


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowerCAmelCase__       :	Tuple     				=torch.cuda.is_available()
lowerCAmelCase__       :	int     				=[' Sam ate lunch today.', 'Sams lunch ingredients.']
lowerCAmelCase__       :	Dict     				=['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowerCAmelCase__       :	List[str]     				={
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 42,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1e-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}






def 		a__       (							A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			Any       						= '\n'.join(A__							)
			Path(A__							).open('w'							).writelines(A__							)






def 		a__       (							A__							):
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(A__,	F'''{split}.source'''							),	A__							)
						_dump_articles(os.path.join(A__,	F'''{split}.target'''							),	A__							)
			return tmp_dir







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@require_torch_multi_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= {
										    'no_teacher': True,
										    'freeze_encoder': True,
										    'gpus': 2,
										    'overwrite_output_dir': True,
										    'sortish_sampler': True,
										}
										self._test_distiller_cli_fork(lowerCAmelCase__       ,		check_contents=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=True    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= {
										    'label_smoothing': 0.0,
										    'early_stopping_patience': -1,
										    'train_batch_size': 1,
										    'eval_batch_size': 2,
										    'max_epochs': 2,
										    'alpha_mlm': 0.2,
										    'alpha_ce': 0.8,
										    'do_predict': True,
										    'model_name_or_path': 'sshleifer/tinier_bart',
										    'teacher': CHEAP_ARGS['model_name_or_path'],
										    'val_check_interval': 0.5,
										}
										default_updates.update(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			dict       						= CHEAP_ARGS.copy()
										SCREAMING_SNAKE_CASE_						:			List[str]       						= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= self.get_auto_remove_tmp_dir()
										args_d.update(data_dir=lowerCAmelCase__       ,		output_dir=lowerCAmelCase__       ,		**lowerCAmelCase__    )

										def convert(lowerCAmelCase__       ,		lowerCAmelCase__    ):
													if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																return ""
													if v is False or v is None:
																return ""
													if v is True:  # or len(str(v))==0:
																return F'''--{k}'''
													return F'''--{k}={v}'''

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= [x for x in (convert(lowerCAmelCase__       ,		lowerCAmelCase__    ) for k, v in args_d.items()) if len(lowerCAmelCase__    )]
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			Any       						= os.listdir(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= {os.path.basename(lowerCAmelCase__    ) for p in contents}
										SCREAMING_SNAKE_CASE_						:			int       						= [p for p in contents if p.endswith('ckpt'    )]
										assert len(lowerCAmelCase__    ) > 0

										self.assertIn('test_generations.txt'       ,		lowerCAmelCase__    )
										self.assertIn('test_results.txt'       ,		lowerCAmelCase__    )

										# get the following from the module, (we don't have access to `model` here)
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= os.path.join(lowerCAmelCase__       ,		'metrics.json'    )
										SCREAMING_SNAKE_CASE_						:			Any       						= 'rouge2'

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= load_json(lowerCAmelCase__    )
										# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
										print(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= metrics['val'][-1]
										self.assertGreaterEqual(last_step_stats['val_avg_gen_time']       ,		0.01    )
										self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']       ,		lowerCAmelCase__    )
										self.assertEqual(len(metrics['test']    )       ,		1    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1    )
										self.assertEqual(len(metrics['val']    )       ,		lowerCAmelCase__    )









import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				lowerCAmelCase__       :	Dict     				='platform'

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def 		a__       (							A__,	A__,	A__=None,	A__=None							):
			if attention_mask is None:
						SCREAMING_SNAKE_CASE_						:			Tuple       						= np.where(input_ids != config.pad_token_id,	1,	0							)
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}







@require_flax
class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=1_3       ,		lowerCAmelCase__=7       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=9_9       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=4       ,		lowerCAmelCase__=4       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=2_0       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=0.02       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= parent
										SCREAMING_SNAKE_CASE_						:			List[str]       						= batch_size
										SCREAMING_SNAKE_CASE_						:			str       						= seq_length
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= is_training
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= use_labels
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= vocab_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= hidden_size
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= num_hidden_layers
										SCREAMING_SNAKE_CASE_						:			Dict       						= num_attention_heads
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= intermediate_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Dict       						= hidden_dropout_prob
										SCREAMING_SNAKE_CASE_						:			int       						= attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= max_position_embeddings
										SCREAMING_SNAKE_CASE_						:			Tuple       						= eos_token_id
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= pad_token_id
										SCREAMING_SNAKE_CASE_						:			str       						= bos_token_id
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= embed_dim
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= word_embed_proj_dim
										SCREAMING_SNAKE_CASE_						:			Tuple       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= False






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,		self.vocab_size    )       ,		3       ,		self.vocab_size    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,		dtype=np.intaa    ))       ,		-1    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= OPTConfig(
										    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		num_attention_heads=self.num_attention_heads       ,		ffn_dim=self.intermediate_size       ,		dropout=self.hidden_dropout_prob       ,		attention_dropout=self.attention_probs_dropout_prob       ,		max_position_embeddings=self.max_position_embeddings       ,		eos_token_id=self.eos_token_id       ,		bos_token_id=self.bos_token_id       ,		pad_token_id=self.pad_token_id       ,		embed_dim=self.embed_dim       ,		is_encoder_decoder=lowerCAmelCase__       ,		word_embed_proj_dim=self.word_embed_proj_dim       ,		initializer_range=self.initializer_range       ,		use_cache=lowerCAmelCase__       ,		)
										SCREAMING_SNAKE_CASE_						:			Dict       						= prepare_opt_inputs_dict(lowerCAmelCase__       ,		lowerCAmelCase__    )
										return config, inputs_dict






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= self.prepare_config_and_inputs()
										return config, inputs_dict






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 2_0
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model_class_name(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Tuple       						= inputs_dict['input_ids']
										SCREAMING_SNAKE_CASE_						:			Dict       						= inputs_dict['attention_mask']

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.init_cache(input_ids.shape[0]       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= jnp.ones((input_ids.shape[0], max_length)       ,		dtype='i4'    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1    )[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)       ,		)
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(
										    input_ids[:, :-1]       ,		attention_mask=lowerCAmelCase__       ,		past_key_values=lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype='i4'    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(
										    input_ids[:, -1:]       ,		attention_mask=lowerCAmelCase__       ,		past_key_values=outputs_cache.past_key_values       ,		position_ids=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 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  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 2_0
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model_class_name(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= (
										    inputs_dict['input_ids'],
										    inputs_dict['attention_mask'],
										)

										SCREAMING_SNAKE_CASE_						:			Any       						= jnp.concatenate(
										    [
										        attention_mask,
										        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])    ),
										    ]       ,		axis=-1       ,		)

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model.init_cache(input_ids.shape[0]       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1    )[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)       ,		)

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(
										    input_ids[:, :-1]       ,		attention_mask=lowerCAmelCase__       ,		past_key_values=lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		)
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype='i4'    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(
										    input_ids[:, -1:]       ,		past_key_values=outputs_cache.past_key_values       ,		attention_mask=lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Dict       						= model(lowerCAmelCase__       ,		attention_mask=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Any       						= 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      __lowercase  (__SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ,    __SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							_UpperCAmelCase     =		() if is_flax_available() else ()






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= FlaxOPTModelTester(self    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Dict       						= self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			str       						= model_class_name.from_pretrained('facebook/opt-125m'    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= np.ones((1, 1)    ) * model.config.eos_token_id
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(lowerCAmelCase__    )
													self.assertIsNotNone(lowerCAmelCase__    )







@require_sentencepiece
@require_flax
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= FlaxOPTModel.from_pretrained('facebook/opt-350m'    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(input_ids=lowerCAmelCase__    ).last_hidden_state
										SCREAMING_SNAKE_CASE_						:			int       						= (1, 1_1, 5_1_2)
										self.assertEqual(output.shape       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= jnp.array(
										    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]    )
										self.assertTrue(jnp.allclose(output[:, :3, :3]       ,		lowerCAmelCase__       ,		atol=4E-2    )    )







@require_flax
@slow
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										super().setUp()
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 'facebook/opt-350m'






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= FlaxOPTForCausalLM.from_pretrained(self.path_model    )
										SCREAMING_SNAKE_CASE_						:			int       						= GPTaTokenizer.from_pretrained(self.path_model    )

										SCREAMING_SNAKE_CASE_						:			int       						= [
										    'Today is a beautiful day and I want to',
										    'In the city of',
										    'Paris is the capital of France and',
										    'Computers and mobile phones have taken',
										]
										# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
										SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'       ,		padding=lowerCAmelCase__       ,		add_special_tokens=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= model(inputs.input_ids       ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
										SCREAMING_SNAKE_CASE_						:			Any       						= jnp.array(
										    [
										        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
										        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
										        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
										        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
										    ]    )
										self.assertTrue(jnp.allclose(lowerCAmelCase__       ,		lowerCAmelCase__       ,		atol=4E-2    )    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= jax.jit(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= model(inputs.input_ids       ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
										self.assertTrue(jnp.allclose(lowerCAmelCase__       ,		lowerCAmelCase__       ,		atol=4E-2    )    )







@require_flax
@slow
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return [
										    "Today is a beautiful day and I want",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 'facebook/opt-125m'

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= [
										    'Today is a beautiful day and I want to',
										    'In the city of New York, the city',
										    'Paris is the capital of France and the capital',
										    'Computers and mobile phones have taken over the',
										]

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= []

										SCREAMING_SNAKE_CASE_						:			int       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )

										for prompt in self.prompts:
													SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'    ).input_ids

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.generate(lowerCAmelCase__       ,		max_length=1_0    )
													SCREAMING_SNAKE_CASE_						:			int       						= generated_ids[0]

													SCREAMING_SNAKE_CASE_						:			int       						= tokenizer.batch_decode(lowerCAmelCase__       ,		skip_special_tokens=lowerCAmelCase__    )
													predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= 'facebook/opt-350m'

										SCREAMING_SNAKE_CASE_						:			List[str]       						= [
										    'Today is a beautiful day and I want to',
										    'In the city of San Francisco, the city',
										    'Paris is the capital of France and the capital',
										    'Computers and mobile phones have taken over the',
										]

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= []
										SCREAMING_SNAKE_CASE_						:			List[str]       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )

										for prompt in self.prompts:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'    ).input_ids

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.generate(lowerCAmelCase__       ,		max_length=1_0    )
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= generated_ids[0]

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= tokenizer.batch_decode(lowerCAmelCase__       ,		skip_special_tokens=lowerCAmelCase__    )
													predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= 'facebook/opt-125m'
										SCREAMING_SNAKE_CASE_						:			str       						= [
										    'Today is a beautiful day and I want to thank',
										    'In the city of Rome Canaver Canaver Canaver Canaver',
										]
										SCREAMING_SNAKE_CASE_						:			Any       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= tokenizer(
										    [
										        'Today is a beautiful day and I want to',
										        'In the city of',
										    ]       ,		return_tensors='jax'       ,		padding=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			List[str]       						= jax.jit(model.generate    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= jit_generate(inputs['input_ids']       ,		attention_mask=inputs['attention_mask']    ).sequences

										SCREAMING_SNAKE_CASE_						:			str       						= tokenizer.batch_decode(lowerCAmelCase__       ,		skip_special_tokens=lowerCAmelCase__    )

										self.assertIsNotNone(lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= 'facebook/opt-350m'

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 'left'

										# use different length sentences to test batching
										SCREAMING_SNAKE_CASE_						:			Dict       						= [
										    'Hello, my dog is a little',
										    'Today, I',
										]

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'       ,		padding=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= inputs['input_ids']

										SCREAMING_SNAKE_CASE_						:			str       						= model.generate(input_ids=lowerCAmelCase__       ,		attention_mask=inputs['attention_mask']       ,		trace=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= tokenizer(sentences[0]       ,		return_tensors='jax'    ).input_ids
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model.generate(input_ids=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
										SCREAMING_SNAKE_CASE_						:			str       						= tokenizer(sentences[1]       ,		return_tensors='jax'    ).input_ids
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model.generate(input_ids=lowerCAmelCase__       ,		max_length=model.config.max_length - num_paddings    )

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= tokenizer.batch_decode(outputs[0]       ,		skip_special_tokens=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			int       						= tokenizer.decode(output_non_padded[0][0]       ,		skip_special_tokens=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= tokenizer.decode(output_padded[0][0]       ,		skip_special_tokens=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= [
										    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
										    'Today, I was in the middle of a conversation with a friend about the',
										]
										self.assertListEqual(lowerCAmelCase__       ,		lowerCAmelCase__    )
										self.assertListEqual(lowerCAmelCase__       ,		[non_padded_sentence, padded_sentence]    )





