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


_lowercase:	List[Any]       			=		trt.Logger(trt.Logger.WARNING)
_lowercase:	Dict       			=		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_lowercase:	int       			=		logging.getLogger(__name__)

_lowercase:	List[str]       			=		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_8_4,
    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_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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''',
)

_lowercase:	Union[str, Any]       			=		parser.parse_args()

if args.tokenizer_name:
				_lowercase:	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)

_lowercase:	List[Any]       			=		args.per_device_eval_batch_size

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

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

_lowercase:	Optional[Any]       			=		'''temp_engine/bert-fp32.engine'''
if args.fpaa:
				_lowercase:	List[str]       			=		'''temp_engine/bert-fp16.engine'''
if args.inta:
				_lowercase:	List[str]       			=		'''temp_engine/bert-int8.engine'''

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

_lowercase:	Union[str, Any]       			=		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
				_lowercase:	Dict       			=		[network.get_input(i) for i in range(network.num_inputs)]
				_lowercase:	Any       			=		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								_lowercase:	Optional[int]       			=		1 << 5_0
								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)
								_lowercase:	Any       			=		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)
								_lowercase:	List[Any]       			=		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  (		snake_case     , snake_case     , snake_case     , snake_case     , snake_case     , snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase							=							np.asarray(inputs['input_ids']     , dtype=np.intaa  )
							_lowerCAmelCase							=							np.asarray(inputs['attention_mask']     , dtype=np.intaa  )
							_lowerCAmelCase							=							np.asarray(inputs['token_type_ids']     , dtype=np.intaa  )

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

_lowercase:	Optional[int]       			=		raw_datasets['''validation'''].column_names

_lowercase:	str       			=		'''question''' if '''question''' in column_names else column_names[0]
_lowercase:	Dict       			=		'''context''' if '''context''' in column_names else column_names[1]
_lowercase:	Any       			=		'''answers''' if '''answers''' in column_names else column_names[2]

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

_lowercase:	Optional[Any]       			=		min(args.max_seq_length, tokenizer.model_max_length)






def _lowerCamelCase  (		snake_case  ):
							# 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
							_lowerCAmelCase							=							[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.
							_lowerCAmelCase							=							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=snake_case     , stride=args.doc_stride     , return_overflowing_tokens=snake_case     , return_offsets_mapping=snake_case     , 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.
							_lowerCAmelCase							=							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.
							_lowerCAmelCase							=							[]

							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).
														_lowerCAmelCase							=							tokenized_examples.sequence_ids(snake_case  )
														_lowerCAmelCase							=							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.
														_lowerCAmelCase							=							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.
														_lowerCAmelCase							=							[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples['offset_mapping'][i]  )
														]

							return tokenized_examples


_lowercase:	Any       			=		raw_datasets['''validation''']
# Validation Feature Creation
_lowercase:	str       			=		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''',
)

_lowercase:	Tuple       			=		default_data_collator

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






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case="eval"  ):
							# Post-processing: we match the start logits and end logits to answers in the original context.
							_lowerCAmelCase							=							postprocess_qa_predictions(
							    examples=snake_case     , features=snake_case     , predictions=snake_case     , 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=snake_case     , )
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														_lowerCAmelCase							=							[
														    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
														]
							else:
														_lowerCAmelCase							=							[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

							_lowerCAmelCase							=							[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
							return EvalPrediction(predictions=snake_case     , label_ids=snake_case  )


_lowercase:	Optional[int]       			=		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  (		snake_case  ):
											return trt.volume(engine.get_binding_shape(snake_case  )  ) * engine.get_binding_dtype(snake_case  ).itemsize

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

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

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

				_lowercase:	Dict       			=		0.0
				_lowercase:	Dict       			=		0
				_lowercase:	Dict       			=		timeit.default_timer()

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

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

								# necessary to pad predictions and labels for being gathered
								_lowercase:	Dict       			=		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
								_lowercase:	List[Any]       			=		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

								_lowercase:	Optional[int]       			=		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								_lowercase:	Dict       			=		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

				if all_preds is not None:
								_lowercase:	List[str]       			=		nested_truncate(all_preds, len(eval_dataset))

				_lowercase:	List[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 * 1_0_0_0 / niter))
				logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1_0_0_0))



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

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



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







import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
				    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SqueezeBertForMaskedLM,
				    SqueezeBertForMultipleChoice,
				    SqueezeBertForQuestionAnswering,
				    SqueezeBertForSequenceClassification,
				    SqueezeBertForTokenClassification,
				    SqueezeBertModel,
				)



class        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	Dict  ,  lowercase__	:	Dict  ,  lowercase__	:	Optional[Any]=13  ,  lowercase__	:	Dict=7  ,  lowercase__	:	Dict=True  ,  lowercase__	:	Optional[Any]=True  ,  lowercase__	:	Optional[int]=False  ,  lowercase__	:	Any=True  ,  lowercase__	:	Union[str, Any]=99  ,  lowercase__	:	Optional[int]=32  ,  lowercase__	:	Any=5  ,  lowercase__	:	Any=4  ,  lowercase__	:	List[str]=64  ,  lowercase__	:	Any="gelu"  ,  lowercase__	:	Optional[Any]=0.1  ,  lowercase__	:	List[str]=0.1  ,  lowercase__	:	Dict=5_12  ,  lowercase__	:	List[str]=16  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	str=0.0_2  ,  lowercase__	:	Optional[int]=3  ,  lowercase__	:	Union[str, Any]=4  ,  lowercase__	:	Union[str, Any]=None  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	List[Any]=2  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Union[str, Any]=4  ,  lowercase__	:	Tuple=1  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_input_mask
										_lowerCAmelCase							=							use_token_type_ids
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							type_sequence_label_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							num_labels
										_lowerCAmelCase							=							num_choices
										_lowerCAmelCase							=							scope
										_lowerCAmelCase							=							q_groups
										_lowerCAmelCase							=							k_groups
										_lowerCAmelCase							=							v_groups
										_lowerCAmelCase							=							post_attention_groups
										_lowerCAmelCase							=							intermediate_groups
										_lowerCAmelCase							=							output_groups





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size						)

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

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

										_lowerCAmelCase							=							self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										return SqueezeBertConfig(
										    embedding_size=self.hidden_size  ,  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  ,  attention_probs_dropout_prob=self.hidden_dropout_prob  ,  attention_dropout=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  initializer_range=self.initializer_range  ,  q_groups=self.q_groups  ,  k_groups=self.k_groups  ,  v_groups=self.v_groups  ,  post_attention_groups=self.post_attention_groups  ,  intermediate_groups=self.intermediate_groups  ,  output_groups=self.output_groups  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Dict  ,  lowercase__	:	Any  ,  lowercase__	:	str  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Any  ,  lowercase__	:	Tuple						):
										_lowerCAmelCase							=							SqueezeBertModel(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							SqueezeBertForMaskedLM(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  lowercase__	:	str  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	int						):
										_lowerCAmelCase							=							SqueezeBertForQuestionAnswering(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(
										    lowercase__  ,  attention_mask=lowercase__  ,  start_positions=lowercase__  ,  end_positions=lowercase__						)
										self.parent.assertEqual(result.start_logits.shape  ,  (self.batch_size, self.seq_length)						)
										self.parent.assertEqual(result.end_logits.shape  ,  (self.batch_size, self.seq_length)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any  ,  lowercase__	:	List[str]  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Tuple						):
										_lowerCAmelCase							=							self.num_labels
										_lowerCAmelCase							=							SqueezeBertForSequenceClassification(lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	str  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							self.num_labels
										_lowerCAmelCase							=							SqueezeBertForTokenClassification(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()

										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any						):
										_lowerCAmelCase							=							self.num_choices
										_lowerCAmelCase							=							SqueezeBertForMultipleChoice(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							input_ids.unsqueeze(1						).expand(-1  ,  self.num_choices  ,  -1						).contiguous()
										_lowerCAmelCase							=							input_mask.unsqueeze(1						).expand(-1  ,  self.num_choices  ,  -1						).contiguous()
										_lowerCAmelCase							=							model(
										    lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__  ,  )
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							self.prepare_config_and_inputs()
										((_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase))							=							config_and_inputs
										_lowerCAmelCase							=							{'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict







@require_torch
class        lowerCamelCase__				(   UpperCAmelCase       ,UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(
			    (
			        SqueezeBertModel,
			        SqueezeBertForMaskedLM,
			        SqueezeBertForMultipleChoice,
			        SqueezeBertForQuestionAnswering,
			        SqueezeBertForSequenceClassification,
			        SqueezeBertForTokenClassification,
			    )
			    if is_torch_available()
			    else None
			)
			UpperCamelCase__    							=(
			    {
			        "feature-extraction": SqueezeBertModel,
			        "fill-mask": SqueezeBertForMaskedLM,
			        "question-answering": SqueezeBertForQuestionAnswering,
			        "text-classification": SqueezeBertForSequenceClassification,
			        "token-classification": SqueezeBertForTokenClassification,
			        "zero-shot": SqueezeBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__    							=False
			UpperCamelCase__    							=True
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							SqueezeBertModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  dim=37						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										self.config_tester.run_common_tests()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_model(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase__						)





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase							=							SqueezeBertModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)







@require_sentencepiece
@require_tokenizers
@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'						)

										_lowerCAmelCase							=							torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]						)
										_lowerCAmelCase							=							model(lowercase__						)[0]
										_lowerCAmelCase							=							torch.Size((1, 3)						)
										self.assertEqual(output.shape  ,  lowercase__						)
										_lowerCAmelCase							=							torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]						)
										self.assertTrue(torch.allclose(lowercase__  ,  lowercase__  ,  atol=1e-4						)						)






