






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


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

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__      :			Optional[int]							=		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=3_84,
    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=1_28,
    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""",
)

SCREAMING_SNAKE_CASE__      :			Dict							=		parser.parse_args()

if args.tokenizer_name:
					SCREAMING_SNAKE_CASE__      :			str							=		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)

SCREAMING_SNAKE_CASE__      :			Dict							=		args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE__      :			Tuple							=		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		True

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

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

SCREAMING_SNAKE_CASE__      :			Optional[int]							=		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
					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		[network.get_input(i) for i in range(network.num_inputs)]
					SCREAMING_SNAKE_CASE__      :			Dict							=		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

					with builder.create_builder_config() as config:
										SCREAMING_SNAKE_CASE__      :			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)
										SCREAMING_SNAKE_CASE__      :			Tuple							=		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)
										SCREAMING_SNAKE_CASE__      :			Tuple							=		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      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =			np.asarray(inputs['''input_ids'''],  dtype=np.intaa )
				__magic_name__		:Dict        =			np.asarray(inputs['''attention_mask'''],  dtype=np.intaa )
				__magic_name__		:Union[str, Any]        =			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
				__magic_name__		:str        =			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
				__magic_name__		:Any        =			time.time()
				__magic_name__		:List[Any]        =			end_time - start_time
				__magic_name__		: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.
SCREAMING_SNAKE_CASE__      :			Tuple							=		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.
					SCREAMING_SNAKE_CASE__      :			List[str]							=		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.

SCREAMING_SNAKE_CASE__      :			str							=		raw_datasets["""validation"""].column_names

SCREAMING_SNAKE_CASE__      :			str							=		"""question""" if """question""" in column_names else column_names[0]
SCREAMING_SNAKE_CASE__      :			List[Any]							=		"""context""" if """context""" in column_names else column_names[1]
SCREAMING_SNAKE_CASE__      :			Tuple							=		"""answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE__      :			List[str]							=		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}."
					)

SCREAMING_SNAKE_CASE__      :			int							=		min(args.max_seq_length, tokenizer.model_max_length)

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		: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.
				__magic_name__		: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.
				__magic_name__		:Union[str, Any]        =			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.
				__magic_name__		:Dict        =			[]

				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).
								__magic_name__		:Optional[Any]        =			tokenized_examples.sequence_ids(lowerCAmelCase__ )
								__magic_name__		:Optional[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.
								__magic_name__		:Optional[int]        =			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.
								__magic_name__		:Tuple        =			[
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
								]

				return tokenized_examples


SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		raw_datasets["""validation"""]
# Validation Feature Creation
SCREAMING_SNAKE_CASE__      :			int							=		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""",
)

SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		default_data_collator

SCREAMING_SNAKE_CASE__      :			Optional[int]							=		eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case="eval" ):







				"""simple docstring"""






				__magic_name__		:List[str]        =			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:
								__magic_name__		:Optional[Any]        =			[
								    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
								]
				else:
								__magic_name__		:str        =			[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


SCREAMING_SNAKE_CASE__      :			List[Any]							=		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      __lowercase					( snake_case ):







									"""simple docstring"""






									return trt.volume(engine.get_binding_shape(lowerCAmelCase__ ) ) * engine.get_binding_dtype(lowerCAmelCase__ ).itemsize

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

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

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

					SCREAMING_SNAKE_CASE__      :			Any							=		0.0
					SCREAMING_SNAKE_CASE__      :			Tuple							=		0
					SCREAMING_SNAKE_CASE__      :			Any							=		timeit.default_timer()

					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		None
					for step, batch in enumerate(eval_dataloader):
										SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
										total_time += infer_time
										niter += 1

										SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		outputs
										SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		torch.tensor(start_logits)
										SCREAMING_SNAKE_CASE__      :			Dict							=		torch.tensor(end_logits)

										# necessary to pad predictions and labels for being gathered
										SCREAMING_SNAKE_CASE__      :			int							=		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
										SCREAMING_SNAKE_CASE__      :			int							=		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

										SCREAMING_SNAKE_CASE__      :			List[Any]							=		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
										SCREAMING_SNAKE_CASE__      :			Optional[int]							=		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

					if all_preds is not None:
										SCREAMING_SNAKE_CASE__      :			Optional[int]							=		nested_truncate(all_preds, len(eval_dataset))

					SCREAMING_SNAKE_CASE__      :			Any							=		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 * 10_00 / niter))
					logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 10_00))

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

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

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














import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-classification""",
        """language-modeling""",
        """summarization""",
        """token-classification""",
        """question-answering""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
					import run_clm_flax
					import run_flax_glue
					import run_flax_ner
					import run_mlm_flax
					import run_qa
					import run_summarization_flax
					import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__      :			int							=		logging.getLogger()

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:int        =      argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				__magic_name__		:Tuple        =      parser.parse_args()
				return args.f

def      __lowercase					( snake_case,  snake_case="eval" ):







				"""simple docstring"""






				__magic_name__		:str        =      os.path.join(snake_case,  f'''{split}_results.json''' )
				if os.path.exists(snake_case ):
								with open(snake_case,  '''r''' ) as f:
												return json.load(snake_case )
				raise ValueError(f'''can\'t find {path}''' )


SCREAMING_SNAKE_CASE__      :			Tuple							=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 						lowerCamelCase_				(      lowerCamelCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      F'''
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_glue.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple        =      F'''
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_clm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					1_0_0     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[str]        =      F'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_summarization_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase       ,					split='''test'''     )
												self.assertGreaterEqual(result['''test_rouge1''']       ,					1_0     )
												self.assertGreaterEqual(result['''test_rouge2''']       ,					2     )
												self.assertGreaterEqual(result['''test_rougeL''']       ,					7     )
												self.assertGreaterEqual(result['''test_rougeLsum''']       ,					7     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      F'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_mlm_flax.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					4_2     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[Any]        =      F'''
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_ta_mlm_flax.main()
												__magic_name__		:Dict        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.42     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__magic_name__		:int        =      7 if get_gpu_count() > 1 else 2

								__magic_name__		:str        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:str        =      F'''
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_flax_ner.main()
												__magic_name__		:Any        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					0.3     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:int        =      F'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

								with patch.object(__lowerCAmelCase       ,					'''argv'''       ,					__lowerCAmelCase     ):
												run_qa.main()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					3_0     )
												self.assertGreaterEqual(result['''eval_exact''']       ,					3_0     )






