




"""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


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

A				=							logging.getLogger(__name__)

A				=							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""",
)

A				=							parser.parse_args()

if args.tokenizer_name:
					A				=							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)

A				=							args.per_device_eval_batch_size

A				=							(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A				=							True

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

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

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

					with builder.create_builder_config() as config:
										A				=							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)
										A				=							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)
										A				=							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     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Dict												=       np.asarray(inputs["input_ids"]					,							dtype=np.intaa				)
			__UpperCAmelCase						:			Tuple												=       np.asarray(inputs["attention_mask"]					,							dtype=np.intaa				)
			__UpperCAmelCase						:			Union[str, Any]												=       np.asarray(inputs["token_type_ids"]					,							dtype=np.intaa				)

			# Copy inputs
			cuda.memcpy_htod_async(d_inputs[0]					,							input_ids.ravel()					,							__UpperCAmelCase				)
			cuda.memcpy_htod_async(d_inputs[1]					,							attention_mask.ravel()					,							__UpperCAmelCase				)
			cuda.memcpy_htod_async(d_inputs[2]					,							token_type_ids.ravel()					,							__UpperCAmelCase				)
			# start time
			__UpperCAmelCase						:			List[str]												=       time.time()
			# Run inference
			context.execute_async(
			    bindings=[int(__UpperCAmelCase				) for d_inp in d_inputs] + [int(__UpperCAmelCase				), int(__UpperCAmelCase				)]					,							stream_handle=stream.handle				)
			# Transfer predictions back from GPU
			cuda.memcpy_dtoh_async(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase				)
			cuda.memcpy_dtoh_async(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase				)
			# Synchronize the stream and take time
			stream.synchronize()
			# end time
			__UpperCAmelCase						:			Union[str, Any]												=       time.time()
			__UpperCAmelCase						:			str												=       end_time - start_time
			__UpperCAmelCase						:			Union[str, 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.
A				=							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.
					A				=							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.

A				=							raw_datasets['validation'].column_names

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

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

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

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			# 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
			__UpperCAmelCase						:			Union[str, 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.
			__UpperCAmelCase						:			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=__UpperCAmelCase					,							stride=args.doc_stride					,							return_overflowing_tokens=__UpperCAmelCase					,							return_offsets_mapping=__UpperCAmelCase					,							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.
			__UpperCAmelCase						:			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.
			__UpperCAmelCase						:			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).
						__UpperCAmelCase						:			Dict												=       tokenized_examples.sequence_ids(__UpperCAmelCase				)
						__UpperCAmelCase						:			int												=       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.
						__UpperCAmelCase						:			List[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.
						__UpperCAmelCase						:			List[Any]												=       [
						    (o if sequence_ids[k] == context_index else None)
						    for k, o in enumerate(tokenized_examples["offset_mapping"][i]				)
						]

			return tokenized_examples


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

A				=							default_data_collator

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

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase="eval"				)      ->      Tuple:


			"""simple docstring"""
			# Post-processing: we match the start logits and end logits to answers in the original context.
			__UpperCAmelCase						:			Tuple												=       postprocess_qa_predictions(
			    examples=__UpperCAmelCase					,							features=__UpperCAmelCase					,							predictions=__UpperCAmelCase					,							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=__UpperCAmelCase					,							)
			# Format the result to the format the metric expects.
			if args.version_2_with_negative:
						__UpperCAmelCase						:			int												=       [
						    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
						]
			else:
						__UpperCAmelCase						:			List[Any]												=       [{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


A				=							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     (      UpperCamelCase				)      ->      Union[str, Any]:


								"""simple docstring"""
								return trt.volume(engine.get_binding_shape(__UpperCAmelCase				)				) * engine.get_binding_dtype(__UpperCAmelCase				).itemsize

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

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

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

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

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

										A				=							outputs
										A				=							torch.tensor(start_logits)
										A				=							torch.tensor(end_logits)

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

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

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

					A				=							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)

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





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








import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 @require_torch
 def  lowerCAmelCase_    (				self    :						int		):
       # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
       # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
       # while running an external program

       # python one-liner segments

       # this must be loaded before socket.socket is monkey-patched
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

       # Force fetching the files so that we can use the cache
       SCREAMING_SNAKE_CASE_      		=     'hf-internal-testing/tiny-random-bert'
       BertConfig.from_pretrained(_lowerCAmelCase		)
       BertModel.from_pretrained(_lowerCAmelCase		)
       BertTokenizer.from_pretrained(_lowerCAmelCase		)
       pipeline(task='fill-mask'							,							model=_lowerCAmelCase		)

       # baseline - just load from_pretrained with normal network
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, run, mock]		)]

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)



 @require_torch
 def  lowerCAmelCase_    (				self    :						Tuple		):
       # python one-liner segments
       # this must be loaded before socket.socket is monkey-patched
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

       # Force fetching the files so that we can use the cache
       SCREAMING_SNAKE_CASE_      		=     'hf-internal-testing/tiny-random-bert'
       BertConfig.from_pretrained(_lowerCAmelCase		)
       BertModel.from_pretrained(_lowerCAmelCase		)
       BertTokenizer.from_pretrained(_lowerCAmelCase		)
       pipeline(task='fill-mask'							,							model=_lowerCAmelCase		)

       # baseline - just load from_pretrained with normal network
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, run, mock]		)]

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)



 @require_torch
 def  lowerCAmelCase_    (				self    :						List[str]		):
       # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
       # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
       # while running an external program

       # python one-liner segments

       # this must be loaded before socket.socket is monkey-patched
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

       # baseline - just load from_pretrained with normal network
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, run]		)]

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)

       # next emulate no network
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, mock, run]		)]

       # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
       # env["TRANSFORMERS_OFFLINE"] = "0"
       # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
       # self.assertEqual(result.returncode, 1, result.stderr)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)



 @require_torch
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import pipeline\n        '
       SCREAMING_SNAKE_CASE_      		=     '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, mock, run]		)]
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							1							,							result.stderr		)
       self.assertIn(
           'You cannot infer task automatically within `pipeline` when using offline mode'							,							result.stderr.decode().replace('\n'							,							''		)							,							)




 @require_torch
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import AutoModel\n        '
       SCREAMING_SNAKE_CASE_      		=     '\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

       # baseline - just load from_pretrained with normal network
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, run]		)]

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)