






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__magic_name__:  Union[str, Any]						=			16
__magic_name__:  int						=			32

def 					UpperCamelCase		( _A,					_A = 16				):






						"""simple docstring"""







						__magic_name__    :     str     		=      AutoTokenizer.from_pretrained("""bert-base-cased"""				)
						__magic_name__    :     List[str]     		=      load_dataset("""glue""",					"""mrpc"""				)

						def tokenize_function(_A				):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__    :     Any     		=      tokenizer(examples["""sentence1"""],					examples["""sentence2"""],					truncation=_A,					max_length=_A				)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__magic_name__    :     List[Any]     		=      datasets.map(
												    _A,					batched=_A,					remove_columns=["""idx""", """sentence1""", """sentence2"""],					)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__magic_name__    :     int     		=      tokenized_datasets.rename_column("""label""",					"""labels"""				)

						def collate_fn(_A				):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__    :     str     		=      128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__magic_name__    :     Union[str, Any]     		=      16
												elif accelerator.mixed_precision != "no":
																		__magic_name__    :     Optional[Any]     		=      8
												else:
																		__magic_name__    :     Union[str, Any]     		=      None

												return tokenizer.pad(
												    _A,					padding="""longest""",					max_length=_A,					pad_to_multiple_of=_A,					return_tensors="""pt""",					)

						# Instantiate dataloaders.
						__magic_name__    :     Dict     		=      DataLoader(
						    tokenized_datasets["""train"""],					shuffle=_A,					collate_fn=_A,					batch_size=_A				)
						__magic_name__    :     int     		=      DataLoader(
						    tokenized_datasets["""validation"""],					shuffle=_A,					collate_fn=_A,					batch_size=_A				)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__magic_name__:  Any						=			mocked_dataloaders  # noqa: F811

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						if os.environ.get("""TESTING_MOCKED_DATALOADERS""",					_A				) == "1":
												__magic_name__    :     Tuple     		=      2
						# Initialize accelerator
						__magic_name__    :     Optional[int]     		=      Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision				)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__magic_name__    :     Optional[int]     		=      config["""lr"""]
						__magic_name__    :     Union[str, Any]     		=      int(config["""num_epochs"""]				)
						__magic_name__    :     Dict     		=      int(config["""seed"""]				)
						__magic_name__    :     str     		=      int(config["""batch_size"""]				)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps,					"""isdigit"""				):
												if args.checkpointing_steps == "epoch":
																		__magic_name__    :     str     		=      args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		__magic_name__    :     str     		=      int(args.checkpointing_steps				)
												else:
																		raise ValueError(
																		    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'				)
						else:
												__magic_name__    :     Optional[Any]     		=      None

						set_seed(_A				)

						__magic_name__		,__magic_name__    :     Tuple     		=      get_dataloaders(_A,					_A				)
						__magic_name__    :     Any     		=      evaluate.load("""glue""",					"""mrpc"""				)

						# If the batch size is too big we use gradient accumulation
						__magic_name__    :     List[str]     		=      1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__magic_name__    :     Tuple     		=      batch_size // MAX_GPU_BATCH_SIZE
												__magic_name__    :     Union[str, Any]     		=      MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__magic_name__    :     List[str]     		=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",					return_dict=_A				)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__magic_name__    :     Optional[int]     		=      model.to(accelerator.device				)

						# Instantiate optimizer
						__magic_name__    :     Dict     		=      AdamW(params=model.parameters(),					lr=_A				)

						# Instantiate scheduler
						__magic_name__    :     List[str]     		=      get_linear_schedule_with_warmup(
						    optimizer=_A,					num_warmup_steps=100,					num_training_steps=(len(_A				) * num_epochs) // gradient_accumulation_steps,					)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__magic_name__		,__magic_name__		,__magic_name__		,__magic_name__		,__magic_name__    :     int     		=      accelerator.prepare(
						    _A,					_A,					_A,					_A,					_A				)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						__magic_name__    :     List[Any]     		=      0
						# We also need to keep track of the stating epoch so files are named properly
						__magic_name__    :     Optional[int]     		=      0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'				)
																		accelerator.load_state(args.resume_from_checkpoint				)
																		__magic_name__    :     Dict     		=      os.path.basename(args.resume_from_checkpoint				)
												else:
																		# Get the most recent checkpoint
																		__magic_name__    :     Any     		=      [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
																		dirs.sort(key=os.path.getctime				)
																		__magic_name__    :     int     		=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__magic_name__    :     Optional[Any]     		=      os.path.splitext(_A				)[0]

												if "epoch" in training_difference:
																		__magic_name__    :     List[str]     		=      int(training_difference.replace("""epoch_""",					""""""				)				) + 1
																		__magic_name__    :     Dict     		=      None
												else:
																		__magic_name__    :     Union[str, Any]     		=      int(training_difference.replace("""step_""",					""""""				)				)
																		__magic_name__    :     str     		=      resume_step // len(_A				)
																		resume_step -= starting_epoch * len(_A				)

    # Now we train the model
						for epoch in range(_A,					_A				):
												model.train()
												# New Code #
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		__magic_name__    :     str     		=      accelerator.skip_first_batches(_A,					_A				)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__magic_name__    :     Dict     		=      train_dataloader
												for step, batch in enumerate(_A				):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device				)
																		__magic_name__    :     Any     		=      model(**_A				)
																		__magic_name__    :     Dict     		=      outputs.loss
																		__magic_name__    :     List[str]     		=      loss / gradient_accumulation_steps
																		accelerator.backward(_A				)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(_A,					_A				):
																								__magic_name__    :     Any     		=      f'step_{overall_step}'
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__magic_name__    :     str     		=      os.path.join(args.output_dir,					_A				)
																														accelerator.save_state(_A				)

												model.eval()
												for step, batch in enumerate(_A				):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device				)
																		with torch.no_grad():
																								__magic_name__    :     Union[str, Any]     		=      model(**_A				)
																		__magic_name__    :     Tuple     		=      outputs.logits.argmax(dim=-1				)
																		__magic_name__		,__magic_name__    :     Optional[Any]     		=      accelerator.gather_for_metrics((predictions, batch["""labels"""])				)
																		metric.add_batch(
																		    predictions=_A,					references=_A,					)

												__magic_name__    :     List[Any]     		=      metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:',					_A				)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		__magic_name__    :     Dict     		=      f'epoch_{epoch}'
																		if args.output_dir is not None:
																								__magic_name__    :     Tuple     		=      os.path.join(args.output_dir,					_A				)
																		accelerator.save_state(_A				)

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     int     		=      argparse.ArgumentParser(description="""Simple example of training script."""				)
						parser.add_argument(
						    """--mixed_precision""",					type=_A,					default=_A,					choices=["""no""", """fp16""", """bf16""", """fp8"""],					help="""Whether to use mixed precision. Choose"""
						    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
						    """and an Nvidia Ampere GPU.""",					)
						parser.add_argument("""--cpu""",					action="""store_true""",					help="""If passed, will train on the CPU."""				)
						parser.add_argument(
						    """--checkpointing_steps""",					type=_A,					default=_A,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""",					)
						parser.add_argument(
						    """--output_dir""",					type=_A,					default=""".""",					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""",					)
						parser.add_argument(
						    """--resume_from_checkpoint""",					type=_A,					default=_A,					help="""If the training should continue from a checkpoint folder.""",					)
						__magic_name__    :     List[str]     		=      parser.parse_args()
						__magic_name__    :     Tuple     		=      {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(_A,					_A				)


if __name__ == "__main__":
		main()








import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
		__magic_name__:  List[Any]  	=			"platform"

		import jax
		import jax.numpy as jnp

		from transformers import AutoTokenizer
		from transformers.models.mbart.modeling_flax_mbart import (
		    FlaxMBartForConditionalGeneration,
		    FlaxMBartForQuestionAnswering,
		    FlaxMBartForSequenceClassification,
		    FlaxMBartModel,
		    shift_tokens_right,
		)

def 					UpperCamelCase		( _A,					_A,					_A=None,					_A=None,					_A=None,					_A=None,					_A=None,					_A=None,					):






						"""simple docstring"""







						if attention_mask is None:
												__magic_name__    :     List[Any]     		=      np.where(input_ids != config.pad_token_id,					1,					0				)
						if decoder_attention_mask is None:
												__magic_name__    :     int     		=      np.where(decoder_input_ids != config.pad_token_id,					1,					0				)
						if head_mask is None:
												__magic_name__    :     Tuple     		=      np.ones((config.encoder_layers, config.encoder_attention_heads)				)
						if decoder_head_mask is None:
												__magic_name__    :     Union[str, Any]     		=      np.ones((config.decoder_layers, config.decoder_attention_heads)				)
						if cross_attn_head_mask is None:
												__magic_name__    :     str     		=      np.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,
						}





class  snake_case__      (							unittest.TestCase   ):
	def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=13    ,     lowerCAmelCase__=7    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=99    ,     lowerCAmelCase__=16    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__="gelu"    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=32    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=1    ,     lowerCAmelCase__=0    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=0.0_2    ,     )							->   Any:
							__magic_name__    :     Dict     		=      parent
							__magic_name__    :     int     		=      batch_size
							__magic_name__    :     Union[str, Any]     		=      seq_length
							__magic_name__    :     int     		=      is_training
							__magic_name__    :     Optional[Any]     		=      use_labels
							__magic_name__    :     Tuple     		=      vocab_size
							__magic_name__    :     Union[str, Any]     		=      hidden_size
							__magic_name__    :     Tuple     		=      num_hidden_layers
							__magic_name__    :     Optional[Any]     		=      num_attention_heads
							__magic_name__    :     int     		=      intermediate_size
							__magic_name__    :     Optional[Any]     		=      hidden_act
							__magic_name__    :     List[str]     		=      hidden_dropout_prob
							__magic_name__    :     Union[str, Any]     		=      attention_probs_dropout_prob
							__magic_name__    :     List[Any]     		=      max_position_embeddings
							__magic_name__    :     Tuple     		=      eos_token_id
							__magic_name__    :     str     		=      pad_token_id
							__magic_name__    :     str     		=      bos_token_id
							__magic_name__    :     Optional[int]     		=      decoder_start_token_id
							__magic_name__    :     Tuple     		=      initializer_range
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__    :     Dict     		=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,     self.vocab_size				)    ,     3    ,     self.vocab_size				)
							__magic_name__    :     List[Any]     		=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,     dtype=np.intaa				))    ,     -1				)

							__magic_name__    :     List[str]     		=      shift_tokens_right(lowerCAmelCase__    ,     1				)

							__magic_name__    :     str     		=      MBartConfig(
							    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_id=self.eos_token_id    ,     bos_token_id=self.bos_token_id    ,     pad_token_id=self.pad_token_id    ,     decoder_start_token_id=self.decoder_start_token_id    ,     initializer_range=self.initializer_range    ,     use_cache=lowerCAmelCase__    ,     )
							__magic_name__    :     Dict     		=      prepare_mbart_inputs_dict(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
							return config, inputs_dict
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__		,__magic_name__    :     List[str]     		=      self.prepare_config_and_inputs()
							return config, inputs_dict
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   Optional[Any]:
							__magic_name__    :     Any     		=      20
							__magic_name__    :     Any     		=      model_class_name(lowerCAmelCase__				)

							__magic_name__    :     Union[str, Any]     		=      model.encode(inputs_dict["""input_ids"""]				)

							__magic_name__		,__magic_name__    :     Optional[Any]     		=      (
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

							__magic_name__    :     str     		=      model.init_cache(decoder_input_ids.shape[0]    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
							__magic_name__    :     Optional[int]     		=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,     dtype="""i4"""				)

							__magic_name__    :     Optional[Any]     		=      jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]    ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,     )
							__magic_name__    :     List[Any]     		=      model.decode(
							    decoder_input_ids[:, :-1]    ,     lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__    ,     past_key_values=lowerCAmelCase__    ,     decoder_position_ids=lowerCAmelCase__    ,     )

							__magic_name__    :     str     		=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,     dtype="""i4"""				)
							__magic_name__    :     Optional[Any]     		=      model.decode(
							    decoder_input_ids[:, -1:]    ,     lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__    ,     past_key_values=outputs_cache.past_key_values    ,     decoder_position_ids=lowerCAmelCase__    ,     )

							__magic_name__    :     Optional[Any]     		=      model.decode(lowerCAmelCase__    ,     lowerCAmelCase__				)

							__magic_name__    :     List[Any]     		=      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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   str:
							__magic_name__    :     int     		=      20
							__magic_name__    :     List[str]     		=      model_class_name(lowerCAmelCase__				)

							__magic_name__    :     int     		=      model.encode(inputs_dict["""input_ids"""]				)

							__magic_name__		,__magic_name__    :     int     		=      (
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

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

							__magic_name__    :     Tuple     		=      model.init_cache(decoder_input_ids.shape[0]    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
							__magic_name__    :     Optional[Any]     		=      jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]    ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,     )

							__magic_name__    :     Any     		=      model.decode(
							    decoder_input_ids[:, :-1]    ,     lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__    ,     past_key_values=lowerCAmelCase__    ,     decoder_position_ids=lowerCAmelCase__    ,     )
							__magic_name__    :     List[str]     		=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,     dtype="""i4"""				)
							__magic_name__    :     Dict     		=      model.decode(
							    decoder_input_ids[:, -1:]    ,     lowerCAmelCase__    ,     past_key_values=outputs_cache.past_key_values    ,     decoder_attention_mask=lowerCAmelCase__    ,     decoder_position_ids=lowerCAmelCase__    ,     )

							__magic_name__    :     Dict     		=      model.decode(lowerCAmelCase__    ,     lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__				)

							__magic_name__    :     Union[str, Any]     		=      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}'				)





@require_flax
class  snake_case__      (							unittest.TestCase   ):
	lowercase__		:  List[Any]							   =					99
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__    :     Any     		=      np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]    ,     dtype=np.intaa    ,     )

							__magic_name__    :     int     		=      input_ids.shape[0]
							__magic_name__    :     Dict     		=      MBartConfig(
							    vocab_size=self.vocab_size    ,     d_model=24    ,     encoder_layers=2    ,     decoder_layers=2    ,     encoder_attention_heads=2    ,     decoder_attention_heads=2    ,     encoder_ffn_dim=32    ,     decoder_ffn_dim=32    ,     max_position_embeddings=48    ,     eos_token_id=2    ,     pad_token_id=1    ,     bos_token_id=0    ,     )
							return config, input_ids, batch_size
	def     __magic_name__       (       self				)							->   List[Any]:
							__magic_name__		,__magic_name__		,__magic_name__    :     Any     		=      self._get_config_and_data()
							__magic_name__    :     Union[str, Any]     		=      FlaxMBartForSequenceClassification(lowerCAmelCase__				)
							__magic_name__    :     Union[str, Any]     		=      model(input_ids=lowerCAmelCase__    ,     decoder_input_ids=lowerCAmelCase__				)
							__magic_name__    :     Any     		=      (batch_size, config.num_labels)
							self.assertEqual(outputs["""logits"""].shape    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Optional[int]:
							__magic_name__		,__magic_name__		,__magic_name__    :     Tuple     		=      self._get_config_and_data()
							__magic_name__    :     Optional[int]     		=      FlaxMBartForQuestionAnswering(lowerCAmelCase__				)
							__magic_name__    :     str     		=      model(input_ids=lowerCAmelCase__				)

							self.assertEqual(outputs["""start_logits"""].shape    ,     input_ids.shape				)
							self.assertEqual(outputs["""end_logits"""].shape    ,     input_ids.shape				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__		,__magic_name__		,__magic_name__    :     str     		=      self._get_config_and_data()
							__magic_name__    :     Union[str, Any]     		=      FlaxMBartForConditionalGeneration(lowerCAmelCase__				)
							__magic_name__    :     int     		=      lm_model(input_ids=lowerCAmelCase__				)
							__magic_name__    :     List[str]     		=      (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     int     		=      MBartConfig(
							    vocab_size=self.vocab_size    ,     d_model=14    ,     encoder_layers=2    ,     decoder_layers=2    ,     encoder_attention_heads=2    ,     decoder_attention_heads=2    ,     encoder_ffn_dim=8    ,     decoder_ffn_dim=8    ,     max_position_embeddings=48    ,     )
							__magic_name__    :     List[str]     		=      FlaxMBartForConditionalGeneration(lowerCAmelCase__				)
							__magic_name__    :     Dict     		=      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,     dtype=np.intaa				)
							__magic_name__    :     Dict     		=      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,     dtype=np.intaa				)
							__magic_name__    :     Union[str, Any]     		=      lm_model(input_ids=lowerCAmelCase__    ,     decoder_input_ids=lowerCAmelCase__				)
							__magic_name__    :     str     		=      (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape    ,     lowerCAmelCase__				)







	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     int     		=      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,     dtype=np.intaa				)
							__magic_name__    :     Tuple     		=      shift_tokens_right(lowerCAmelCase__    ,     1				)
							__magic_name__    :     Any     		=      np.equal(lowerCAmelCase__    ,     1				).astype(np.floataa				).sum()
							__magic_name__    :     Tuple     		=      np.equal(lowerCAmelCase__    ,     1				).astype(np.floataa				).sum()
							self.assertEqual(shifted.shape    ,     input_ids.shape				)
							self.assertEqual(lowerCAmelCase__    ,     n_pad_before - 1				)
							self.assertTrue(np.equal(shifted[:, 0]    ,     2				).all()				)





@require_flax
class  snake_case__      (							_lowerCAmelCase		,						unittest.TestCase		,						_lowerCAmelCase   ):
	lowercase__		:  Dict							   =					True
	lowercase__		:  Any							   =					(
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase__		:  str							   =					(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__    :     Any     		=      FlaxMBartModelTester(self				)
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__		,__magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__		,__magic_name__    :     List[Any]     		=      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__		,__magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__				):
																			__magic_name__    :     Tuple     		=      self._prepare_for_class(lowerCAmelCase__    ,     lowerCAmelCase__				)
																			__magic_name__    :     Optional[int]     		=      model_class(lowerCAmelCase__				)

																			@jax.jit
																			def encode_jitted(lowerCAmelCase__    ,     lowerCAmelCase__=None    ,     **lowerCAmelCase__				):
																									return model.encode(input_ids=lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__				)

																			with self.subTest("""JIT Enabled"""				):
																									__magic_name__    :     Any     		=      encode_jitted(**lowerCAmelCase__				).to_tuple()

																			with self.subTest("""JIT Disabled"""				):
																									with jax.disable_jit():
																															__magic_name__    :     Optional[Any]     		=      encode_jitted(**lowerCAmelCase__				).to_tuple()

																			self.assertEqual(len(lowerCAmelCase__				)    ,     len(lowerCAmelCase__				)				)
																			for jitted_output, output in zip(lowerCAmelCase__    ,     lowerCAmelCase__				):
																									self.assertEqual(jitted_output.shape    ,     output.shape				)
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__		,__magic_name__    :     Dict     		=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__				):
																			__magic_name__    :     Optional[int]     		=      model_class(lowerCAmelCase__				)
																			__magic_name__    :     Union[str, Any]     		=      model.encode(inputs_dict["""input_ids"""]    ,     inputs_dict["""attention_mask"""]				)

																			__magic_name__    :     Dict     		=      {
																			    """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(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				):
																									return model.decode(
																									    decoder_input_ids=lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__    ,     encoder_outputs=lowerCAmelCase__    ,     )

																			with self.subTest("""JIT Enabled"""				):
																									__magic_name__    :     Optional[int]     		=      decode_jitted(**lowerCAmelCase__				).to_tuple()

																			with self.subTest("""JIT Disabled"""				):
																									with jax.disable_jit():
																															__magic_name__    :     Any     		=      decode_jitted(**lowerCAmelCase__				).to_tuple()

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







	@slow
	def     __magic_name__       (       self				)							->   List[Any]:
							for model_class_name in self.all_model_classes:
													__magic_name__    :     Any     		=      model_class_name.from_pretrained("""facebook/mbart-large-cc25"""    ,     from_pt=lowerCAmelCase__				)
													# FlaxMBartForSequenceClassification expects eos token in input_ids
													__magic_name__    :     List[Any]     		=      np.ones((1, 1)				) * model.config.eos_token_id
													__magic_name__    :     str     		=      model(lowerCAmelCase__				)
													self.assertIsNotNone(lowerCAmelCase__				)





@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__      (							unittest.TestCase   ):
	lowercase__		:  Any							   =					[
	    ''' UN Chief Says There Is No Military Solution in Syria''',
	]
	lowercase__		:  Optional[Any]							   =					[
	    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
	]
	lowercase__		:  Optional[Any]							   =					'''facebook/mbart-large-en-ro'''
	@cached_property
	def     __magic_name__       (       self				)							->   Tuple:
							return AutoTokenizer.from_pretrained(self.model_name				)
	@cached_property
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     Tuple     		=      FlaxMBartForConditionalGeneration.from_pretrained(self.model_name    ,     from_pt=lowerCAmelCase__				)
							return model
	def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   Tuple:
							__magic_name__    :     Optional[Any]     		=      self.translate_src_text(**lowerCAmelCase__				)
							self.assertListEqual(self.expected_text    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   Dict:
							__magic_name__    :     Dict     		=      self.tokenizer(self.src_text    ,     **lowerCAmelCase__    ,     return_tensors="""np"""				)
							__magic_name__    :     str     		=      self.model.generate(
							    model_inputs.input_ids    ,     attention_mask=model_inputs.attention_mask    ,     decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]    ,     early_stopping=lowerCAmelCase__    ,     num_beams=2    ,     ).sequences
							__magic_name__    :     str     		=      self.tokenizer.batch_decode(lowerCAmelCase__    ,     skip_special_tokens=lowerCAmelCase__				)
							return generated_words







	@slow
	def     __magic_name__       (       self				)							->   List[str]:
							self._assert_generated_batch_equal_expected()
