


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCAmelCase__       :	Optional[Any]     				=getLogger(__name__)


lowerCAmelCase__       :	Any     				='cuda' if torch.cuda.is_available() else 'cpu'






def 		a__       (							A__,	A__,	A__,	A__ = 8,	A__ = DEFAULT_DEVICE,	A__=False,	A__="summarization",	A__=None,	**A__,	):
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= Path(A__							).open('w',	encoding='utf-8'							)
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= str(A__							)
			SCREAMING_SNAKE_CASE_						:			Tuple       						= AutoModelForSeqaSeqLM.from_pretrained(A__							).to(A__							)
			if fpaa:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model.half()

			SCREAMING_SNAKE_CASE_						:			Tuple       						= AutoTokenizer.from_pretrained(A__							)
			logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''							)  # if this is wrong, check config.model_type.

			SCREAMING_SNAKE_CASE_						:			Tuple       						= time.time()
			# update config with task specific params
			use_task_specific_params(A__,	A__							)
			if prefix is None:
						SCREAMING_SNAKE_CASE_						:			str       						= prefix or getattr(model.config,	'prefix',	''							) or ''
			for examples_chunk in tqdm(list(chunks(A__,	A__							)							)							):
						SCREAMING_SNAKE_CASE_						:			List[str]       						= [prefix + text for text in examples_chunk]
						SCREAMING_SNAKE_CASE_						:			List[str]       						= tokenizer(A__,	return_tensors='pt',	truncation=A__,	padding='longest'							).to(A__							)
						SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.generate(
						    input_ids=batch.input_ids,	attention_mask=batch.attention_mask,	**A__,	)
						SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer.batch_decode(A__,	skip_special_tokens=A__,	clean_up_tokenization_spaces=A__							)
						for hypothesis in dec:
									fout.write(hypothesis + '\n'							)
									fout.flush()
			fout.close()
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= int(time.time() - start_time							)  # seconds
			SCREAMING_SNAKE_CASE_						:			str       						= len(A__							)
			return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs,	4							)}






def 		a__       (							):
			return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'							)






def 		a__       (							A__=True							):
			SCREAMING_SNAKE_CASE_						:			Any       						= argparse.ArgumentParser()
			parser.add_argument('model_name',	type=A__,	help='like facebook/bart-large-cnn,t5-base, etc.'							)
			parser.add_argument('input_path',	type=A__,	help='like cnn_dm/test.source'							)
			parser.add_argument('save_path',	type=A__,	help='where to save summaries'							)
			parser.add_argument('--reference_path',	type=A__,	required=A__,	help='like cnn_dm/test.target'							)
			parser.add_argument('--score_path',	type=A__,	required=A__,	default='metrics.json',	help='where to save metrics'							)
			parser.add_argument('--device',	type=A__,	required=A__,	default=A__,	help='cuda, cuda:1, cpu etc.'							)
			parser.add_argument(
			    '--prefix',	type=A__,	required=A__,	default=A__,	help='will be added to the begininng of src examples'							)
			parser.add_argument('--task',	type=A__,	default='summarization',	help='used for task_specific_params + metrics'							)
			parser.add_argument('--bs',	type=A__,	default=8,	required=A__,	help='batch size'							)
			parser.add_argument(
			    '--n_obs',	type=A__,	default=-1,	required=A__,	help='How many observations. Defaults to all.'							)
			parser.add_argument('--fp16',	action='store_true'							)
			parser.add_argument('--dump-args',	action='store_true',	help='print the custom hparams with the results'							)
			parser.add_argument(
			    '--info',	nargs='?',	type=A__,	const=datetime_now(),	help=(
			        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
			        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
			    ),	)
			# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
			SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= parser.parse_known_args()
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= parse_numeric_n_bool_cl_kwargs(A__							)
			if parsed_args and verbose:
						print(F'''parsed the following generate kwargs: {parsed_args}'''							)
			with open(args.input_path							) as f:
						SCREAMING_SNAKE_CASE_						:			Dict       						= [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
			if args.n_obs > 0:
						SCREAMING_SNAKE_CASE_						:			Tuple       						= examples[: args.n_obs]
			Path(args.save_path							).parent.mkdir(exist_ok=A__							)
			if args.reference_path is None and Path(args.score_path							).exists():
						warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''							)
			SCREAMING_SNAKE_CASE_						:			List[str]       						= generate_summaries_or_translations(
			    A__,	args.save_path,	args.model_name,	batch_size=args.bs,	device=args.device,	fpaa=args.fpaa,	task=args.task,	prefix=args.prefix,	**A__,	)

			if args.reference_path is None:
						return {}

			# Compute scores
			SCREAMING_SNAKE_CASE_						:			int       						= calculate_bleu if 'translation' in args.task else calculate_rouge
			SCREAMING_SNAKE_CASE_						:			Any       						= [x.rstrip() for x in open(args.save_path							).readlines()]
			SCREAMING_SNAKE_CASE_						:			Any       						= [x.rstrip() for x in open(args.reference_path							).readlines()][: len(A__							)]
			SCREAMING_SNAKE_CASE_						:			dict       						= score_fn(A__,	A__							)
			scores.update(A__							)

			if args.dump_args:
						scores.update(A__							)
			if args.info:
						SCREAMING_SNAKE_CASE_						:			str       						= args.info

			if verbose:
						print(A__							)

			if args.score_path is not None:
						json.dump(A__,	open(args.score_path,	'w'							)							)

			return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)









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]    )





