



"""simple docstring"""






import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


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

_lowercase				:				List[str]   		=		logging.getLogger(__name__)

_lowercase				:				Optional[Any]   		=		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				:				Optional[int]   		=		parser.parse_args()

if args.tokenizer_name:
		_lowercase				:				Optional[Any]   		=		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				:				List[Any]   		=		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowercase				:				str   		=		True

_lowercase				:				int   		=		"temp_engine/bert-fp32.engine"
if args.fpaa:
		_lowercase				:				Optional[int]   		=		"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				:				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
		_lowercase				:				Union[str, Any]   		=		[network.get_input(i) for i in range(network.num_inputs)]
		_lowercase				:				str   		=		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

		with builder.create_builder_config() as config:
				_lowercase				:				Union[str, Any]   		=		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				:				List[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[str]   		=		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   snake_case__     (	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Optional[int]			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=np.asarray(inputs['''input_ids''']							,	dtype=np.intaa			)
			lowerCamelCase__	:	str				=np.asarray(inputs['''attention_mask''']							,	dtype=np.intaa			)
			lowerCamelCase__	:	Dict				=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
			lowerCamelCase__	:	Optional[int]				=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
			lowerCamelCase__	:	List[Any]				=time.time()
			lowerCamelCase__	:	Tuple				=end_time - start_time
			lowerCamelCase__	:	Optional[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.
_lowercase				:				List[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				:				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.

_lowercase				:				List[Any]   		=		raw_datasets["validation"].column_names

_lowercase				:				str   		=		"question" if "question" in column_names else column_names[0]
_lowercase				:				List[Any]   		=		"context" if "context" in column_names else column_names[1]
_lowercase				:				Optional[int]   		=		"answers" if "answers" in column_names else column_names[2]

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

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






def   snake_case__     (	__lowerCamelCase       :					Dict			):
			"""simple docstring"""
			# Some of the questions have lots of whitespace on the left, which is not useful and will make the
			# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
			# left whitespace
			lowerCamelCase__	:	str				=[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__	:	Any				=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.
			lowerCamelCase__	:	List[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.
			lowerCamelCase__	:	Union[str, Any]				=[]

			for i in range(len(tokenized_examples['''input_ids''']			)			):
						# Grab the sequence corresponding to that example (to know what is the context and what is the question).
						lowerCamelCase__	:	Dict				=tokenized_examples.sequence_ids(__lowerCamelCase			)
						lowerCamelCase__	:	List[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.
						lowerCamelCase__	:	str				=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__	:	List[Any]				=[
						    (o if sequence_ids[k] == context_index else None)
						    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]			)
						]

			return tokenized_examples


_lowercase				:				List[str]   		=		raw_datasets["validation"]
# Validation Feature Creation
_lowercase				:				List[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				:				Optional[Any]   		=		default_data_collator

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






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Any="eval"			):
			"""simple docstring"""
			# Post-processing: we match the start logits and end logits to answers in the original context.
			lowerCamelCase__	:	List[Any]				=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:
						lowerCamelCase__	:	int				=[
						    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
						]
			else:
						lowerCamelCase__	:	Optional[int]				=[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

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


_lowercase				:				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   snake_case__     (	__lowerCamelCase       :					Tuple			):
					"""simple docstring"""
					return trt.volume(engine.get_binding_shape(__lowerCamelCase			)			) * engine.get_binding_dtype(__lowerCamelCase			).itemsize

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

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

		# Create a stream in which to copy inputs/outputs and run inference.
		_lowercase				:				List[str]   		=		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				:				str   		=		0.0
		_lowercase				:				Tuple   		=		0
		_lowercase				:				Optional[Any]   		=		timeit.default_timer()

		_lowercase				:				Optional[Any]   		=		None
		for step, batch in enumerate(eval_dataloader):
				_lowercase		,			_lowercase				:				List[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				:				str   		=		torch.tensor(start_logits)
				_lowercase				:				Dict   		=		torch.tensor(end_logits)

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

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

		if all_preds is not None:
				_lowercase				:				Optional[int]   		=		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				:				List[str]   		=		post_processing_function(eval_examples, eval_dataset, all_preds)
_lowercase				:				Dict   		=		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)

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









"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							BlenderbotSmallConfig
					_a         =							{}
					_a         =							'gelu'



					def __init__( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict=13,						lowerCamelCase							:						Optional[Any]=7,						lowerCamelCase							:						Optional[int]=True,						lowerCamelCase							:						int=False,						lowerCamelCase							:						Union[str, Any]=99,						lowerCamelCase							:						str=32,						lowerCamelCase							:						List[Any]=2,						lowerCamelCase							:						Optional[int]=4,						lowerCamelCase							:						Union[str, Any]=37,						lowerCamelCase							:						str=0.1,						lowerCamelCase							:						Optional[int]=0.1,						lowerCamelCase							:						Optional[Any]=20,						lowerCamelCase							:						int=2,						lowerCamelCase							:						Any=1,						lowerCamelCase							:						Optional[Any]=0,						)->      List[str]:
								lowerCamelCase__	:	Any				=parent
								lowerCamelCase__	:	Dict				=batch_size
								lowerCamelCase__	:	Optional[int]				=seq_length
								lowerCamelCase__	:	Tuple				=is_training
								lowerCamelCase__	:	Dict				=use_labels
								lowerCamelCase__	:	List[Any]				=vocab_size
								lowerCamelCase__	:	str				=hidden_size
								lowerCamelCase__	:	str				=num_hidden_layers
								lowerCamelCase__	:	Union[str, Any]				=num_attention_heads
								lowerCamelCase__	:	Any				=intermediate_size

								lowerCamelCase__	:	Dict				=hidden_dropout_prob
								lowerCamelCase__	:	List[Any]				=attention_probs_dropout_prob
								lowerCamelCase__	:	str				=max_position_embeddings
								lowerCamelCase__	:	Optional[int]				=eos_token_id
								lowerCamelCase__	:	str				=pad_token_id
								lowerCamelCase__	:	Union[str, Any]				=bos_token_id



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	Any				=ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ),						1  )
								lowerCamelCase__	:	Any				=tf.concat([input_ids, eos_tensor],						axis=1  )

								lowerCamelCase__	:	Optional[int]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

								lowerCamelCase__	:	int				=self.config_cls(
								    vocab_size=self.vocab_size,						d_model=self.hidden_size,						encoder_layers=self.num_hidden_layers,						decoder_layers=self.num_hidden_layers,						encoder_attention_heads=self.num_attention_heads,						decoder_attention_heads=self.num_attention_heads,						encoder_ffn_dim=self.intermediate_size,						decoder_ffn_dim=self.intermediate_size,						dropout=self.hidden_dropout_prob,						attention_dropout=self.attention_probs_dropout_prob,						max_position_embeddings=self.max_position_embeddings,						eos_token_ids=[2],						bos_token_id=self.bos_token_id,						pad_token_id=self.pad_token_id,						decoder_start_token_id=self.pad_token_id,						**self.config_updates,						)
								lowerCamelCase__	:	Optional[int]				=prepare_blenderbot_small_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=TFBlenderbotSmallModel(config=lowerCamelCase  ).get_decoder()
								lowerCamelCase__	:	List[Any]				=inputs_dict['''input_ids''']

								lowerCamelCase__	:	Optional[int]				=input_ids[:1, :]
								lowerCamelCase__	:	str				=inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__	:	Union[str, Any]				=inputs_dict['''head_mask''']
								lowerCamelCase__	:	Optional[Any]				=1

								# first forward pass
								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						head_mask=lowerCamelCase,						use_cache=lowerCamelCase  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase__	:	Union[str, Any]				=ids_tensor((self.batch_size, 3),						config.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.cast(ids_tensor((self.batch_size, 3),						2  ),						tf.inta  )

								# append to next input_ids and
								lowerCamelCase__	:	List[str]				=tf.concat([input_ids, next_tokens],						axis=-1  )
								lowerCamelCase__	:	str				=tf.concat([attention_mask, next_attn_mask],						axis=-1  )

								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
								lowerCamelCase__	:	Union[str, Any]				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase  )[0]

								self.parent.assertEqual(next_tokens.shape[1],						output_from_past.shape[1]  )

								# select random slice
								lowerCamelCase__	:	Tuple				=int(ids_tensor((1,),						output_from_past.shape[-1]  )  )
								lowerCamelCase__	:	int				=output_from_no_past[:, -3:, random_slice_idx]
								lowerCamelCase__	:	List[str]				=output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowerCamelCase,						lowerCamelCase,						rtol=1E-3  )






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=None							,	__lowerCamelCase       :					Dict=None							,	__lowerCamelCase       :					Optional[int]=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	List[str]				=tf.cast(tf.math.not_equal(__lowerCamelCase							,	config.pad_token_id			)							,	tf.inta			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	str				=tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape							,	dtype=tf.inta			),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,	config.pad_token_id			)							,	tf.inta			),
						    ]							,	axis=-1							,	)
			if head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	List[str]				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}

@require_tf
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					_a         =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					_a         =							(
					    {
					        'conversational': TFBlenderbotSmallForConditionalGeneration,
					        'feature-extraction': TFBlenderbotSmallModel,
					        'summarization': TFBlenderbotSmallForConditionalGeneration,
					        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
					        'translation': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a         =							True
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      str:
								lowerCamelCase__	:	Tuple				=TFBlenderbotSmallModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								self.config_tester.run_common_tests()




					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase  )





@require_tokenizers
@require_tf
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							[
					    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
					    '  i\'m going to throw up.\nand why is that?'
					]
					_a         =							'facebook/blenderbot_small-90M'



					@cached_property
					def 	snake_case ( self							:						Any  )->      List[Any]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )



					@cached_property
					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	str				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model




					@slow
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Dict				=self.tokenizer(self.src_text,						return_tensors='''tf'''  )
								lowerCamelCase__	:	Any				=self.model.generate(
								    model_inputs.input_ids,						attention_mask=model_inputs.attention_mask,						num_beams=2,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=self.tokenizer.batch_decode(generated_ids.numpy(),						skip_special_tokens=lowerCamelCase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)




