


'''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


__magic_name__			=      trt.Logger(trt.Logger.WARNING)
__magic_name__			=      absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__magic_name__			=      logging.getLogger(__name__)

__magic_name__			=      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',
)

__magic_name__			=      parser.parse_args()

if args.tokenizer_name:
     __magic_name__			=      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)

__magic_name__			=      args.per_device_eval_batch_size

__magic_name__			=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__magic_name__			=      True

__magic_name__			=      'temp_engine/bert-fp32.engine'
if args.fpaa:
     __magic_name__			=      'temp_engine/bert-fp16.engine'
if args.inta:
     __magic_name__			=      'temp_engine/bert-int8.engine'

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

__magic_name__			=      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
     __magic_name__			=      [network.get_input(i) for i in range(network.num_inputs)]
     __magic_name__			=      [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

     with builder.create_builder_config() as config:
          __magic_name__			=      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)
          __magic_name__			=      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)
          __magic_name__			=      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      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      List[str]):
      A_						:  str          =				np.asarray(inputs["""input_ids"""]							,						dtype=np.intaa)
      A_						:  int          =				np.asarray(inputs["""attention_mask"""]							,						dtype=np.intaa)
      A_						:  Optional[int]          =				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_						:  List[Any]          =				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_						:  str          =				time.time()
      A_						:  Tuple          =				end_time - start_time
      A_						:  Any          =				(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.
__magic_name__			=      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.
     __magic_name__			=      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.

__magic_name__			=      raw_datasets['validation'].column_names

__magic_name__			=      'question' if 'question' in column_names else column_names[0]
__magic_name__			=      'context' if 'context' in column_names else column_names[1]
__magic_name__			=      'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__magic_name__			=      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}."""
     )

__magic_name__			=      min(args.max_seq_length, tokenizer.model_max_length)




def      lowerCamelCase					(      lowerCamelCase  :      Dict):
      # 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_						:  List[Any]          =				[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_						:  Optional[int]          =				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_						:  List[str]          =				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_						:  Union[str, Any]          =				[]

      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_						:  Any          =				tokenized_examples.sequence_ids(lowerCamelCase)
            A_						:  Tuple          =				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_						:  Union[str, Any]          =				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_						:  Dict          =				[
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
            ]

      return tokenized_examples


__magic_name__			=      raw_datasets['validation']
# Validation Feature Creation
__magic_name__			=      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',
)

__magic_name__			=      default_data_collator

__magic_name__			=      eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__magic_name__			=      DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Optional[Any]="eval"):
      # Post-processing: we match the start logits and end logits to answers in the original context.
      A_						:  Tuple          =				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_						:  Dict          =				[
                {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
            ]
      else:
            A_						:  Union[str, Any]          =				[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


__magic_name__			=      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      lowerCamelCase					(      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.
     __magic_name__			=      [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

     # Allocate output buffer
     __magic_name__			=      cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
     __magic_name__			=      cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
     __magic_name__			=      cuda.mem_alloc(h_outputa.nbytes)
     __magic_name__			=      cuda.mem_alloc(h_outputa.nbytes)

     # Create a stream in which to copy inputs/outputs and run inference.
     __magic_name__			=      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}""")

     __magic_name__			=      0.0
     __magic_name__			=      0
     __magic_name__			=      timeit.default_timer()

     __magic_name__			=      None
     for step, batch in enumerate(eval_dataloader):
          __magic_name__		,     __magic_name__			=      model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
          total_time += infer_time
          niter += 1

          __magic_name__		,     __magic_name__			=      outputs
          __magic_name__			=      torch.tensor(start_logits)
          __magic_name__			=      torch.tensor(end_logits)

          # necessary to pad predictions and labels for being gathered
          __magic_name__			=      accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
          __magic_name__			=      accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

          __magic_name__			=      (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
          __magic_name__			=      logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

     __magic_name__			=      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 * 1_000 / niter))
     logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_000))






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

__magic_name__			=      post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__			=      metric.compute(predictions=prediction.predictions, references=prediction.label_ids)






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




'''simple docstring'''


import unittest

from transformers import is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )



class 	__lowerCAmelCase		:


  '''simple docstring'''







  def __init__(							self :       Optional[int]	,_a :       List[Any]	,_a :       Dict=13	,_a :       List[Any]=7	,_a :       Optional[Any]=True	,_a :       Any=True	,_a :       Optional[int]=True	,_a :       Union[str, Any]=99	,_a :       Union[str, Any]=32	,_a :       List[str]=5	,_a :       List[str]=4	,_a :       Dict=37	,_a :       List[Any]="gelu"	,_a :       int=0.1	,_a :       Optional[int]=0.1	,_a :       Tuple=512	,_a :       Union[str, Any]=16	,_a :       Optional[Any]=2	,_a :       Optional[Any]=0.02	,_a :       Optional[int]=3	,_a :       str=4	,_a :       Optional[Any]=None	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				parent
        A_						:  str          =				batch_size
        A_						:  int          =				seq_length
        A_						:  Union[str, Any]          =				is_training
        A_						:  Optional[Any]          =				use_token_type_ids
        A_						:  int          =				use_labels
        A_						:  Dict          =				vocab_size
        A_						:  List[Any]          =				hidden_size
        A_						:  Tuple          =				num_hidden_layers
        A_						:  Optional[int]          =				num_attention_heads
        A_						:  int          =				intermediate_size
        A_						:  Tuple          =				hidden_act
        A_						:  int          =				hidden_dropout_prob
        A_						:  Dict          =				attention_probs_dropout_prob
        A_						:  Any          =				max_position_embeddings
        A_						:  Optional[Any]          =				type_vocab_size
        A_						:  Tuple          =				type_sequence_label_size
        A_						:  int          =				initializer_range
        A_						:  Optional[Any]          =				num_labels
        A_						:  str          =				num_choices
        A_						:  Optional[Any]          =				scope
        A_						:  List[Any]          =				self.vocab_size - 1






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  List[Any]          =				ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size )

        A_						:  List[Any]          =				None
        if self.use_token_type_ids:
              A_						:  Optional[int]          =				ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size )

        A_						:  int          =				None
        A_						:  str          =				None
        A_						:  Union[str, Any]          =				None
        if self.use_labels:
              A_						:  Optional[int]          =				ids_tensor([self.batch_size]	,self.type_sequence_label_size )
              A_						:  str          =				ids_tensor([self.batch_size, self.seq_length]	,self.num_labels )
              A_						:  Any          =				ids_tensor([self.batch_size]	,self.num_choices )

        A_						:  List[Any]          =				OpenAIGPTConfig(
            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	,pad_token_id=self.pad_token_id	,)

        A_						:  Tuple          =				ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,2 )

        return (
            config,
            input_ids,
            head_mask,
            token_type_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )






  def        _a							(							self :       Optional[int]	,_a :       List[str]	,_a :       str	,_a :       int	,_a :       int	,*_a :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				OpenAIGPTModel(config=_a )
        model.to(_a )
        model.eval()

        A_						:  Optional[int]          =				model(_a	,token_type_ids=_a	,head_mask=_a )
        A_						:  str          =				model(_a	,token_type_ids=_a )
        A_						:  Dict          =				model(_a )

        self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size) )






  def        _a							(							self :       Dict	,_a :       Optional[int]	,_a :       Union[str, Any]	,_a :       Dict	,_a :       List[str]	,*_a :       str ):

        '''simple docstring'''




        A_						:  str          =				OpenAIGPTLMHeadModel(_a )
        model.to(_a )
        model.eval()

        A_						:  Any          =				model(_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.loss.shape	,() )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size) )






  def        _a							(							self :       Any	,_a :       Dict	,_a :       List[Any]	,_a :       Dict	,_a :       Union[str, Any]	,*_a :       str ):

        '''simple docstring'''




        A_						:  Any          =				OpenAIGPTDoubleHeadsModel(_a )
        model.to(_a )
        model.eval()

        A_						:  Optional[int]          =				model(_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.loss.shape	,() )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size) )






  def        _a							(							self :       List[str]	,_a :       str	,_a :       Tuple	,_a :       Dict	,_a :       Tuple	,*_a :       Dict ):

        '''simple docstring'''




        A_						:  List[str]          =				self.num_labels
        A_						:  int          =				OpenAIGPTForSequenceClassification(_a )
        model.to(_a )
        model.eval()

        A_						:  Dict          =				ids_tensor([self.batch_size]	,self.type_sequence_label_size )
        A_						:  Optional[Any]          =				model(_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels) )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.prepare_config_and_inputs()
        (
            (
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					
        )						:  str          =				config_and_inputs
        A_						:  int          =				{
            """input_ids""": input_ids,
            """token_type_ids""": token_type_ids,
            """head_mask""": head_mask,
        }

        return config, inputs_dict







@require_torch
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							(
      (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
      if is_torch_available()
      else ()
  )
  a_  					=							(
      (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
  a_  					=							(
      {
          """feature-extraction""": OpenAIGPTModel,
          """text-classification""": OpenAIGPTForSequenceClassification,
          """text-generation""": OpenAIGPTLMHeadModel,
          """zero-shot""": OpenAIGPTForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )






  def        _a							(							self :       Tuple	,_a :       Optional[int]	,_a :       str	,_a :       List[str]	,_a :       List[str]	,_a :       Any ):

        '''simple docstring'''




        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
              # tiny config could not be created.
              return True

        return False






  def        _a							(							self :       Optional[int]	,_a :       str	,_a :       Dict	,_a :       Optional[int]=False ):

        '''simple docstring'''




        A_						:  Any          =				super()._prepare_for_class(_a	,_a	,return_labels=_a )

        if return_labels:
              if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                    A_						:  Union[str, Any]          =				torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)	,dtype=torch.long	,device=_a	,)
                    A_						:  Any          =				inputs_dict["""labels"""]
                    A_						:  Any          =				inputs_dict["""labels"""]
                    A_						:  Tuple          =				torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices)	,dtype=torch.long	,device=_a	,)
                    A_						:  int          =				torch.zeros(
                        self.model_tester.batch_size	,dtype=torch.long	,device=_a )
        return inputs_dict






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Tuple          =				OpenAIGPTModelTester(self )
        A_						:  Optional[int]          =				ConfigTester(self	,config_class=_a	,n_embd=37 )






  def        _a							(							self :       Any ):

        '''simple docstring'''




        self.config_tester.run_common_tests()






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  int          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*_a )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Dict          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*_a )






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*_a )






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_a )






  @slow
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A_						:  Union[str, Any]          =				OpenAIGPTModel.from_pretrained(_a )
              self.assertIsNotNone(_a )







@require_torch
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  @slow
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Dict          =				OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
        model.to(_a )
        A_						:  Dict          =				torch.tensor([[481, 4735, 544]]	,dtype=torch.long	,device=_a )  # the president is
        A_						:  Dict          =				[
            481,
            4735,
            544,
            246,
            963,
            870,
            762,
            239,
            244,
            40477,
            244,
            249,
            719,
            881,
            487,
            544,
            240,
            244,
            603,
            481,
        ]  # the president is a very good man. " \n " i\'m sure he is, " said the

        A_						:  int          =				model.generate(_a	,do_sample=_a )
        self.assertListEqual(output_ids[0].tolist()	,_a )
