

"""simple docstring"""




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


__A  :					List[str]												=	torch.cuda.is_available()
__A  :					int												=	[' Sam ate lunch today.', 'Sams lunch ingredients.']
__A  :					int												=	['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__A  :					Tuple												=	{
    '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-08,
    '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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      "\n".join(lowercase__   )
					Path(lowercase__   ).open("w"   ).writelines(lowercase__   )
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(lowercase__    ,							F"""{split}.source"""   )    ,							lowercase__   )
										_dump_articles(os.path.join(lowercase__    ,							F"""{split}.target"""   )    ,							lowercase__   )
					return tmp_dir





class     __UpperCamelCase				(     _A		):


		@classmethod
		def       SCREAMING_SNAKE_CASE__     (cls				:	Optional[Any]):
							return cls


		@require_torch_multi_gpu
		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
							A    =      {
							    "no_teacher": True,
							    "freeze_encoder": True,
							    "gpus": 2,
							    "overwrite_output_dir": True,
							    "sortish_sampler": True,
							}
							self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE  ,  check_contents=__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Tuple=True):
							A    =      {
							    "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(__SCREAMING_SNAKE_CASE)
							A    =      CHEAP_ARGS.copy()
							A    =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
							A    =      self.get_auto_remove_tmp_dir()
							args_d.update(data_dir=__SCREAMING_SNAKE_CASE  ,  output_dir=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)

							def convert(__SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
												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}"""

							A    =      [x for x in (convert(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE)]
							A    =      [sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
							execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,  env=self.get_env())

							A    =      os.listdir(__SCREAMING_SNAKE_CASE)
							A    =      {os.path.basename(__SCREAMING_SNAKE_CASE) for p in contents}
							A    =      [p for p in contents if p.endswith("ckpt")]
							assert len(__SCREAMING_SNAKE_CASE) > 0

							self.assertIn("test_generations.txt"  ,  __SCREAMING_SNAKE_CASE)
							self.assertIn("test_results.txt"  ,  __SCREAMING_SNAKE_CASE)

							# get the following from the module, (we don't have access to `model` here)
							A    =      os.path.join(__SCREAMING_SNAKE_CASE  ,  "metrics.json")
							A    =      "rouge2"

							A    =      load_json(__SCREAMING_SNAKE_CASE)
							# {'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(__SCREAMING_SNAKE_CASE)
							A    =      metrics["val"][-1]
							self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,  0.0_1)
							self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]  ,  __SCREAMING_SNAKE_CASE)
							self.assertEqual(len(metrics["test"])  ,  1)
							A    =      int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1)
							self.assertEqual(len(metrics["val"])  ,  __SCREAMING_SNAKE_CASE)








"""simple docstring"""




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
	__A  :					Optional[int]												=	'platform'

	import jax
	import jax.numpy as jnp

	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=None   ):
					"""simple docstring"""
					if attention_mask is None:
										A    =      np.where(input_ids != config.pad_token_id    ,							1    ,							0   )
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}





@require_flax
class     __UpperCamelCase				:


		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	int=1_3  ,  __SCREAMING_SNAKE_CASE				:	List[str]=7  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=True  ,  __SCREAMING_SNAKE_CASE				:	Dict=False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=9_9  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	List[str]=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=4  ,  __SCREAMING_SNAKE_CASE				:	Any=4  ,  __SCREAMING_SNAKE_CASE				:	str="gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0.1  ,  __SCREAMING_SNAKE_CASE				:	Any=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=2_0  ,  __SCREAMING_SNAKE_CASE				:	Any=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1  ,  __SCREAMING_SNAKE_CASE				:	Any=0  ,  __SCREAMING_SNAKE_CASE				:	str=1_6  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.0_2  ,  ):
							A    =      parent
							A    =      batch_size
							A    =      seq_length
							A    =      is_training
							A    =      use_labels
							A    =      vocab_size
							A    =      hidden_size
							A    =      num_hidden_layers
							A    =      num_attention_heads
							A    =      intermediate_size
							A    =      hidden_act
							A    =      hidden_dropout_prob
							A    =      attention_probs_dropout_prob
							A    =      max_position_embeddings
							A    =      eos_token_id
							A    =      pad_token_id
							A    =      bos_token_id
							A    =      embed_dim
							A    =      word_embed_proj_dim
							A    =      initializer_range
							A    =      False


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

							A    =      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=__SCREAMING_SNAKE_CASE  ,  word_embed_proj_dim=self.word_embed_proj_dim  ,  initializer_range=self.initializer_range  ,  use_cache=__SCREAMING_SNAKE_CASE  ,  )
							A    =      prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							return config, inputs_dict


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    ,					A    =      self.prepare_config_and_inputs()
							return config, inputs_dict


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      2_0
							A    =      model_class_name(__SCREAMING_SNAKE_CASE)

							A    =      inputs_dict["input_ids"]
							A    =      inputs_dict["attention_mask"]

							A    =      model.init_cache(input_ids.shape[0]  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.ones((input_ids.shape[0], max_length)  ,  dtype="i4")

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

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

							A    =      model(__SCREAMING_SNAKE_CASE)

							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      2_0
							A    =      model_class_name(__SCREAMING_SNAKE_CASE)

							A    ,					A    =      (
							    inputs_dict["input_ids"],
							    inputs_dict["attention_mask"],
							)

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

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

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

							A    =      model(__SCREAMING_SNAKE_CASE  ,  attention_mask=__SCREAMING_SNAKE_CASE)

							A    =      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     __UpperCamelCase				(     _A  ,	unittest.TestCase  ,	_A		):
		SCREAMING_SNAKE_CASE              =					(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		SCREAMING_SNAKE_CASE              =					() if is_flax_available() else ()


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    =      FlaxOPTModelTester(self)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    ,					A    =      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							A    ,					A    =      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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		@slow
		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
							for model_class_name in self.all_model_classes:
												A    =      model_class_name.from_pretrained("facebook/opt-125m")
												A    =      np.ones((1, 1)) * model.config.eos_token_id
												A    =      model(__SCREAMING_SNAKE_CASE)
												self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





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


		@slow
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							A    =      FlaxOPTModel.from_pretrained("facebook/opt-350m")
							A    =      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]])
							A    =      model(input_ids=__SCREAMING_SNAKE_CASE).last_hidden_state
							A    =      (1, 1_1, 5_1_2)
							self.assertEqual(output.shape  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.array(
							    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]])
							self.assertTrue(jnp.allclose(output[:, :3, :3]  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))





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


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							super().setUp()
							A    =      "facebook/opt-350m"


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      FlaxOPTForCausalLM.from_pretrained(self.path_model)
							A    =      GPTaTokenizer.from_pretrained(self.path_model)

							A    =      [
							    "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
							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  add_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
							A    =      jnp.array(
							    [
							        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
							        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
							        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
							        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
							    ])
							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))

							A    =      jax.jit(__SCREAMING_SNAKE_CASE)
							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))





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


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							A    =      "facebook/opt-125m"

							A    =      [
							    "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",
							]

							A    =      []

							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							for prompt in self.prompts:
												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids

												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
												A    =      generated_ids[0]

												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
												predicted_outputs += generated_string

							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
							A    =      "facebook/opt-350m"

							A    =      [
							    "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",
							]

							A    =      []
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							for prompt in self.prompts:
												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids

												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
												A    =      generated_ids[0]

												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
												predicted_outputs += generated_string

							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    =      "facebook/opt-125m"
							A    =      [
							    "Today is a beautiful day and I want to thank",
							    "In the city of Rome Canaver Canaver Canaver Canaver",
							]
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      tokenizer(
							    [
							        "Today is a beautiful day and I want to",
							        "In the city of",
							    ]  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  )

							A    =      jax.jit(model.generate)

							A    =      jit_generate(inputs["input_ids"]  ,  attention_mask=inputs["attention_mask"]).sequences

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

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							A    =      "facebook/opt-350m"

							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)

							A    =      "left"

							# use different length sentences to test batching
							A    =      [
							    "Hello, my dog is a little",
							    "Today, I",
							]

							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE)
							A    =      inputs["input_ids"]

							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  attention_mask=inputs["attention_mask"]  ,  trace=__SCREAMING_SNAKE_CASE)

							A    =      tokenizer(sentences[0]  ,  return_tensors="jax").input_ids
							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE)

							A    =      inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
							A    =      tokenizer(sentences[1]  ,  return_tensors="jax").input_ids
							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  max_length=model.config.max_length - num_paddings)

							A    =      tokenizer.batch_decode(outputs[0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      tokenizer.decode(output_non_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
							A    =      tokenizer.decode(output_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)

							A    =      [
							    "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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  [non_padded_sentence, padded_sentence])





