
"""simple docstring"""



import argparse
import logging
import os
import time
import timeit

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

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


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

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

a					:	Union[str, 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''',
)

a					:	Any							=		parser.parse_args()

if args.tokenizer_name:
 a					:	Tuple							=		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					:	Any							=		args.per_device_eval_batch_size

a					:	Optional[int]							=		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a					:	int							=		True

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

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

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

 with builder.create_builder_config() as config:
  a					:	Optional[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)
  a					:	int							=		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					:	Union[str, 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  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   np.asarray(inputs["""input_ids"""]					,      dtype=np.intaa      )
    _UpperCAmelCase          =   np.asarray(inputs["""attention_mask"""]					,      dtype=np.intaa      )
    _UpperCAmelCase          =   np.asarray(inputs["""token_type_ids"""]					,      dtype=np.intaa      )

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

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

# Padding side determines if we do (question|context) or (context|question).
a					:	str							=		tokenizer.padding_side == '''right'''

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

a					:	List[Any]							=		min(args.max_seq_length, tokenizer.model_max_length)


def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   [q.lstrip() for q in examples[question_column_name]]

    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    _UpperCAmelCase          =   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=_A					,      stride=args.doc_stride					,      return_overflowing_tokens=_A					,      return_offsets_mapping=_A					,      padding="""max_length"""					,      )

    # Since one example might give us several features if it has a long context, we need a map from a feature to
    # its corresponding example. This key gives us just that.
    _UpperCAmelCase          =   tokenized_examples.pop("""overflow_to_sample_mapping"""      )

    # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
    # corresponding example_id and we will store the offset mappings.
    _UpperCAmelCase          =   []

    for i in range(len(tokenized_examples["""input_ids"""]      )      ):
        # Grab the sequence corresponding to that example (to know what is the context and what is the question).
        _UpperCAmelCase          =   tokenized_examples.sequence_ids(_A      )
        _UpperCAmelCase          =   1 if pad_on_right else 0

        # One example can give several spans, this is the index of the example containing this span of text.
        _UpperCAmelCase          =   sample_mapping[i]
        tokenized_examples["example_id"].append(examples["""id"""][sample_index]      )

        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
        # position is part of the context or not.
        _UpperCAmelCase          =   [
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]      )
        ]

    return tokenized_examples


a					:	str							=		raw_datasets['''validation''']
# Validation Feature Creation
a					:	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					:	List[str]							=		default_data_collator

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


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A="eval"      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   postprocess_qa_predictions(
        examples=_A					,      features=_A					,      predictions=_A					,      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=_A					,      )
    # Format the result to the format the metric expects.
    if args.version_2_with_negative:
        _UpperCAmelCase          =   [
            {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
        ]
    else:
        _UpperCAmelCase          =   [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


a					:	Any							=		load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
 # setup for TRT inferrence
 for i in range(len(input_names)):
  context.set_binding_shape(i, INPUT_SHAPE)
 assert context.all_binding_shapes_specified


 def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





     """simple docstring"""

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

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

 # Allocate output buffer
 a					:	Tuple							=		cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
 a					:	Optional[Any]							=		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					:	Tuple							=		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					:	Tuple							=		0.0
 a					:	Any							=		0
 a					:	Tuple							=		timeit.default_timer()

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

  a		, a					:	Any							=		outputs
  a					:	Optional[int]							=		torch.tensor(start_logits)
  a					:	Union[str, Any]							=		torch.tensor(end_logits)

  # necessary to pad predictions and labels for being gathered
  a					:	Tuple							=		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
  a					:	str							=		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

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

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

 a					:	Union[str, 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)

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}")

"""simple docstring"""



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 a					:	str							=		'''platform'''
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 		a_						:
    a      :				List[Any]     =				PegasusConfig
    a      :				Dict     =				{}
    a      :				List[Any]     =				'gelu'







    def __init__(			self			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Tuple=13     ,					__UpperCamelCase			:       Tuple=7     ,					__UpperCamelCase			:       Tuple=True     ,					__UpperCamelCase			:       Any=False     ,					__UpperCamelCase			:       Any=99     ,					__UpperCamelCase			:       Optional[int]=32     ,					__UpperCamelCase			:       Dict=5     ,					__UpperCamelCase			:       List[str]=4     ,					__UpperCamelCase			:       Dict=37     ,					__UpperCamelCase			:       int=0.1     ,					__UpperCamelCase			:       Dict=0.1     ,					__UpperCamelCase			:       Optional[Any]=20     ,					__UpperCamelCase			:       Tuple=2     ,					__UpperCamelCase			:       Optional[int]=1     ,					__UpperCamelCase			:       Tuple=0     ,					)		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   intermediate_size

        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   max_position_embeddings
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   bos_token_id







    def      _snake_case					(			self			:       Union[str, Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length - 1]     ,					self.vocab_size     ).clip(3     ,					self.vocab_size     )
        _UpperCAmelCase          =   np.expand_dims(np.array([self.eos_token_id] * self.batch_size     )     ,					1     )
        _UpperCAmelCase          =   np.concatenate([input_ids, eos_tensor]     ,					axis=1     )

        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size     )

        _UpperCAmelCase          =   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     ,					)
        _UpperCAmelCase          =   prepare_pegasus_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, inputs_dict







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   20
        _UpperCAmelCase          =   model_class_name(__UpperCamelCase     )

        _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCAmelCase          =   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,					dtype="""i4"""     )

        _UpperCAmelCase          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)
        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, :-1]     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					past_key_values=__UpperCamelCase     ,					decoder_position_ids=__UpperCamelCase     ,					)

        _UpperCAmelCase          =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,					dtype="""i4"""     )
        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, -1:]     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					past_key_values=outputs_cache.past_key_values     ,					decoder_position_ids=__UpperCamelCase     ,					)

        _UpperCAmelCase          =   model.decode(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3     ,					msg=f"""Max diff is {diff}"""     )







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   20
        _UpperCAmelCase          =   model_class_name(__UpperCamelCase     )

        _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCAmelCase          =   jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
            ]     ,					axis=-1     ,					)

        _UpperCAmelCase          =   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)

        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, :-1]     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					past_key_values=__UpperCamelCase     ,					decoder_position_ids=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,					dtype="""i4"""     )
        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, -1:]     ,					__UpperCamelCase     ,					past_key_values=outputs_cache.past_key_values     ,					decoder_attention_mask=__UpperCamelCase     ,					decoder_position_ids=__UpperCamelCase     ,					)

        _UpperCAmelCase          =   model.decode(__UpperCamelCase     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     )

        _UpperCAmelCase          =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3     ,					msg=f"""Max diff is {diff}"""     )







def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A=None					,      _A=None					,      )    ->  int:





    """simple docstring"""

    if attention_mask is None:
        _UpperCAmelCase          =   np.not_equal(_A					,      config.pad_token_id      ).astype(np.inta      )
    if decoder_attention_mask is None:
        _UpperCAmelCase          =   np.concatenate(
            [
                np.ones(decoder_input_ids[:, :1].shape					,      dtype=np.inta      ),
                np.not_equal(decoder_input_ids[:, 1:]					,      config.pad_token_id      ).astype(np.inta      ),
            ]					,      axis=-1					,      )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }







@require_flax
class 		a_						( _UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    a      :				Any     =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    a      :				Any     =				True
    a      :				int     =				False
    a      :				Union[str, Any]     =				False
    a      :				Optional[int]     =				False







    def      _snake_case					(			self			:       Union[str, Any]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   FlaxPegasusModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     )		->Union[str, Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def      _snake_case					(			self			:       Optional[int]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                _UpperCAmelCase          =   self._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     )
                _UpperCAmelCase          =   model_class(__UpperCamelCase     )

                @jax.jit
                def encode_jitted(__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       str=None     ,					**__UpperCamelCase			:       int     ):
                    return model.encode(input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     )

                with self.subTest("""JIT Enabled"""     ):
                    _UpperCAmelCase          =   encode_jitted(**__UpperCamelCase     ).to_tuple()

                with self.subTest("""JIT Disabled"""     ):
                    with jax.disable_jit():
                        _UpperCAmelCase          =   encode_jitted(**__UpperCamelCase     ).to_tuple()

                self.assertEqual(len(__UpperCamelCase     )     ,					len(__UpperCamelCase     )     )
                for jitted_output, output in zip(__UpperCamelCase     ,					__UpperCamelCase     ):
                    self.assertEqual(jitted_output.shape     ,					output.shape     )







    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                _UpperCAmelCase          =   model_class(__UpperCamelCase     )
                _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     ,					inputs_dict["""attention_mask"""]     )

                _UpperCAmelCase          =   {
                    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                    """encoder_outputs""": encoder_outputs,
                }

                @jax.jit
                def decode_jitted(__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Tuple     ):
                    return model.decode(
                        decoder_input_ids=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					encoder_outputs=__UpperCamelCase     ,					)

                with self.subTest("""JIT Enabled"""     ):
                    _UpperCAmelCase          =   decode_jitted(**__UpperCamelCase     ).to_tuple()

                with self.subTest("""JIT Disabled"""     ):
                    with jax.disable_jit():
                        _UpperCAmelCase          =   decode_jitted(**__UpperCamelCase     ).to_tuple()

                self.assertEqual(len(__UpperCamelCase     )     ,					len(__UpperCamelCase     )     )
                for jitted_output, output in zip(__UpperCamelCase     ,					__UpperCamelCase     ):
                    self.assertEqual(jitted_output.shape     ,					output.shape     )







    @slow
    def      _snake_case					(			self			:       int     )		->int:






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
            _UpperCAmelCase          =   model_class_name.from_pretrained("""google/pegasus-large"""     ,					from_pt=__UpperCamelCase     )
            _UpperCAmelCase          =   np.ones((1, 1)     )
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







    @slow
    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""     )
        _UpperCAmelCase          =   PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""     )

        _UpperCAmelCase          =   [
            """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
            """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
        ]

        _UpperCAmelCase          =   [
            """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
            """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
        ]

        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""np"""     ,					truncation=__UpperCamelCase     ,					max_length=5_12     ,					padding=__UpperCamelCase     )
        _UpperCAmelCase          =   model.generate(**__UpperCamelCase     ,					num_beams=2     ).sequences
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     ,					skip_special_tokens=__UpperCamelCase     )
        assert tgt_text == decoded