





'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer






class        UpperCAmelCase     :
 def __init__(    self				:Dict						,	lowercase_				:Tuple						,	lowercase_				:Optional[int]=13						,	lowercase_				:Optional[Any]=7						,	lowercase_				:Tuple=True						,	lowercase_				:Optional[int]=True						,	lowercase_				:Optional[int]=False						,	lowercase_				:int=True						,	lowercase_				:Optional[int]=99						,	lowercase_				:Dict=32						,	lowercase_				:Optional[int]=5						,	lowercase_				:Optional[Any]=4						,	lowercase_				:List[Any]=37						,	lowercase_				:Tuple="gelu"						,	lowercase_				:Optional[int]=0.1						,	lowercase_				:Optional[Any]=0.1						,	lowercase_				:List[str]=5_12						,	lowercase_				:Tuple=16						,	lowercase_				:Union[str, Any]=2						,	lowercase_				:Dict=0.0_2						,	lowercase_				:Union[str, Any]=3						,	lowercase_				:str=4						,	lowercase_				:Optional[int]=None						,	)->							Union[str, Any]:
     A__    =      parent
     A__    =      batch_size
     A__    =      seq_length
     A__    =      is_training
     A__    =      use_input_mask
     A__    =      use_token_type_ids
     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__    =      type_vocab_size
     A__    =      type_sequence_label_size
     A__    =      initializer_range
     A__    =      num_labels
     A__    =      num_choices
     A__    =      scope
 def 					UpperCAmelCase_							(    self				:Dict							)->							Dict:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

     A__    =      None
     if self.use_input_mask:
         A__    =      random_attention_mask([self.batch_size, self.seq_length]							)

     A__    =      None
     if self.use_token_type_ids:
         A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size							)

     A__    =      None
     A__    =      None
     A__    =      None
     if self.use_labels:
         A__    =      ids_tensor([self.batch_size]						,	self.type_sequence_label_size							)
         A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels							)
         A__    =      ids_tensor([self.batch_size]						,	self.num_choices							)

     A__    =      self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Any:
     return LlamaConfig(
         vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	is_decoder=lowercase_						,	initializer_range=self.initializer_range						,	)
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:int						,	lowercase_				:Optional[int]						,	lowercase_				:Dict						,	lowercase_				:Optional[int]						,	lowercase_				:Tuple						,	lowercase_				:int						,	lowercase_				:Any							)->							str:
     A__    =      LlamaModel(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_						,	attention_mask=lowercase_							)
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:Optional[Any]						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:List[str]						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:List[Any]						,	)->							Optional[Any]:
     A__    =      True
     A__    =      LlamaModel(lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(
         lowercase_						,	attention_mask=lowercase_						,	encoder_hidden_states=lowercase_						,	encoder_attention_mask=lowercase_						,	)
     A__    =      model(
         lowercase_						,	attention_mask=lowercase_						,	encoder_hidden_states=lowercase_						,	)
     A__    =      model(lowercase_						,	attention_mask=lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Any						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:str						,	lowercase_				:Optional[int]						,	lowercase_				:Tuple						,	lowercase_				:int						,	lowercase_				:Tuple						,	lowercase_				:str						,	)->							str:
     A__    =      LlamaForCausalLM(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_						,	attention_mask=lowercase_						,	labels=lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Tuple						,	lowercase_				:List[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:List[Any]						,	lowercase_				:List[str]						,	lowercase_				:List[Any]						,	lowercase_				:List[Any]						,	)->							Optional[int]:
     A__    =      True
     A__    =      True
     A__    =      LlamaForCausalLM(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()

     # first forward pass
     A__    =      model(
         lowercase_						,	attention_mask=lowercase_						,	encoder_hidden_states=lowercase_						,	encoder_attention_mask=lowercase_						,	use_cache=lowercase_						,	)
     A__    =      outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     A__    =      ids_tensor((self.batch_size, 3)						,	config.vocab_size							)
     A__    =      ids_tensor((self.batch_size, 3)						,	vocab_size=2							)

     # append to next input_ids and
     A__    =      torch.cat([input_ids, next_tokens]						,	dim=-1							)
     A__    =      torch.cat([input_mask, next_mask]						,	dim=-1							)

     A__    =      model(
         lowercase_						,	attention_mask=lowercase_						,	encoder_hidden_states=lowercase_						,	encoder_attention_mask=lowercase_						,	output_hidden_states=lowercase_						,	)["hidden_states"][0]
     A__    =      model(
         lowercase_						,	attention_mask=lowercase_						,	encoder_hidden_states=lowercase_						,	encoder_attention_mask=lowercase_						,	past_key_values=lowercase_						,	output_hidden_states=lowercase_						,	)["hidden_states"][0]

     # select random slice
     A__    =      ids_tensor((1,)						,	output_from_past.shape[-1]							).item()
     A__    =      output_from_no_past[:, -3:, random_slice_idx].detach()
     A__    =      output_from_past[:, :, random_slice_idx].detach()

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(lowercase_						,	lowercase_						,	atol=1E-3							)							)



 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      self.prepare_config_and_inputs()
     (
         (
         A__
     ),     (
         A__
     ),     (
         A__
     ),     (
         A__
     ),     (
         A__
     ),     (
         A__
     ),     (
         A__
     ),     
     )    =      config_and_inputs
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 __lowercase       		=    (LlamaForCausalLM,) if is_torch_available() else ()
 __lowercase       		=    (
     {
         """feature-extraction""": LlamaModel,
         """text-classification""": LlamaForSequenceClassification,
         """text-generation""": LlamaForCausalLM,
         """zero-shot""": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      LlamaModelTester(self							)
     A__    =      ConfigTester(self						,	config_class=lowercase_						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:int							)->							Dict:
     self.config_tester.run_common_tests()
 def 					UpperCAmelCase_							(    self				:Dict							)->							str:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							List[Any]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         A__    =      type
         self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[str]:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
     A__    =      3
     A__    =      input_dict["input_ids"]
     A__    =      input_ids.ne(1							).to(lowercase_							)
     A__    =      ids_tensor([self.model_tester.batch_size]						,	self.model_tester.type_sequence_label_size							)
     A__    =      LlamaForSequenceClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_						,	attention_mask=lowercase_						,	labels=lowercase_							)
     self.assertEqual(result.logits.shape						,	(self.model_tester.batch_size, self.model_tester.num_labels)							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
     A__    =      3
     A__    =      "single_label_classification"
     A__    =      input_dict["input_ids"]
     A__    =      input_ids.ne(1							).to(lowercase_							)
     A__    =      ids_tensor([self.model_tester.batch_size]						,	self.model_tester.type_sequence_label_size							)
     A__    =      LlamaForSequenceClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_						,	attention_mask=lowercase_						,	labels=lowercase_							)
     self.assertEqual(result.logits.shape						,	(self.model_tester.batch_size, self.model_tester.num_labels)							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
     A__    =      3
     A__    =      "multi_label_classification"
     A__    =      input_dict["input_ids"]
     A__    =      input_ids.ne(1							).to(lowercase_							)
     A__    =      ids_tensor(
         [self.model_tester.batch_size, config.num_labels]						,	self.model_tester.type_sequence_label_size							).to(torch.float							)
     A__    =      LlamaForSequenceClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_						,	attention_mask=lowercase_						,	labels=lowercase_							)
     self.assertEqual(result.logits.shape						,	(self.model_tester.batch_size, self.model_tester.num_labels)							)
 @unittest.skip("LLaMA buffers include complex numbers, which breaks this test"							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
     pass



 @parameterized.expand([("linear",), ("dynamic",)]							)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:int							)->							int:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
     A__    =      ids_tensor([1, 10]						,	config.vocab_size							)
     A__    =      ids_tensor([1, int(config.max_position_embeddings * 1.5							)]						,	config.vocab_size							)

     set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
     A__    =      LlamaModel(lowercase_							)
     original_model.to(lowercase_							)
     original_model.eval()
     A__    =      original_model(lowercase_							).last_hidden_state
     A__    =      original_model(lowercase_							).last_hidden_state

     set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
     A__    =      {"type": scaling_type, "factor": 1_0.0}
     A__    =      LlamaModel(lowercase_							)
     scaled_model.to(lowercase_							)
     scaled_model.eval()
     A__    =      scaled_model(lowercase_							).last_hidden_state
     A__    =      scaled_model(lowercase_							).last_hidden_state

     # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
     # maximum sequence length, so the outputs for the short input should match.
     if scaling_type == "dynamic":
         self.assertTrue(torch.allclose(lowercase_						,	lowercase_						,	atol=1E-5							)							)
     else:
         self.assertFalse(torch.allclose(lowercase_						,	lowercase_						,	atol=1E-5							)							)

     # The output should be different for long inputs
     self.assertFalse(torch.allclose(lowercase_						,	lowercase_						,	atol=1E-5							)							)






@require_torch
class        UpperCAmelCase     (	unittest.TestCase    ):
 @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
 @slow
 def 					UpperCAmelCase_							(    self				:str							)->							List[Any]:
     A__    =      [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     A__    =      LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"						,	device_map="auto"							)
     A__    =      model(torch.tensor([input_ids]							)							)
     # Expected mean on dim = -1
     A__    =      torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]							)
     torch.testing.assert_close(out.mean(-1							)						,	lowercase_						,	atol=1E-2						,	rtol=1E-2							)
     # slicing logits[0, 0, 0:30]
     # fmt: off
     A__    =      torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]							)
     # fmt: on
     torch.testing.assert_close(out[0, 0, :30]						,	lowercase_						,	atol=1E-5						,	rtol=1E-5							)
 @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
 @slow
 def 					UpperCAmelCase_							(    self				:int							)->							Optional[Any]:
     A__    =      [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     A__    =      LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"						,	device_map="auto"							)
     A__    =      model(torch.tensor(lowercase_							)							)
     # Expected mean on dim = -1
     A__    =      torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]							)
     torch.testing.assert_close(out.mean(-1							)						,	lowercase_						,	atol=1E-2						,	rtol=1E-2							)
     # slicing logits[0, 0, 0:30]
     # fmt: off
     A__    =      torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]							)
     # fmt: on
     torch.testing.assert_close(out[0, 0, :30]						,	lowercase_						,	atol=1E-5						,	rtol=1E-5							)
 @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[Any]:
     A__    =      [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     A__    =      LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"						,	device_map="auto"							)
     A__    =      model(torch.tensor(lowercase_							)							)
     # Expected mean on dim = -1
     A__    =      torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]							)
     torch.testing.assert_close(out.mean(-1							)						,	lowercase_						,	atol=1E-2						,	rtol=1E-2							)
     # slicing logits[0, 0, 0:30]
     # fmt: off
     A__    =      torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]							)
     # fmt: on
     torch.testing.assert_close(out.mean(-1							)						,	lowercase_						,	atol=1E-2						,	rtol=1E-2							)
 @unittest.skip(
     "Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"							)
 @slow
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
     A__    =      LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"						,	device_map="auto"							)
     A__    =      model(torch.tensor(lowercase_							)							)

     A__    =      torch.tensor(
         [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]						,	dtype=torch.floataa							)
     torch.testing.assert_close(out.mean(-1							)						,	lowercase_						,	atol=1E-2						,	rtol=1E-2							)
     # fmt: off
     A__    =      torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]							)
     # fmt: on
     torch.testing.assert_close(out[0, 0, :30]						,	lowercase_						,	atol=1E-5						,	rtol=1E-5							)



 @unittest.skip("Model is curently gated"							)
 @slow
 def 					UpperCAmelCase_							(    self				:List[str]							)->							List[Any]:
     A__    =      "Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
     A__    =      "Simply put, the theory of relativity states that "
     A__    =      LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"							)
     A__    =      tokenizer.encode(lowercase_						,	return_tensors="pt"							)
     A__    =      LlamaForCausalLM.from_pretrained(
         "meta-llama/Llama-2-13b-chat-hf"						,	device_map="sequential"						,	use_safetensors=lowercase_							)

     # greedy generation outputs
     A__    =      model.generate(lowercase_						,	max_new_tokens=64						,	top_p=lowercase_						,	temperature=1						,	do_sample=lowercase_							)
     A__    =      tokenizer.decode(generated_ids[0]						,	skip_special_tokens=lowercase_							)
     self.assertEqual(lowercase_						,	lowercase_							)










'''simple docstring'''

import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__lowerCAmelCase							:     Tuple		     =trt.Logger(trt.Logger.WARNING)
__lowerCAmelCase							:     Optional[Any]		     =absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__lowerCAmelCase							:     List[Any]		     =logging.getLogger(__name__)

__lowerCAmelCase							:     Optional[Any]		     =argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=384,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=128,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=20,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=30,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

__lowerCAmelCase							:     Tuple		     =parser.parse_args()

if args.tokenizer_name:
  __lowerCAmelCase							:     int		     =AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
  raise ValueError(
      "You are instantiating a new tokenizer from scratch. This is not supported by this script."
      "You can do it from another script, save it, and load it from here, using --tokenizer_name."
  )

logger.info("Training/evaluation parameters %s", args)

__lowerCAmelCase							:     Union[str, Any]		     =args.per_device_eval_batch_size

__lowerCAmelCase							:     List[Any]		     =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCAmelCase							:     Tuple		     =True

__lowerCAmelCase							:     int		     ="temp_engine/bert-fp32.engine"
if args.fpaa:
  __lowerCAmelCase							:     Tuple		     ="temp_engine/bert-fp16.engine"
if args.inta:
  __lowerCAmelCase							:     Optional[int]		     ="temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
  os.makedirs("temp_engine")

__lowerCAmelCase							:     Tuple		     =1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
  with open(args.onnx_model_path, "rb") as model:
    if not parser.parse(model.read()):
      for error in range(parser.num_errors):
        print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
  __lowerCAmelCase							:     Optional[Any]		     =[network.get_input(i) for i in range(network.num_inputs)]
  __lowerCAmelCase							:     Any		     =[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

  with builder.create_builder_config() as config:
    __lowerCAmelCase							:     Optional[Any]		     =1 << 50
    if STRICT_TYPES:
      config.set_flag(trt.BuilderFlag.STRICT_TYPES)
    if args.fpaa:
      config.set_flag(trt.BuilderFlag.FPaa)
    if args.inta:
      config.set_flag(trt.BuilderFlag.INTa)
    __lowerCAmelCase							:     int		     =builder.create_optimization_profile()
    config.add_optimization_profile(profile)
    for i in range(len(input_names)):
      profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
    __lowerCAmelCase							:     Dict		     =builder.build_engine(network, config)

    # serialize_engine and store in file (can be directly loaded and deserialized):
    with open(engine_name, "wb") as f:
      f.write(engine.serialize())





def 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Dict	):
    A__    =      np.asarray(inputs["input_ids"]						,	dtype=np.intaa	)
    A__    =      np.asarray(inputs["attention_mask"]						,	dtype=np.intaa	)
    A__    =      np.asarray(inputs["token_type_ids"]						,	dtype=np.intaa	)

    # Copy inputs
    cuda.memcpy_htod_async(d_inputs[0]						,	input_ids.ravel()						,	_lowerCamelCase	)
    cuda.memcpy_htod_async(d_inputs[1]						,	attention_mask.ravel()						,	_lowerCamelCase	)
    cuda.memcpy_htod_async(d_inputs[2]						,	token_type_ids.ravel()						,	_lowerCamelCase	)
    # start time
    A__    =      time.time()
    # Run inference
    context.execute_async(
        bindings=[int(_lowerCamelCase	) for d_inp in d_inputs] + [int(_lowerCamelCase	), int(_lowerCamelCase	)]						,	stream_handle=stream.handle	)
    # Transfer predictions back from GPU
    cuda.memcpy_dtoh_async(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    cuda.memcpy_dtoh_async(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    # Synchronize the stream and take time
    stream.synchronize()
    # end time
    A__    =      time.time()
    A__    =      end_time - start_time
    A__    =      (h_outputa, h_outputa)
    # print(outputs)
    return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__lowerCAmelCase							:     str		     =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
  datasets.utils.logging.set_verbosity_warning()
  transformers.utils.logging.set_verbosity_info()
else:
  datasets.utils.logging.set_verbosity_error()
  transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
  set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  __lowerCAmelCase							:     List[Any]		     =load_dataset(args.dataset_name, args.dataset_config_name)
else:
  raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__lowerCAmelCase							:     Optional[Any]		     =raw_datasets["validation"].column_names

__lowerCAmelCase							:     Optional[Any]		     ="question" if "question" in column_names else column_names[0]
__lowerCAmelCase							:     str		     ="context" if "context" in column_names else column_names[1]
__lowerCAmelCase							:     Optional[Any]		     ="answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__lowerCAmelCase							:     Any		     =tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
  logger.warning(
      f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
      f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
  )

__lowerCAmelCase							:     Any		     =min(args.max_seq_length, tokenizer.model_max_length)





def 						UpperCamelCase    (							_lowerCamelCase : Optional[int]	):
    # Some of the questions have lots of whitespace on the left, which is not useful and will make the
    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
    # left whitespace
    A__    =      [q.lstrip() for q in examples[question_column_name]]

    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    A__    =      tokenizer(
        examples[question_column_name if pad_on_right else context_column_name]						,	examples[context_column_name if pad_on_right else question_column_name]						,	truncation="only_second" if pad_on_right else "only_first"						,	max_length=_lowerCamelCase						,	stride=args.doc_stride						,	return_overflowing_tokens=_lowerCamelCase						,	return_offsets_mapping=_lowerCamelCase						,	padding="max_length"						,	)

    # Since one example might give us several features if it has a long context, we need a map from a feature to
    # its corresponding example. This key gives us just that.
    A__    =      tokenized_examples.pop("overflow_to_sample_mapping"	)

    # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
    # corresponding example_id and we will store the offset mappings.
    A__    =      []

    for i in range(len(tokenized_examples["input_ids"]	)	):
        # Grab the sequence corresponding to that example (to know what is the context and what is the question).
        A__    =      tokenized_examples.sequence_ids(_lowerCamelCase	)
        A__    =      1 if pad_on_right else 0

        # One example can give several spans, this is the index of the example containing this span of text.
        A__    =      sample_mapping[i]
        tokenized_examples["example_id"].append(examples["id"][sample_index]	)

        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
        # position is part of the context or not.
        A__    =      [
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples["offset_mapping"][i]	)
        ]

    return tokenized_examples


__lowerCAmelCase							:     str		     =raw_datasets["validation"]
# Validation Feature Creation
__lowerCAmelCase							:     Union[str, Any]		     =eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

__lowerCAmelCase							:     List[Any]		     =default_data_collator

__lowerCAmelCase							:     List[Any]		     =eval_dataset.remove_columns(["example_id", "offset_mapping"])
__lowerCAmelCase							:     List[str]		     =DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]="eval"	):
    # Post-processing: we match the start logits and end logits to answers in the original context.
    A__    =      postprocess_qa_predictions(
        examples=_lowerCamelCase						,	features=_lowerCamelCase						,	predictions=_lowerCamelCase						,	version_2_with_negative=args.version_2_with_negative						,	n_best_size=args.n_best_size						,	max_answer_length=args.max_answer_length						,	null_score_diff_threshold=args.null_score_diff_threshold						,	output_dir=args.output_dir						,	prefix=_lowerCamelCase						,	)
    # Format the result to the format the metric expects.
    if args.version_2_with_negative:
        A__    =      [
            {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
        ]
    else:
        A__    =      [{"id": k, "prediction_text": v} for k, v in predictions.items()]

    A__    =      [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
    return EvalPrediction(predictions=_lowerCamelCase						,	label_ids=_lowerCamelCase	)


__lowerCAmelCase							:     Tuple		     =load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
  # setup for TRT inferrence
  for i in range(len(input_names)):
    context.set_binding_shape(i, INPUT_SHAPE)
  assert context.all_binding_shapes_specified





  def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]	):
      return trt.volume(engine.get_binding_shape(_lowerCamelCase	)	) * engine.get_binding_dtype(_lowerCamelCase	).itemsize

  # Allocate device memory for inputs and outputs.
  __lowerCAmelCase							:     Any		     =[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

  # Allocate output buffer
  __lowerCAmelCase							:     List[Any]		     =cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
  __lowerCAmelCase							:     List[str]		     =cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
  __lowerCAmelCase							:     List[str]		     =cuda.mem_alloc(h_outputa.nbytes)
  __lowerCAmelCase							:     int		     =cuda.mem_alloc(h_outputa.nbytes)

  # Create a stream in which to copy inputs/outputs and run inference.
  __lowerCAmelCase							:     Optional[Any]		     =cuda.Stream()

  # Evaluation
  logger.info("***** Running Evaluation *****")
  logger.info(f"""  Num examples = {len(eval_dataset)}""")
  logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

  __lowerCAmelCase							:     str		     =0.0
  __lowerCAmelCase							:     Tuple		     =0
  __lowerCAmelCase							:     List[str]		     =timeit.default_timer()

  __lowerCAmelCase							:     Union[str, Any]		     =None
  for step, batch in enumerate(eval_dataloader):
    __lowerCAmelCase     ,					__lowerCAmelCase							:     Dict		     =model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
    total_time += infer_time
    niter += 1

    __lowerCAmelCase     ,					__lowerCAmelCase							:     List[Any]		     =outputs
    __lowerCAmelCase							:     Tuple		     =torch.tensor(start_logits)
    __lowerCAmelCase							:     Tuple		     =torch.tensor(end_logits)

    # necessary to pad predictions and labels for being gathered
    __lowerCAmelCase							:     Tuple		     =accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
    __lowerCAmelCase							:     Union[str, Any]		     =accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

    __lowerCAmelCase							:     int		     =(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
    __lowerCAmelCase							:     List[Any]		     =logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

  if all_preds is not None:
    __lowerCAmelCase							:     Dict		     =nested_truncate(all_preds, len(eval_dataset))

  __lowerCAmelCase							:     Optional[int]		     =timeit.default_timer() - start_time
  logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
  # Inference time from TRT
  logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1000 / niter))
  logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1000))



  logger.info("Total Number of Inference =  %d", niter)

__lowerCAmelCase							:     Optional[Any]		     =post_processing_function(eval_examples, eval_dataset, all_preds)
__lowerCAmelCase							:     Optional[Any]		     =metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



logger.info(f"""Evaluation metrics: {eval_metric}""")



