'''simple docstring'''




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 experiment tracking 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
#
########################################################################

__UpperCAmelCase     							=1_6
__UpperCAmelCase     							=3_2






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__ = 16				)	->    Optional[Any]:
    __lowerCamelCase		=  AutoTokenizer.from_pretrained('''bert-base-cased'''				)
    __lowerCamelCase		=  load_dataset('''glue'''				,  '''mrpc'''				)

    def tokenize_function(UpperCamelCase__				):
        # max_length=None => use the model max length (it's actually the default)
        __lowerCamelCase		=  tokenizer(examples['''sentence1''']				,  examples['''sentence2''']				,  truncation=UpperCamelCase__				,  max_length=UpperCamelCase__				)
        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():
        __lowerCamelCase		=  datasets.map(
            UpperCamelCase__				,  batched=UpperCamelCase__				,  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
    __lowerCamelCase		=  tokenized_datasets.rename_column('''label'''				,  '''labels'''				)

    def collate_fn(UpperCamelCase__				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __lowerCamelCase		=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __lowerCamelCase		=  16
        elif accelerator.mixed_precision != "no":
            __lowerCamelCase		=  8
        else:
            __lowerCamelCase		=  None

        return tokenizer.pad(
            UpperCamelCase__				,  padding='''longest'''				,  max_length=UpperCamelCase__				,  pad_to_multiple_of=UpperCamelCase__				,  return_tensors='''pt'''				,  )

    # Instantiate dataloaders.
    __lowerCamelCase		=  DataLoader(
        tokenized_datasets['''train''']				,  shuffle=UpperCamelCase__				,  collate_fn=UpperCamelCase__				,  batch_size=UpperCamelCase__				)
    __lowerCamelCase		=  DataLoader(
        tokenized_datasets['''validation''']				,  shuffle=UpperCamelCase__				,  collate_fn=UpperCamelCase__				,  batch_size=UpperCamelCase__				)

    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

  __UpperCAmelCase     							=mocked_dataloaders  # noqa: F811






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Any:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,  UpperCamelCase__				) == "1":
        __lowerCamelCase		=  2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        __lowerCamelCase		=  Accelerator(
            cpu=args.cpu				,  mixed_precision=args.mixed_precision				,  log_with='''all'''				,  project_dir=args.project_dir				)
    else:
        __lowerCamelCase		=  Accelerator(cpu=args.cpu				,  mixed_precision=args.mixed_precision				)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __lowerCamelCase		=  config['''lr''']
    __lowerCamelCase		=  int(config['''num_epochs''']				)
    __lowerCamelCase		=  int(config['''seed''']				)
    __lowerCamelCase		=  int(config['''batch_size''']				)
    set_seed(UpperCamelCase__				)

    __lowerCamelCase	,  __lowerCamelCase		=  get_dataloaders(UpperCamelCase__				,  UpperCamelCase__				)
    __lowerCamelCase		=  evaluate.load('''glue'''				,  '''mrpc'''				)

    # If the batch size is too big we use gradient accumulation
    __lowerCamelCase		=  1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __lowerCamelCase		=  batch_size // MAX_GPU_BATCH_SIZE
        __lowerCamelCase		=  MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __lowerCamelCase		=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,  return_dict=UpperCamelCase__				)

    # 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).
    __lowerCamelCase		=  model.to(accelerator.device				)

    # Instantiate optimizer
    __lowerCamelCase		=  AdamW(params=model.parameters()				,  lr=UpperCamelCase__				)

    # Instantiate scheduler
    __lowerCamelCase		=  get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase__				,  num_warmup_steps=1_00				,  num_training_steps=(len(UpperCamelCase__				) * 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.
    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  accelerator.prepare(
        UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        __lowerCamelCase		=  os.path.split(UpperCamelCase__				)[-1].split('''.'''				)[0]
        accelerator.init_trackers(UpperCamelCase__				,  UpperCamelCase__				)

    # Now we train the model
    for epoch in range(UpperCamelCase__				):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            __lowerCamelCase		=  0
        for step, batch in enumerate(UpperCamelCase__				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            __lowerCamelCase		=  model(**UpperCamelCase__				)
            __lowerCamelCase		=  outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            __lowerCamelCase		=  loss / gradient_accumulation_steps
            accelerator.backward(UpperCamelCase__				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

        __lowerCamelCase		=  metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""				,  UpperCamelCase__				)

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': eval_metric['''accuracy'''],
                    '''f1''': eval_metric['''f1'''],
                    '''train_loss''': total_loss.item() / len(UpperCamelCase__				),
                    '''epoch''': epoch,
                }				,  step=UpperCamelCase__				,  )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()






def __lowerCAmelCase (							)	->    List[Any]:
    __lowerCamelCase		=  argparse.ArgumentParser(description='''Simple example of training script.'''				)
    parser.add_argument(
        '''--mixed_precision'''				,  type=UpperCamelCase__				,  default=UpperCamelCase__				,  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(
        '''--with_tracking'''				,  action='''store_true'''				,  help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''				,  )
    parser.add_argument(
        '''--project_dir'''				,  type=UpperCamelCase__				,  default='''logs'''				,  help='''Location on where to store experiment tracking logs` and relevent project information'''				,  )
    __lowerCamelCase		=  parser.parse_args()
    __lowerCamelCase		=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCamelCase__				,  UpperCamelCase__				)


if __name__ == "__main__":
  main()







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=9_9       ,  __lowerCAmelCase=1_3       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=9       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase=8       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.002       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		encoder_seq_length
				lowerCamelCase__								=		decoder_seq_length
				# For common tests
				lowerCamelCase__								=		self.decoder_seq_length
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_attention_mask
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		d_ff
				lowerCamelCase__								=		relative_attention_num_buckets
				lowerCamelCase__								=		dropout_rate
				lowerCamelCase__								=		initializer_factor
				lowerCamelCase__								=		eos_token_id
				lowerCamelCase__								=		pad_token_id
				lowerCamelCase__								=		decoder_start_token_id
				lowerCamelCase__								=		None
				lowerCamelCase__								=		decoder_layers





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return TaConfig.from_pretrained('''google/umt5-base'''							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				if attention_mask is None:
					lowerCamelCase__								=		input_ids.ne(config.pad_token_id							)
				if decoder_attention_mask is None:
					lowerCamelCase__								=		decoder_input_ids.ne(config.pad_token_id							)
				if head_mask is None:
					lowerCamelCase__								=		torch.ones(config.num_hidden_layers       ,  config.num_attention_heads       ,  device=__lowerCAmelCase							)
				if decoder_head_mask is None:
					lowerCamelCase__								=		torch.ones(config.num_decoder_layers       ,  config.num_attention_heads       ,  device=__lowerCAmelCase							)
				if cross_attn_head_mask is None:
					lowerCamelCase__								=		torch.ones(
					    config.num_decoder_layers       ,  config.num_attention_heads       ,  device=__lowerCAmelCase							)
				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,
				}





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ids_tensor([self.batch_size, self.encoder_seq_length]       ,  self.vocab_size							)
				lowerCamelCase__								=		ids_tensor([self.batch_size, self.decoder_seq_length]       ,  self.vocab_size							)

				# we need to clamp the input ids here to avoid having pad token in between
				# this is because for NllbMoe the position_ids are prepared such that
				# all pad tokens have pos id = 2 and rest are between 2..seq_length
				# and the seq_length here is seq_length - num_pad_tokens
				# but when using past, there is no way of knowing if the past input ids had
				# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
				# position_ids being off by num_pad_tokens in past input
				lowerCamelCase__								=		input_ids.clamp(self.pad_token_id + 1							)
				lowerCamelCase__								=		decoder_input_ids.clamp(self.pad_token_id + 1							)

				lowerCamelCase__								=		self.get_config()
				lowerCamelCase__								=		config.num_attention_heads
				lowerCamelCase__								=		self.prepare_inputs_dict(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							)
				return config, input_dict





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.prepare_config_and_inputs()
				return config, inputs_dict





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return TaConfig(
				    vocab_size=1_6_6       ,  d_model=self.hidden_size       ,  d_ff=self.d_ff       ,  d_kv=self.hidden_size // self.num_attention_heads       ,  num_layers=self.num_hidden_layers       ,  num_decoder_layers=self.decoder_layers       ,  num_heads=self.num_attention_heads       ,  relative_attention_num_buckets=self.relative_attention_num_buckets       ,  dropout_rate=self.dropout_rate       ,  initializer_factor=self.initializer_factor       ,  eos_token_id=self.eos_token_id       ,  bos_token_id=self.pad_token_id       ,  pad_token_id=self.pad_token_id       ,  decoder_start_token_id=self.decoder_start_token_id       ,  )





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return TaConfig(
				    vocab_size=self.vocab_size       ,  d_model=self.hidden_size       ,  d_ff=self.d_ff       ,  d_kv=self.hidden_size // self.num_attention_heads       ,  num_layers=self.num_hidden_layers       ,  num_decoder_layers=self.decoder_layers       ,  num_heads=self.num_attention_heads       ,  relative_attention_num_buckets=self.relative_attention_num_buckets       ,  dropout_rate=self.dropout_rate       ,  initializer_factor=self.initializer_factor       ,  eos_token_id=self.eos_token_id       ,  bos_token_id=self.pad_token_id       ,  pad_token_id=self.pad_token_id       ,  decoder_start_token_id=self.decoder_start_token_id       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModel(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(
				    input_ids=__lowerCAmelCase       ,  decoder_input_ids=__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  decoder_attention_mask=__lowerCAmelCase       ,  )
				lowerCamelCase__								=		model(input_ids=__lowerCAmelCase       ,  decoder_input_ids=__lowerCAmelCase							)
				lowerCamelCase__								=		result.last_hidden_state
				lowerCamelCase__								=		result.past_key_values
				lowerCamelCase__								=		result.encoder_last_hidden_state

				self.parent.assertEqual(encoder_output.size()       ,  (self.batch_size, self.encoder_seq_length, self.hidden_size)							)
				self.parent.assertEqual(decoder_output.size()       ,  (self.batch_size, self.decoder_seq_length, self.hidden_size)							)
				# There should be `num_layers` key value embeddings stored in decoder_past
				self.parent.assertEqual(len(__lowerCAmelCase							)       ,  config.num_layers							)
				# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
				self.parent.assertEqual(len(decoder_past[0]							)       ,  4							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModel(config=__lowerCAmelCase							).get_decoder().to(__lowerCAmelCase							).eval()
				# first forward pass
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  use_cache=__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  use_cache=__lowerCAmelCase							)

				self.parent.assertTrue(len(__lowerCAmelCase							) == len(__lowerCAmelCase							)							)
				self.parent.assertTrue(len(__lowerCAmelCase							) == len(__lowerCAmelCase							) + 1							)

				lowerCamelCase__				,	lowerCamelCase__								=		outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				lowerCamelCase__								=		ids_tensor((self.batch_size, 1)       ,  config.vocab_size							)

				# append to next input_ids and
				lowerCamelCase__								=		torch.cat([input_ids, next_tokens]       ,  dim=-1							)

				lowerCamelCase__								=		model(__lowerCAmelCase							)['''last_hidden_state''']
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  past_key_values=__lowerCAmelCase							)['''last_hidden_state''']

				# select random slice
				lowerCamelCase__								=		ids_tensor((1,)       ,  output_from_past.shape[-1]							).item()
				lowerCamelCase__								=		output_from_no_past[:, -1, random_slice_idx].detach()
				lowerCamelCase__								=		output_from_past[:, 0, random_slice_idx].detach()

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModel(config=__lowerCAmelCase							).to(__lowerCAmelCase							).half().eval()
				lowerCamelCase__								=		model(**__lowerCAmelCase							)['''last_hidden_state''']
				self.parent.assertFalse(torch.isnan(__lowerCAmelCase							).any().item()							)






@require_torch
class 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			lowerCAmelCase_							     =						(UMTaForConditionalGeneration,) if is_torch_available() else ()
			lowerCAmelCase_							     =						(
			    {
			        """conversational""": UMTaForConditionalGeneration,
			        """feature-extraction""": UMTaModel,
			        """summarization""": UMTaForConditionalGeneration,
			        """text2text-generation""": UMTaForConditionalGeneration,
			        """translation""": UMTaForConditionalGeneration,
			        """question-answering""": UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCAmelCase_							     =						True
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						True
			lowerCAmelCase_							     =						True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			lowerCAmelCase_							     =						[0.8, 0.9]





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaModelTester(self							)





			@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				lowerCamelCase__								=		UMTaModel(config_and_inputs[0]							).to(__lowerCAmelCase							)
				with tempfile.TemporaryDirectory() as tmpdirname:
					torch.onnx.export(
					    __lowerCAmelCase       ,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])       ,  F'{tmpdirname}/t5_test.onnx'       ,  export_params=__lowerCAmelCase       ,  opset_version=9       ,  input_names=['''input_ids''', '''decoder_input_ids''']       ,  )





			@unittest.skipIf(torch_device == '''cpu'''       ,  '''Cant do half precision'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				lowerCamelCase__								=		config_and_inputs[0]
				lowerCamelCase__								=		UMTaForConditionalGeneration(__lowerCAmelCase							).eval()
				model.to(__lowerCAmelCase							)

				lowerCamelCase__								=		{
				    '''head_mask''': torch.zeros(config.num_layers       ,  config.num_heads       ,  device=__lowerCAmelCase							),
				    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers       ,  config.num_heads       ,  device=__lowerCAmelCase							),
				    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers       ,  config.num_heads       ,  device=__lowerCAmelCase							),
				}

				for attn_name, (name, mask) in zip(__lowerCAmelCase       ,  head_masking.items()							):
					lowerCamelCase__								=		{name: mask}
					# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
					if name == "head_mask":
						lowerCamelCase__								=		torch.ones(
						    config.num_decoder_layers       ,  config.num_heads       ,  device=__lowerCAmelCase							)

					lowerCamelCase__								=		model.generate(
					    config_and_inputs[1]['''input_ids''']       ,  num_beams=1       ,  max_length=3       ,  output_attentions=__lowerCAmelCase       ,  return_dict_in_generate=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )
					# We check the state of decoder_attentions and cross_attentions just from the last step
					lowerCamelCase__								=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
					self.assertEqual(sum([w.sum().item() for w in attn_weights]							)       ,  0.0							)





			@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass






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

			'''simple docstring'''







			@slow
			@unittest.skip(
			    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''       ,  return_dict=__lowerCAmelCase							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		AutoTokenizer.from_pretrained('''google/umt5-small'''       ,  use_fast=__lowerCAmelCase       ,  legacy=__lowerCAmelCase							)
				lowerCamelCase__								=		[
				    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
				    '''No se como puedo <extra_id_0>.''',
				    '''This is the reason why we <extra_id_0> them.''',
				    '''The <extra_id_0> walks in <extra_id_1>, seats''',
				    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
				]
				lowerCamelCase__								=		tokenizer(__lowerCAmelCase       ,  return_tensors='''pt'''       ,  padding=__lowerCAmelCase							).input_ids
				# fmt: off
				lowerCamelCase__								=		torch.tensor(
				    [
				        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
				        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
				    ]							)
				# fmt: on
				torch.testing.assert_allclose(__lowerCAmelCase       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		model.generate(input_ids.to(__lowerCAmelCase							)							)
				lowerCamelCase__								=		[
				    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
				    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				]
				lowerCamelCase__								=		tokenizer.batch_decode(__lowerCAmelCase							)
				self.assertEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)



