsNotNone(A)





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       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Any:



						"""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    _lowerCamelCase	(						self       :  Any			,		A       :  Tuple=1)       ->   List[str]:



						"""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    _lowerCamelCase	(						self       :  Dict			,		A       :  int)       ->   str:



						"""simple docstring"""






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



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



						"""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'			,		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}			,		A)
