

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_	:			Optional[int]  	=      trt.Logger(trt.Logger.WARNING)
A_	:			Optional[Any]  	=      absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

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

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

if args.tokenizer_name:
	A_	:			List[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)

A_	:			List[str]  	=      args.per_device_eval_batch_size

A_	:			Any  	=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A_	:			Any  	=      True

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

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

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

	with builder.create_builder_config() as config:
		A_	:			List[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)
		A_	:			Optional[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)
		A_	:			Dict  	=      builder.build_engine(network, config)

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

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

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

A_	:			str  	=      raw_datasets['validation'].column_names

A_	:			Optional[Any]  	=      'question' if 'question' in column_names else column_names[0]
A_	:			Tuple  	=      'context' if 'context' in column_names else column_names[1]
A_	:			Union[str, Any]  	=      'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
A_	:			Union[str, Any]  	=      tokenizer.padding_side == 'right'

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

A_	:			List[str]  	=      min(args.max_seq_length, tokenizer.model_max_length)

def 						UpperCamelCase (lowercase_:			List[Any] )					->				Union[str, Any]:
	# 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__							:   int					       =		[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__							:   Dict					       =		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=lowercase_   ,				stride=args.doc_stride   ,				return_overflowing_tokens=lowercase_   ,				return_offsets_mapping=lowercase_   ,				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__							:   Tuple					       =		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__							:   int					       =		[]

	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__							:   Tuple					       =		tokenized_examples.sequence_ids(lowercase_ )
		A__							:   Union[str, Any]					       =		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__							:   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.
		A__							:   Tuple					       =		[
		    (o if sequence_ids[k] == context_index else None)
		    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i] )
		]

	return tokenized_examples


A_	:			Tuple  	=      raw_datasets['validation']
# Validation Feature Creation
A_	:			Optional[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',
)

A_	:			str  	=      default_data_collator

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

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

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


A_	:			Dict  	=      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 (lowercase_:			List[Any] )					->				Any:
		return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize

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

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

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

	A_	:			Dict  	=      0.0
	A_	:			Any  	=      0
	A_	:			List[str]  	=      timeit.default_timer()

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

		A_	,      A_	:			List[Any]  	=      outputs
		A_	:			List[Any]  	=      torch.tensor(start_logits)
		A_	:			Optional[int]  	=      torch.tensor(end_logits)

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

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

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

	A_	:			Optional[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 * 1000 / niter))
	logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))


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

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


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






import unittest

from transformers import AutoTokenizer, FalconConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    FalconForCausalLM,
	    FalconForQuestionAnswering,
	    FalconForSequenceClassification,
	    FalconForTokenClassification,
	    FalconModel,
	)



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=3   ,	A__=7   ,	A__=True   ,	A__=True   ,	A__=False   ,	A__=True   ,	A__=99   ,	A__=32   ,	A__=5   ,	A__=4   ,	A__=37   ,	A__="gelu"   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=16   ,	A__=2   ,	A__=0.0_2   ,	A__=3   ,	A__=4   ,	A__=None   ,	):
						A__							:   List[Any]					       =		parent
						A__							:   List[str]					       =		batch_size
						A__							:   Optional[int]					       =		seq_length
						A__							:   Optional[int]					       =		is_training
						A__							:   Any					       =		use_input_mask
						A__							:   Tuple					       =		use_token_type_ids
						A__							:   str					       =		use_labels
						A__							:   Tuple					       =		vocab_size
						A__							:   Any					       =		hidden_size
						A__							:   List[str]					       =		num_hidden_layers
						A__							:   Optional[int]					       =		num_attention_heads
						A__							:   Optional[Any]					       =		intermediate_size
						A__							:   Optional[Any]					       =		hidden_act
						A__							:   Tuple					       =		hidden_dropout_prob
						A__							:   Union[str, Any]					       =		attention_probs_dropout_prob
						A__							:   List[str]					       =		max_position_embeddings
						A__							:   Union[str, Any]					       =		type_vocab_size
						A__							:   str					       =		type_sequence_label_size
						A__							:   Tuple					       =		initializer_range
						A__							:   Tuple					       =		num_labels
						A__							:   Dict					       =		num_choices
						A__							:   List[str]					       =		scope






					def   __A      (			self      ):
						A__							:   Any					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

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

						A__							:   str					       =		None

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

						A__							:   Dict					       =		self.get_config()

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






					def   __A      (			self      ):
						return FalconConfig(
						    vocab_size=self.vocab_size   ,	hidden_size=self.hidden_size   ,	num_hidden_layers=self.num_hidden_layers   ,	num_attention_heads=self.num_attention_heads   ,	intermediate_size=self.intermediate_size   ,	hidden_act=self.hidden_act   ,	hidden_dropout_prob=self.hidden_dropout_prob   ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,	max_position_embeddings=self.max_position_embeddings   ,	type_vocab_size=self.type_vocab_size   ,	is_decoder=A__   ,	initializer_range=self.initializer_range   ,	pad_token_id=1   ,	new_decoder_architecture=A__   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   List[str]					       =		FalconModel(config=A__      )
						model.to(A__      )
						model.eval()
						A__							:   int					       =		model(A__   ,	attention_mask=A__      )
						A__							:   Union[str, Any]					       =		model(A__      )
						self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Union[str, Any]					       =		True
						A__							:   Union[str, Any]					       =		FalconModel(A__      )
						model.to(A__      )
						model.eval()
						A__							:   Tuple					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	)
						A__							:   Union[str, Any]					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	)
						A__							:   List[str]					       =		model(A__   ,	attention_mask=A__      )
						self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Any					       =		FalconForCausalLM(config=A__      )
						model.to(A__      )
						model.eval()
						A__							:   Tuple					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Optional[Any]					       =		True
						A__							:   Union[str, Any]					       =		True
						A__							:   int					       =		FalconForCausalLM(config=A__      )
						model.to(A__      )
						model.eval()

						# first forward pass
						A__							:   List[Any]					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	use_cache=A__   ,	)
						A__							:   Tuple					       =		outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						A__							:   Tuple					       =		ids_tensor((self.batch_size, 3)   ,	config.vocab_size      )
						A__							:   List[Any]					       =		ids_tensor((self.batch_size, 3)   ,	vocab_size=2      )

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

						A__							:   Optional[int]					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	output_hidden_states=A__   ,	)["""hidden_states"""][0]
						A__							:   Any					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	past_key_values=A__   ,	output_hidden_states=A__   ,	)["""hidden_states"""][0]

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

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

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






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.prepare_config_and_inputs()
						(
						    (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       
						)							:   Tuple					       =		config_and_inputs
						A__							:   List[str]					       =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_torch
class    _a						(__magic_name__		,       __magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	(
					    (
					        FalconModel,
					        FalconForCausalLM,
					        FalconForSequenceClassification,
					        FalconForTokenClassification,
					        FalconForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__:       Tuple  							=	(FalconForCausalLM,) if is_torch_available() else ()
					UpperCAmelCase__:       Optional[int]  							=	(
					    {
					        '''feature-extraction''': FalconModel,
					        '''text-classification''': FalconForSequenceClassification,
					        '''text-generation''': FalconForCausalLM,
					        '''question-answering''': FalconForQuestionAnswering,
					        '''token-classification''': FalconForTokenClassification,
					        '''zero-shot''': FalconForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					UpperCAmelCase__:       str  							=	False
					UpperCAmelCase__:       int  							=	False






					def   __A      (			self      ):
						A__							:   List[Any]					       =		FalconModelTester(self      )
						A__							:   Union[str, Any]					       =		ConfigTester(self   ,	config_class=A__   ,	hidden_size=37      )






					def   __A      (			self      ):
						self.config_tester.run_common_tests()






					def   __A      (			self      ):
						A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__      )






					def   __A      (			self      ):
						A__       ,       *A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs()
						for alibi in [True, False]:
							A__							:   Tuple					       =		alibi
							self.model_tester.create_and_check_model(A__   ,	*A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Optional[int]					       =		3
						A__							:   int					       =		input_dict["""input_ids"""]
						A__							:   int					       =		input_ids.ne(1      ).to(A__      )
						A__							:   Union[str, Any]					       =		ids_tensor([self.model_tester.batch_size]   ,	self.model_tester.type_sequence_label_size      )
						A__							:   Optional[int]					       =		FalconForSequenceClassification(A__      )
						model.to(A__      )
						model.eval()
						A__							:   int					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
						self.assertEqual(result.logits.shape   ,	(self.model_tester.batch_size, self.model_tester.num_labels)      )






					def   __A      (			self      ):
						A__       ,       A__							:   str					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Dict					       =		3
						A__							:   Tuple					       =		"""single_label_classification"""
						A__							:   List[Any]					       =		input_dict["""input_ids"""]
						A__							:   Dict					       =		input_ids.ne(1      ).to(A__      )
						A__							:   Dict					       =		ids_tensor([self.model_tester.batch_size]   ,	self.model_tester.type_sequence_label_size      )
						A__							:   Any					       =		FalconForSequenceClassification(A__      )
						model.to(A__      )
						model.eval()
						A__							:   Any					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
						self.assertEqual(result.logits.shape   ,	(self.model_tester.batch_size, self.model_tester.num_labels)      )






					def   __A      (			self      ):
						A__       ,       A__							:   Optional[Any]					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   List[str]					       =		input_dict["""input_ids"""]
						A__							:   List[str]					       =		FalconForCausalLM(A__      )
						model.to(A__      )
						model.eval()
						A__							:   Any					       =		model(A__   ,	use_cache=A__      )
						A__							:   Any					       =		input_ids.shape[0]
						A__							:   Union[str, Any]					       =		model._convert_to_rw_cache(result.past_key_values      )
						A__							:   int					       =		model._convert_cache_to_standard_format(A__   ,	A__      )
						for layer in range(len(A__      )      ):
							for tensor_idx in range(2      ):
								self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3      )
								self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4      )
								self.assertTrue(
								    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]      )      )






					def   __A      (			self      ):
						A__       ,       A__							:   Dict					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Optional[Any]					       =		3
						A__							:   List[Any]					       =		"""multi_label_classification"""
						A__							:   Tuple					       =		input_dict["""input_ids"""]
						A__							:   List[Any]					       =		input_ids.ne(1      ).to(A__      )
						A__							:   Optional[Any]					       =		ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]   ,	self.model_tester.type_sequence_label_size      ).to(torch.float      )
						A__							:   Optional[int]					       =		FalconForSequenceClassification(A__      )
						model.to(A__      )
						model.eval()
						A__							:   List[Any]					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
						self.assertEqual(result.logits.shape   ,	(self.model_tester.batch_size, self.model_tester.num_labels)      )






					def   __A      (			self      ):
						# Falcon can have different numbers of KV-heads than the number of query heads, so we need
						# to override this test to use the right head counts.
						for model_class in self.all_generative_model_classes:
							A__       ,       A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs_for_common()

							# If it doesn't support cache, pass the test
							if not hasattr(A__   ,	"""use_cache"""      ):
								return

							A__							:   Optional[Any]					       =		model_class(A__      ).to(A__      )
							if "use_cache" not in inputs:
								A__							:   Optional[int]					       =		True
							A__							:   List[Any]					       =		model(**A__      )

							# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
							if "past_key_values" not in outputs:
								return

							A__							:   str					       =		(
							    getattr(A__   ,	"""decoder_layers"""   ,	A__      )
							    or getattr(A__   ,	"""num_decoder_layers"""   ,	A__      )
							    or config.num_hidden_layers
							)
							A__							:   Dict					       =		getattr(A__   ,	"""num_kv_heads"""   ,	config.num_attention_heads      )
							A__							:   List[str]					       =		getattr(A__   ,	"""d_model"""   ,	config.hidden_size      )
							A__							:   Union[str, Any]					       =		embed_dim // num_attention_heads

							A__							:   str					       =		outputs["""past_key_values"""]
							self.assertEqual(len(A__      )   ,	A__      )

							A__       ,       A__							:   int					       =		inputs["""input_ids"""].shape
							for i in range(A__      ):
								if config.new_decoder_architecture:
									A__							:   Any					       =		config.num_attention_heads
								elif config.multi_query:
									A__							:   List[Any]					       =		1
								self.assertEqual(len(past_kv[0]      )   ,	2      )  # K V for the decoder = 2
								self.assertEqual(
								    past_kv[i][0].shape   ,	(batch_size, num_attention_heads, seq_length, per_head_embed_dim)      )
								self.assertEqual(
								    past_kv[i][1].shape   ,	(batch_size, num_attention_heads, seq_length, per_head_embed_dim)      )



@require_torch
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Dict					       =		AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b"""      )
						A__							:   List[Any]					       =		FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b"""      )
						model.eval()
						model.to(A__      )
						A__							:   Optional[int]					       =		tokenizer("""My favorite food is"""   ,	return_tensors="""pt"""      ).to(A__      )

						A__							:   Optional[Any]					       =		(
						    """My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
						)

						A__							:   Any					       =		model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=19      )
						A__							:   Optional[int]					       =		tokenizer.batch_decode(A__      )[0]

						self.assertEqual(A__   ,	A__      )






					@slow
					def   __A      (			self      ):
						# The big models are way too big for the CI, so we use tiny random models that resemble their
						# architectures but with much smaller and fewer layers
						for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
							A__							:   Dict					       =		AutoTokenizer.from_pretrained(A__      )
							A__							:   List[str]					       =		FalconForCausalLM.from_pretrained(A__      )
							model.eval()
							model.to(A__      )
							A__							:   Union[str, Any]					       =		tokenizer("""My favorite food is"""   ,	return_tensors="""pt"""      ).to(A__      )

							# We just test that these run without errors - the models are randomly initialized
							# and so the actual text outputs will be garbage
							model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=4      )
							model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=4      )
							model.generate(**A__   ,	num_beams=2   ,	max_new_tokens=4      )






					@slow
					def   __A      (			self      ):
						# The big models are way too big for the CI, so we use tiny random models that resemble their
						# architectures but with much smaller and fewer layers
						with torch.no_grad():
							for repo in [
							    "Rocketknight1/falcon-rw-1b",
							    "Rocketknight1/tiny-random-falcon-7b",
							    "Rocketknight1/tiny-random-falcon-40b",
							]:
								A__							:   Dict					       =		AutoTokenizer.from_pretrained(A__      )
								A__							:   Any					       =		FalconForCausalLM.from_pretrained(A__      )
								model.eval()
								model.to(device=A__      )
								A__							:   List[str]					       =		tokenizer("""My favorite food is"""   ,	return_tensors="""pt"""      ).to(A__      )

								# Test results are the same with and without cache
								A__							:   Tuple					       =		model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=20   ,	use_cache=A__      )
								A__							:   Optional[Any]					       =		model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=20   ,	use_cache=A__      )
								self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0      )



