

import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def       a__					(		UpperCAmelCase							:      Optional[int]      )  ->						List[Any]:
  UpperCAmelCase	:							str						=			fname.split(os.path.sep      )[-1]
  return re.search(r'''^(.*)_\d+\.jpg$'''    ,			UpperCAmelCase      ).groups()[0]





class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Tuple,		__A							:					Union[str, Any],		__A							:					int=None,		__A							:					Union[str, Any]=None						):
     UpperCAmelCase	:							Tuple						=			file_names
     UpperCAmelCase	:							Optional[Any]						=			image_transform
     UpperCAmelCase	:							List[Any]						=			label_to_id



   def __len__(							self							:					Tuple						):
     return len(self.file_names						)





   def __getitem__(							self							:					Dict,		__A							:					int						):
     UpperCAmelCase	:							Any						=			self.file_names[idx]
     UpperCAmelCase	:							int						=			PIL.Image.open(__A						)
     UpperCAmelCase	:							int						=			raw_image.convert('''RGB'''						)
     if self.image_transform is not None:
       UpperCAmelCase	:							List[str]						=			self.image_transform(__A						)
     UpperCAmelCase	:							Optional[Any]						=			extract_label(__A						)
     if self.label_to_id is not None:
       UpperCAmelCase	:							Optional[Any]						=			self.label_to_id[label]
     return {"image": image, "label": label}






def       a__					(		UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      Optional[int]      )  ->						Optional[int]:
  # Initialize accelerator
  if args.with_tracking:
    UpperCAmelCase	:							Optional[Any]						=			Accelerator(
        cpu=args.cpu    ,			mixed_precision=args.mixed_precision    ,			log_with='''all'''    ,			project_dir=args.project_dir      )
  else:
    UpperCAmelCase	:							int						=			Accelerator(cpu=args.cpu    ,			mixed_precision=args.mixed_precision      )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCAmelCase	:							List[Any]						=			config['''lr''']
  UpperCAmelCase	:							Optional[int]						=			int(config['''num_epochs''']      )
  UpperCAmelCase	:							List[Any]						=			int(config['''seed''']      )
  UpperCAmelCase	:							Union[str, Any]						=			int(config['''batch_size''']      )
  UpperCAmelCase	:							Optional[Any]						=			config['''image_size''']
  if not isinstance(UpperCAmelCase    ,			(list, tuple)      ):
    UpperCAmelCase	:							str						=			(image_size, image_size)

  # 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":
      UpperCAmelCase	:							Dict						=			args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      UpperCAmelCase	:							Dict						=			int(args.checkpointing_steps      )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
  else:
    UpperCAmelCase	:							Tuple						=			None

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    UpperCAmelCase	:							Optional[int]						=			os.path.split(UpperCAmelCase      )[-1].split('''.'''      )[0]
    accelerator.init_trackers(UpperCAmelCase    ,			UpperCAmelCase      )

  # Grab all the image filenames
  UpperCAmelCase	:							List[str]						=			[os.path.join(args.data_dir    ,			UpperCAmelCase      ) for fname in os.listdir(args.data_dir      ) if fname.endswith('''.jpg'''      )]

  # Build the label correspondences
  UpperCAmelCase	:							Union[str, Any]						=			[extract_label(UpperCAmelCase      ) for fname in file_names]
  UpperCAmelCase	:							Tuple						=			list(set(UpperCAmelCase      )      )
  id_to_label.sort()
  UpperCAmelCase	:							Optional[int]						=			{lbl: i for i, lbl in enumerate(UpperCAmelCase      )}

  # Set the seed before splitting the data.
  np.random.seed(UpperCAmelCase      )
  torch.manual_seed(UpperCAmelCase      )
  torch.cuda.manual_seed_all(UpperCAmelCase      )

  # Split our filenames between train and validation
  UpperCAmelCase	:							Optional[Any]						=			np.random.permutation(len(UpperCAmelCase      )      )
  UpperCAmelCase	:							int						=			int(0.8 * len(UpperCAmelCase      )      )
  UpperCAmelCase	:							int						=			random_perm[:cut]
  UpperCAmelCase	:							str						=			random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  UpperCAmelCase	:							Union[str, Any]						=			Compose([RandomResizedCrop(UpperCAmelCase    ,			scale=(0.5, 1.0)      ), ToTensor()]      )
  UpperCAmelCase	:							Optional[Any]						=			PetsDataset(
      [file_names[i] for i in train_split]    ,			image_transform=UpperCAmelCase    ,			label_to_id=UpperCAmelCase      )

  # For evaluation, we use a deterministic Resize
  UpperCAmelCase	:							Optional[int]						=			Compose([Resize(UpperCAmelCase      ), ToTensor()]      )
  UpperCAmelCase	:							Any						=			PetsDataset([file_names[i] for i in eval_split]    ,			image_transform=UpperCAmelCase    ,			label_to_id=UpperCAmelCase      )

  # Instantiate dataloaders.
  UpperCAmelCase	:							List[str]						=			DataLoader(UpperCAmelCase    ,			shuffle=UpperCAmelCase    ,			batch_size=UpperCAmelCase    ,			num_workers=4      )
  UpperCAmelCase	:							List[str]						=			DataLoader(UpperCAmelCase    ,			shuffle=UpperCAmelCase    ,			batch_size=UpperCAmelCase    ,			num_workers=4      )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase	:							Dict						=			create_model('''resnet50d'''    ,			pretrained=UpperCAmelCase    ,			num_classes=len(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).
  UpperCAmelCase	:							Optional[Any]						=			model.to(accelerator.device      )

  # Freezing the base model
  for param in model.parameters():
    UpperCAmelCase	:							Optional[int]						=			False
  for param in model.get_classifier().parameters():
    UpperCAmelCase	:							List[str]						=			True

  # We normalize the batches of images to be a bit faster.
  UpperCAmelCase	:							int						=			torch.tensor(model.default_cfg['''mean''']      )[None, :, None, None].to(accelerator.device      )
  UpperCAmelCase	:							Dict						=			torch.tensor(model.default_cfg['''std''']      )[None, :, None, None].to(accelerator.device      )

  # Instantiate optimizer
  UpperCAmelCase	:							List[Any]						=			torch.optim.Adam(params=model.parameters()    ,			lr=lr / 25      )

  # Instantiate learning rate scheduler
  UpperCAmelCase	:							Dict						=			OneCycleLR(optimizer=UpperCAmelCase    ,			max_lr=UpperCAmelCase    ,			epochs=UpperCAmelCase    ,			steps_per_epoch=len(UpperCAmelCase      )      )

  # 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.
  UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			accelerator.prepare(
      UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
  # We need to keep track of how many total steps we have iterated over
  UpperCAmelCase	:							List[Any]						=			0
  # We also need to keep track of the starting epoch so files are named properly
  UpperCAmelCase	:							Union[str, Any]						=			0

  # Potentially load in the weights and states from a previous save
  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      )
      UpperCAmelCase	:							Tuple						=			os.path.basename(args.resume_from_checkpoint      )
    else:
      # Get the most recent checkpoint
      UpperCAmelCase	:							List[Any]						=			[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
      dirs.sort(key=os.path.getctime      )
      UpperCAmelCase	:							Any						=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    UpperCAmelCase	:							Any						=			os.path.splitext(UpperCAmelCase      )[0]

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

    # Now we train the model
  for epoch in range(UpperCAmelCase    ,			UpperCAmelCase      ):
    model.train()
    if args.with_tracking:
      UpperCAmelCase	:							Any						=			0
    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
      UpperCAmelCase	:							Any						=			accelerator.skip_first_batches(UpperCAmelCase    ,			UpperCAmelCase      )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      UpperCAmelCase	:							Optional[int]						=			train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      UpperCAmelCase	:							List[str]						=			{k: v.to(accelerator.device      ) for k, v in batch.items()}
      UpperCAmelCase	:							Optional[Any]						=			(batch['''image'''] - mean) / std
      UpperCAmelCase	:							Tuple						=			model(UpperCAmelCase      )
      UpperCAmelCase	:							List[str]						=			torch.nn.functional.cross_entropy(UpperCAmelCase    ,			batch['''label''']      )
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(UpperCAmelCase      )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      overall_step += 1
      if isinstance(UpperCAmelCase    ,			UpperCAmelCase      ):
        UpperCAmelCase	:							Dict						=			f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            UpperCAmelCase	:							Any						=			os.path.join(args.output_dir    ,			UpperCAmelCase      )
          accelerator.save_state(UpperCAmelCase      )
    model.eval()
    UpperCAmelCase	:							Union[str, Any]						=			0
    UpperCAmelCase	:							str						=			0
    for step, batch in enumerate(UpperCAmelCase      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      UpperCAmelCase	:							Optional[Any]						=			{k: v.to(accelerator.device      ) for k, v in batch.items()}
      UpperCAmelCase	:							Optional[Any]						=			(batch['''image'''] - mean) / std
      with torch.no_grad():
        UpperCAmelCase	:							str						=			model(UpperCAmelCase      )
      UpperCAmelCase	:							str						=			outputs.argmax(dim=-1      )
      UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			accelerator.gather_for_metrics((predictions, batch['''label'''])      )
      UpperCAmelCase	:							List[Any]						=			predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    UpperCAmelCase	:							str						=			accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''      )
    if args.with_tracking:
      accelerator.log(
          {
              '''accuracy''': 100 * eval_metric,
              '''train_loss''': total_loss.item() / len(UpperCAmelCase      ),
              '''epoch''': epoch,
          }    ,			step=UpperCAmelCase    ,			)
    if checkpointing_steps == "epoch":
      UpperCAmelCase	:							Union[str, Any]						=			f'''epoch_{epoch}'''
      if args.output_dir is not None:
        UpperCAmelCase	:							str						=			os.path.join(args.output_dir    ,			UpperCAmelCase      )
      accelerator.save_state(UpperCAmelCase      )

  if args.with_tracking:
    accelerator.end_training()

def       a__					(		)  ->						List[str]:
  UpperCAmelCase	:							Optional[int]						=			argparse.ArgumentParser(description='''Simple example of training script.'''      )
  parser.add_argument('''--data_dir'''    ,			required=UpperCAmelCase    ,			help='''The data folder on disk.'''      )
  parser.add_argument('''--fp16'''    ,			action='''store_true'''    ,			help='''If passed, will use FP16 training.'''      )
  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(
      '''--checkpointing_steps'''    ,			type=UpperCAmelCase    ,			default=UpperCAmelCase    ,			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=UpperCAmelCase    ,			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=UpperCAmelCase    ,			default=UpperCAmelCase    ,			help='''If the training should continue from a checkpoint folder.'''    ,			)
  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'''    ,			)
  UpperCAmelCase	:							Dict						=			parser.parse_args()
  UpperCAmelCase	:							Union[str, Any]						=			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
  training_function(UpperCAmelCase    ,			UpperCAmelCase      )


if __name__ == "__main__":
     main()








import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _lowerCamelCase    :       Any        =							"0.12"  # assumed parallelism: 8


if is_torch_available():
     import torch

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      List[str]=None      )  ->						List[Any]:
  if rng is None:
    UpperCAmelCase	:							Dict						=			random.Random()

  UpperCAmelCase	:							Optional[Any]						=			1
  for dim in shape:
    total_dims *= dim

  UpperCAmelCase	:							List[str]						=			[]
  for _ in range(UpperCAmelCase      ):
    values.append(rng.randint(0    ,			vocab_size - 1      )      )

  UpperCAmelCase	:							List[str]						=			np.array(UpperCAmelCase    ,			dtype=jnp.intaa      ).reshape(UpperCAmelCase      )

  return output

def       a__					(		UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Optional[int]=None      )  ->						List[str]:
  UpperCAmelCase	:							Optional[int]						=			ids_tensor(UpperCAmelCase    ,			vocab_size=2    ,			rng=UpperCAmelCase      )
  # make sure that at least one token is attended to for each batch
  UpperCAmelCase	:							str						=			1
  return attn_mask





@require_flax
class  __UpperCAmelCase					:
   UpperCamelCase       					=					None
   UpperCamelCase       					=					()



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

     # cut to half length & take max batch_size 3
     UpperCAmelCase	:							Optional[int]						=			2
     UpperCAmelCase	:							Dict						=			inputs['''input_ids'''].shape[-1] // 2
     UpperCAmelCase	:							Dict						=			inputs['''input_ids'''][:max_batch_size, :sequence_length]

     UpperCAmelCase	:							Optional[int]						=			jnp.ones_like(__A						)
     UpperCAmelCase	:							Optional[int]						=			attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     UpperCAmelCase	:							Optional[Any]						=			input_ids.shape[-1] + 5
     if config.eos_token_id is not None and config.pad_token_id is None:
       # hack to allow generate for models such as GPT2 as is done in `generate()`
       UpperCAmelCase	:							Optional[Any]						=			config.eos_token_id
     return config, input_ids, attention_mask, max_length



   @is_pt_flax_cross_test
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Any						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Optional[Any]						=			False
     UpperCAmelCase	:							Any						=			max_length
     UpperCAmelCase	:							List[Any]						=			0

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							List[str]						=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
       UpperCAmelCase	:							List[Any]						=			getattr(__A,		__A						)
       UpperCAmelCase	:							Union[str, Any]						=			pt_model_class(__A						).eval()
       UpperCAmelCase	:							Tuple						=			load_flax_weights_in_pytorch_model(__A,		flax_model.params						)

       UpperCAmelCase	:							Dict						=			flax_model.generate(__A						).sequences
       UpperCAmelCase	:							str						=			pt_model.generate(torch.tensor(__A,		dtype=torch.long						)						)

       if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
         UpperCAmelCase	:							Any						=			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

       self.assertListEqual(pt_generation_outputs.numpy().tolist(),		flax_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			False
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							Optional[int]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			True
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							str						=			jit(model.generate						)
       UpperCAmelCase	:							List[Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							int						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Dict						=			False
     UpperCAmelCase	:							Union[str, Any]						=			max_length
     UpperCAmelCase	:							List[Any]						=			2

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							str						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							int						=			jit(model.generate						)
       UpperCAmelCase	:							Union[str, Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Any						=			False
     UpperCAmelCase	:							Optional[int]						=			max_length
     UpperCAmelCase	:							Optional[int]						=			2
     UpperCAmelCase	:							str						=			2

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[0],		input_ids.shape[0] * config.num_return_sequences						)



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							str						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			True
     UpperCAmelCase	:							Union[str, Any]						=			max_length
     UpperCAmelCase	:							Union[str, Any]						=			0.8
     UpperCAmelCase	:							str						=			1_0
     UpperCAmelCase	:							Any						=			0.3
     UpperCAmelCase	:							str						=			1
     UpperCAmelCase	:							Union[str, Any]						=			8
     UpperCAmelCase	:							Optional[Any]						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							List[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[int]						=			jit(model.generate						)
       UpperCAmelCase	:							Any						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Optional[Any]						=			max_length
     UpperCAmelCase	:							Tuple						=			1
     UpperCAmelCase	:							Optional[Any]						=			8
     UpperCAmelCase	:							Optional[int]						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							List[str]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Dict						=			jit(model.generate						)
       UpperCAmelCase	:							List[str]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							List[str]						=			max_length
     UpperCAmelCase	:							Union[str, Any]						=			2
     UpperCAmelCase	:							List[Any]						=			1
     UpperCAmelCase	:							List[str]						=			8
     UpperCAmelCase	:							int						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Optional[Any]						=			model_class(__A						)

       UpperCAmelCase	:							Union[str, Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[str]						=			jit(model.generate						)
       UpperCAmelCase	:							Tuple						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Union[str, Any]						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Tuple						=			False
     UpperCAmelCase	:							str						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Any						=			model_class(__A						)

       UpperCAmelCase	:							Union[str, Any]						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Union[str, Any]						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Union[str, Any]						=			True
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Any						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)





   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Dict						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Union[str, Any]						=			2
     UpperCAmelCase	:							str						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							str						=			model_class(__A						)

       UpperCAmelCase	:							int						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Dict						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)






@require_flax
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Union[str, Any]						=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''						)
     UpperCAmelCase	:							List[str]						=			FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''						)

     UpperCAmelCase	:							int						=			'''Hello world'''
     UpperCAmelCase	:							Optional[int]						=			tokenizer(__A,		return_tensors='''np'''						).input_ids

     # typos are quickly detected (the correct argument is `do_sample`)
     with self.assertRaisesRegex(__A,		'''do_samples'''						):
       model.generate(__A,		do_samples=__A						)

     # arbitrary arguments that will not be used anywhere are also not accepted
     with self.assertRaisesRegex(__A,		'''foo'''						):
       UpperCAmelCase	:							Any						=			{'''foo''': '''bar'''}
       model.generate(__A,		**__A						)





