




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
					UpperCamelCase__            =     """platform"""

					import jax
					import jax.numpy as jnp

					from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def       _a (		SCREAMING_SNAKE_CASE_				:							Tuple , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Tuple=None , SCREAMING_SNAKE_CASE_				:							int=None	):
					if attention_mask is None:
										__lowerCAmelCase							  =			np.where(input_ids != config.pad_token_id , 1 , 0	)
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}
@require_flax
class      a__       :






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




											"""simple docstring"""



											__lowerCAmelCase							  =			parent
											__lowerCAmelCase							  =			batch_size
											__lowerCAmelCase							  =			seq_length
											__lowerCAmelCase							  =			is_training
											__lowerCAmelCase							  =			use_labels
											__lowerCAmelCase							  =			vocab_size
											__lowerCAmelCase							  =			hidden_size
											__lowerCAmelCase							  =			num_hidden_layers
											__lowerCAmelCase							  =			num_attention_heads
											__lowerCAmelCase							  =			intermediate_size
											__lowerCAmelCase							  =			hidden_act
											__lowerCAmelCase							  =			hidden_dropout_prob
											__lowerCAmelCase							  =			attention_probs_dropout_prob
											__lowerCAmelCase							  =			max_position_embeddings
											__lowerCAmelCase							  =			eos_token_id
											__lowerCAmelCase							  =			pad_token_id
											__lowerCAmelCase							  =			bos_token_id
											__lowerCAmelCase							  =			embed_dim
											__lowerCAmelCase							  =			word_embed_proj_dim
											__lowerCAmelCase							  =			initializer_range
											__lowerCAmelCase							  =			False






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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

											__lowerCAmelCase							  =			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=_A						,     word_embed_proj_dim=self.word_embed_proj_dim						,     initializer_range=self.initializer_range						,     use_cache=_A						,     )
											__lowerCAmelCase							  =			prepare_opt_inputs_dict(_A						,     _A     )
											return config, inputs_dict






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase  ,				__lowerCAmelCase							  =			self.prepare_config_and_inputs()
											return config, inputs_dict






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			2_0
											__lowerCAmelCase							  =			model_class_name(_A     )

											__lowerCAmelCase							  =			inputs_dict["input_ids"]
											__lowerCAmelCase							  =			inputs_dict["attention_mask"]

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

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

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

											__lowerCAmelCase							  =			model(_A     )

											__lowerCAmelCase							  =			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						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			2_0
											__lowerCAmelCase							  =			model_class_name(_A     )

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

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

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

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

											__lowerCAmelCase							  =			model(_A						,     attention_mask=_A     )

											__lowerCAmelCase							  =			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      a__       (   snake_case__					,							unittest.TestCase					,							snake_case__ ):
						_a				:       Union[str, Any]   =       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
						_a				:       Dict   =       () if is_flax_available() else ()






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			FlaxOPTModelTester(self     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase  ,				__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(_A						,     _A						,     _A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase  ,				__lowerCAmelCase							  =			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(_A						,     _A						,     _A     )






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											for model_class_name in self.all_model_classes:
																__lowerCAmelCase							  =			model_class_name.from_pretrained("facebook/opt-125m"     )
																__lowerCAmelCase							  =			np.ones((1, 1)     ) * model.config.eos_token_id
																__lowerCAmelCase							  =			model(_A     )
																self.assertIsNotNone(_A     )






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






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			FlaxOPTModel.from_pretrained("facebook/opt-350m"     )
											__lowerCAmelCase							  =			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]]     )
											__lowerCAmelCase							  =			model(input_ids=_A     ).last_hidden_state
											__lowerCAmelCase							  =			(1, 1_1, 5_1_2)
											self.assertEqual(output.shape						,     _A     )
											__lowerCAmelCase							  =			jnp.array(
											    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]     )
											self.assertTrue(jnp.allclose(output[:, :3, :3]						,     _A						,     atol=4E-2     )     )






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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											super().setUp()
											__lowerCAmelCase							  =			"facebook/opt-350m"






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			FlaxOPTForCausalLM.from_pretrained(self.path_model     )
											__lowerCAmelCase							  =			GPTaTokenizer.from_pretrained(self.path_model     )

											__lowerCAmelCase							  =			[
											    "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
											__lowerCAmelCase							  =			tokenizer(_A						,     return_tensors="jax"						,     padding=_A						,     add_special_tokens=_A     )
											__lowerCAmelCase							  =			model(inputs.input_ids						,     attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
											__lowerCAmelCase							  =			jnp.array(
											    [
											        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
											        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
											        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
											        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
											    ]     )
											self.assertTrue(jnp.allclose(_A						,     _A						,     atol=4E-2     )     )

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






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






						@property
						def 							__SCREAMING_SNAKE_CASE(  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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"facebook/opt-125m"

											__lowerCAmelCase							  =			[
											    "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",
											]

											__lowerCAmelCase							  =			[]

											__lowerCAmelCase							  =			FlaxOPTForCausalLM.from_pretrained(_A     )
											__lowerCAmelCase							  =			GPTaTokenizer.from_pretrained(_A     )

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

																__lowerCAmelCase							  =			model.generate(_A						,     max_length=1_0     )
																__lowerCAmelCase							  =			generated_ids[0]

																__lowerCAmelCase							  =			tokenizer.batch_decode(_A						,     skip_special_tokens=_A     )
																predicted_outputs += generated_string

											self.assertListEqual(_A						,     _A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"facebook/opt-350m"

											__lowerCAmelCase							  =			[
											    "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",
											]

											__lowerCAmelCase							  =			[]
											__lowerCAmelCase							  =			FlaxOPTForCausalLM.from_pretrained(_A     )
											__lowerCAmelCase							  =			GPTaTokenizer.from_pretrained(_A     )

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

																__lowerCAmelCase							  =			model.generate(_A						,     max_length=1_0     )
																__lowerCAmelCase							  =			generated_ids[0]

																__lowerCAmelCase							  =			tokenizer.batch_decode(_A						,     skip_special_tokens=_A     )
																predicted_outputs += generated_string

											self.assertListEqual(_A						,     _A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"facebook/opt-125m"
											__lowerCAmelCase							  =			[
											    "Today is a beautiful day and I want to thank",
											    "In the city of Rome Canaver Canaver Canaver Canaver",
											]
											__lowerCAmelCase							  =			FlaxOPTForCausalLM.from_pretrained(_A     )
											__lowerCAmelCase							  =			GPTaTokenizer.from_pretrained(_A     )
											__lowerCAmelCase							  =			tokenizer(
											    [
											        "Today is a beautiful day and I want to",
											        "In the city of",
											    ]						,     return_tensors="jax"						,     padding=_A						,     )

											__lowerCAmelCase							  =			jax.jit(model.generate     )

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

											__lowerCAmelCase							  =			tokenizer.batch_decode(_A						,     skip_special_tokens=_A     )

											self.assertIsNotNone(_A						,     _A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"facebook/opt-350m"

											__lowerCAmelCase							  =			GPTaTokenizer.from_pretrained(_A     )
											__lowerCAmelCase							  =			FlaxOPTForCausalLM.from_pretrained(_A     )

											__lowerCAmelCase							  =			"left"

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

											__lowerCAmelCase							  =			tokenizer(_A						,     return_tensors="jax"						,     padding=_A     )
											__lowerCAmelCase							  =			inputs["input_ids"]

											__lowerCAmelCase							  =			model.generate(input_ids=_A						,     attention_mask=inputs["attention_mask"]						,     trace=_A     )

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

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

											__lowerCAmelCase							  =			tokenizer.batch_decode(outputs[0]						,     skip_special_tokens=_A     )
											__lowerCAmelCase							  =			tokenizer.decode(output_non_padded[0][0]						,     skip_special_tokens=_A     )
											__lowerCAmelCase							  =			tokenizer.decode(output_padded[0][0]						,     skip_special_tokens=_A     )

											__lowerCAmelCase							  =			[
											    "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(_A						,     _A     )
											self.assertListEqual(_A						,     [non_padded_sentence, padded_sentence]     )










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


UpperCamelCase__            =     logging.getLogger(__name__)







def       _a (		):
					__lowerCAmelCase							  =			ArgumentParser()
					__lowerCAmelCase  ,				__lowerCAmelCase							  =			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(("-", "--")	):
															parser.add_argument(arg.split("="	)[0]	)

					return parser.parse_args()







def       _a (		):
					__lowerCAmelCase							  =			parse_args()
					__lowerCAmelCase							  =			88_88
					__lowerCAmelCase							  =			int(os.environ["SM_NUM_GPUS"]	)
					__lowerCAmelCase							  =			json.loads(os.environ["SM_HOSTS"]	)
					__lowerCAmelCase							  =			len(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			os.environ["SM_CURRENT_HOST"]
					__lowerCAmelCase							  =			hosts.index(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			"INFO"

					if num_nodes > 1:
										__lowerCAmelCase							  =			F"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]	)}"""
					else:
										__lowerCAmelCase							  =			F"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]	)}"""
					try:
										subprocess.run(SCREAMING_SNAKE_CASE_ , shell=SCREAMING_SNAKE_CASE_	)
					except Exception as e:
										logger.info(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					main()




