


import tempfile
import unittest

import numpy as np

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

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


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

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

if is_torch_available():
		import torch


class 					lowerCAmelCase_ :




						def __init__(	self						:   str   ,     __A						:   Tuple   ,     __A						:   List[str]=14   ,     __A						:   List[str]=7   ,     __A						:   Tuple=True   ,     __A						:   List[str]=True   ,     __A						:   Dict=False   ,     __A						:   Optional[Any]=True   ,     __A						:   Optional[int]=99   ,     __A						:   Union[str, Any]=32   ,     __A						:   str=4   ,     __A						:   Optional[int]=4   ,     __A						:   Union[str, Any]=4   ,     __A						:   Dict=37   ,     __A						:   str="gelu"   ,     __A						:   Any=0.1   ,     __A						:   Optional[int]=0.1   ,     __A						:   Union[str, Any]=512   ,     __A						:   Any=0.02   ,     )      ->List[Any]:






									"""simple docstring"""






									a__ :str						  =    parent
									a__ :int						  =    batch_size
									a__ :Tuple						  =    seq_length
									a__ :Union[str, Any]						  =    is_training
									a__ :Optional[int]						  =    use_input_mask
									a__ :Any						  =    use_token_type_ids
									a__ :Optional[int]						  =    use_labels
									a__ :List[str]						  =    vocab_size
									a__ :Optional[Any]						  =    hidden_size
									a__ :Any						  =    rotary_dim
									a__ :Optional[int]						  =    num_hidden_layers
									a__ :Any						  =    num_attention_heads
									a__ :Dict						  =    intermediate_size
									a__ :Optional[int]						  =    hidden_act
									a__ :List[Any]						  =    hidden_dropout_prob
									a__ :Union[str, Any]						  =    attention_probs_dropout_prob
									a__ :Optional[int]						  =    max_position_embeddings
									a__ :Dict						  =    initializer_range
									a__ :Any						  =    None
									a__ :Union[str, Any]						  =    vocab_size - 1
									a__ :Optional[int]						  =    vocab_size - 1
									a__ :List[Any]						  =    vocab_size - 1




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Tuple:






									"""simple docstring"""






									a__ :List[str]						  =    ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size						)

									a__ :Dict						  =    None
									if self.use_input_mask:
												a__ :Optional[int]						  =    random_attention_mask([self.batch_size, self.seq_length]						)

									a__ :Tuple						  =    GPTJConfig(
									    vocab_size=self.vocab_size   ,     n_embd=self.hidden_size   ,     n_layer=self.num_hidden_layers   ,     n_head=self.num_attention_heads   ,     n_positions=self.max_position_embeddings   ,     use_cache=__A   ,     bos_token_id=self.bos_token_id   ,     eos_token_id=self.eos_token_id   ,     pad_token_id=self.pad_token_id   ,     rotary_dim=self.rotary_dim   ,     )

									return (config, input_ids, input_mask)




						def  _snake_case					(	self						:   List[Any]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :int						  =    self.prepare_config_and_inputs()
									a__  ,  a__  ,  a__ :Union[str, Any]						  =    config_and_inputs
									a__ :Union[str, Any]						  =    {"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict




						def  _snake_case					(	self						:   Any   ,     __A						:   Tuple   ,     __A						:   Dict   ,     __A						:   Optional[Any]   ,     __A						:   Union[str, Any]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Tuple						  =    20
									a__ :Optional[Any]						  =    model_class_name(__A						)

									a__ :Dict						  =    model.init_cache(input_ids.shape[0]   ,     __A						)
									a__ :Any						  =    jnp.ones((input_ids.shape[0], max_decoder_length)   ,     dtype="i4"						)

									a__ :Optional[Any]						  =    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1						)[None, :]   ,     (input_ids.shape[0], input_ids.shape[-1] - 1)						)
									a__ :Optional[int]						  =    model(
									    input_ids[:, :-1]   ,     attention_mask=__A   ,     past_key_values=__A   ,     position_ids=__A   ,     )

									a__ :Union[str, Any]						  =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,     dtype="i4"						)
									a__ :str						  =    model(
									    input_ids[:, -1:]   ,     attention_mask=__A   ,     past_key_values=outputs_cache.past_key_values   ,     position_ids=__A   ,     )

									a__ :Union[str, Any]						  =    model(__A						)

									a__ :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  _snake_case					(	self						:   List[str]   ,     __A						:   Tuple   ,     __A						:   str   ,     __A						:   List[Any]   ,     __A						:   Any						)      ->Tuple:






									"""simple docstring"""






									a__ :Any						  =    20
									a__ :List[Any]						  =    model_class_name(__A						)

									a__ :List[str]						  =    jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]   ,     axis=-1   ,     )

									a__ :Tuple						  =    model.init_cache(input_ids.shape[0]   ,     __A						)
									a__ :str						  =    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1						)[None, :]   ,     (input_ids.shape[0], input_ids.shape[-1] - 1)						)

									a__ :Optional[Any]						  =    model(
									    input_ids[:, :-1]   ,     attention_mask=__A   ,     past_key_values=__A   ,     position_ids=__A   ,     )
									a__ :int						  =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,     dtype="i4"						)
									a__ :Optional[int]						  =    model(
									    input_ids[:, -1:]   ,     past_key_values=outputs_cache.past_key_values   ,     attention_mask=__A   ,     position_ids=__A   ,     )

									a__ :List[str]						  =    model(__A   ,     attention_mask=__A						)

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






@require_flax
class 					lowerCAmelCase_ (       UpperCamelCase__			,UpperCamelCase__			,unittest.TestCase):
						lowerCamelCase_         =     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						lowerCamelCase_         =     (FlaxGPTJForCausalLM,) if is_flax_available() else ()




						def  _snake_case					(	self						:   Any						)      ->int:






									"""simple docstring"""






									a__ :List[Any]						  =    FlaxGPTJModelTester(self						)




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






									"""simple docstring"""






									for model_class_name in self.all_model_classes:
												a__  ,  a__  ,  a__ :Tuple						  =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(__A   ,     __A   ,     __A   ,     __A						)




						def  _snake_case					(	self						:   List[Any]						)      ->Optional[int]:






									"""simple docstring"""






									for model_class_name in self.all_model_classes:
												a__  ,  a__  ,  a__ :Any						  =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    __A   ,     __A   ,     __A   ,     __A						)




						@tooslow
						def  _snake_case					(	self						:   int						)      ->str:






									"""simple docstring"""






									a__ :Optional[int]						  =    GPTaTokenizer.from_pretrained("gpt2"   ,     pad_token="<|endoftext|>"   ,     padding_side="left"						)
									a__ :Optional[int]						  =    tokenizer(["Hello this is a long string", "Hey"]   ,     return_tensors="np"   ,     padding=__A   ,     truncation=__A						)

									a__ :Optional[Any]						  =    FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"						)
									a__ :Any						  =    False
									a__ :int						  =    model.config.eos_token_id

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

									a__ :Any						  =    jit_generate(
									    inputs["input_ids"]   ,     attention_mask=inputs["attention_mask"]   ,     pad_token_id=tokenizer.pad_token_id						).sequences

									a__ :Tuple						  =    tokenizer.batch_decode(__A   ,     skip_special_tokens=__A						)

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

									self.assertListEqual(__A   ,     __A						)




						@is_pt_flax_cross_test
						def  _snake_case					(	self						:   Dict						)      ->str:






									"""simple docstring"""






									a__  ,  a__ :Optional[int]						  =    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__						):
															# prepare inputs
															a__ :List[Any]						  =    self._prepare_for_class(__A   ,     __A						)
															a__ :Dict						  =    {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a__ :List[str]						  =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a__ :Tuple						  =    getattr(__A   ,     __A						)

															a__  ,  a__ :Dict						  =    pt_inputs["input_ids"].shape
															a__ :Any						  =    np.random.randint(0   ,     seq_length - 1   ,     size=(batch_size,)						)
															for batch_idx, start_index in enumerate(__A						):
																		a__ :str						  =    0
																		a__ :Dict						  =    1
																		a__ :Optional[int]						  =    0
																		a__ :Union[str, Any]						  =    1
															a__ :List[str]						  =    pt_model_class(__A						).eval()
															a__ :Tuple						  =    model_class(__A   ,     dtype=jnp.floataa						)

															a__ :int						  =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,     __A						)
															a__ :Dict						  =    fx_state

															with torch.no_grad():
																		a__ :List[str]						  =    pt_model(**__A						).to_tuple()

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																		pt_model.save_pretrained(__A						)
																		a__ :Union[str, Any]						  =    model_class.from_pretrained(__A   ,     from_pt=__A						)

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




						@is_pt_flax_cross_test
						def  _snake_case					(	self						:   Tuple						)      ->List[str]:






									"""simple docstring"""






									a__  ,  a__ :Any						  =    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__						):
															# prepare inputs
															a__ :List[str]						  =    self._prepare_for_class(__A   ,     __A						)
															a__ :List[str]						  =    {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a__ :int						  =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a__ :Optional[int]						  =    getattr(__A   ,     __A						)

															a__ :List[Any]						  =    pt_model_class(__A						).eval()
															a__ :Any						  =    model_class(__A   ,     dtype=jnp.floataa						)

															a__ :Optional[int]						  =    load_flax_weights_in_pytorch_model(__A   ,     fx_model.params						)
															a__  ,  a__ :Optional[Any]						  =    pt_inputs["input_ids"].shape
															a__ :Dict						  =    np.random.randint(0   ,     seq_length - 1   ,     size=(batch_size,)						)
															for batch_idx, start_index in enumerate(__A						):
																		a__ :Optional[Any]						  =    0
																		a__ :Optional[Any]						  =    1
																		a__ :Optional[Any]						  =    0
																		a__ :Optional[int]						  =    1

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

															with torch.no_grad():
																		a__ :List[str]						  =    pt_model(**__A						).to_tuple()

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																		fx_model.save_pretrained(__A						)
																		a__ :str						  =    pt_model_class.from_pretrained(__A   ,     from_flax=__A						)

															with torch.no_grad():
																		a__ :int						  =    pt_model_loaded(**__A						).to_tuple()

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






						@tooslow
						def  _snake_case					(	self						:   str						)      ->Optional[int]:






									"""simple docstring"""






									for model_class_name in self.all_model_classes:
												a__ :List[str]						  =    model_class_name.from_pretrained("EleutherAI/gpt-j-6B"						)
												a__ :List[Any]						  =    model(np.ones((1, 1)						)						)
												self.assertIsNotNone(__A						)






import os
import time

import numpy as np
import onnxruntime as ort


snake_case__			 =						'''1'''
snake_case__			 =						'''0'''
snake_case__			 =						'''1'''

snake_case__			 =						ort.SessionOptions()
snake_case__			 =						ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__			 =						['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__			 =						ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__			 =						ort.RunOptions()

snake_case__			 =						128
snake_case__			 =						1
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
snake_case__			 =						time.time()
snake_case__			 =						2000
snake_case__			 =						{}
for iter in range(max_iters):
		snake_case__			 =						sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))


