code: 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 		snake_case__  (		lowercase				):
  
     '''simple docstring'''
  
  
  
  
  
     lowerCAmelCase_:       List[Any]         =  fname.split(os.path.sep				)[-1]
     return re.search(r"^(.*)_\d+\.jpg$"       ,							snake_case__				).groups()[0]
  
  class 				_lowercase						(    UpperCamelCase_ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=None					,	lowerCamelCase__=None   ):
         lowerCAmelCase_:       Optional[Any]         =  file_names
         lowerCAmelCase_:       Union[str, Any]         =  image_transform
         lowerCAmelCase_:       Optional[int]         =  label_to_id
  
  
  
  
      def __len__(						self   ):
         return len(self.file_names   )
  
  
  
  
      def __getitem__(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Any         =  self.file_names[idx]
         lowerCAmelCase_:       Dict         =  PIL.Image.open(__SCREAMING_SNAKE_CASE   )
         lowerCAmelCase_:       Tuple         =  raw_image.convert("RGB"   )
         if self.image_transform is not None:
            lowerCAmelCase_:       Any         =  self.image_transform(__SCREAMING_SNAKE_CASE   )
         lowerCAmelCase_:       Optional[Any]         =  extract_label(__SCREAMING_SNAKE_CASE   )
         if self.label_to_id is not None:
            lowerCAmelCase_:       List[str]         =  self.label_to_id[label]
         return {"image": image, "label": label}
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase				):
  
     '''simple docstring'''
  
  
  
  
  
     lowerCAmelCase_:       List[Any]         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)
  
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase_:       List[Any]         =  config["lr"]
     lowerCAmelCase_:       Tuple         =  int(config["num_epochs"]				)
     lowerCAmelCase_:       Optional[Any]         =  int(config["seed"]				)
     lowerCAmelCase_:       Dict         =  int(config["batch_size"]				)
     lowerCAmelCase_:       Any         =  config["image_size"]
     if not isinstance(snake_case__       ,							(list, tuple)				):
        lowerCAmelCase_:       Union[str, Any]         =  (image_size, image_size)
  
     # Grab all the image filenames
     lowerCAmelCase_:       str         =  [os.path.join(args.data_dir       ,							snake_case__				) for fname in os.listdir(args.data_dir				) if fname.endswith(".jpg"				)]
  
     # Build the label correspondences
     lowerCAmelCase_:       Union[str, Any]         =  [extract_label(snake_case__				) for fname in file_names]
     lowerCAmelCase_:       List[Any]         =  list(set(snake_case__				)				)
     id_to_label.sort()
     lowerCAmelCase_:       Optional[int]         =  {lbl: i for i, lbl in enumerate(snake_case__				)}
  
     # Set the seed before splitting the data.
     np.random.seed(snake_case__				)
     torch.manual_seed(snake_case__				)
     torch.cuda.manual_seed_all(snake_case__				)
  
     # Split our filenames between train and validation
     lowerCAmelCase_:       List[str]         =  np.random.permutation(len(snake_case__				)				)
     lowerCAmelCase_:       Dict         =  int(0.8 * len(snake_case__				)				)
     lowerCAmelCase_:       int         =  random_perm[:cut]
     lowerCAmelCase_:       List[str]         =  random_perm[cut:]
  
     # For training we use a simple RandomResizedCrop
     lowerCAmelCase_:       int         =  Compose([RandomResizedCrop(snake_case__       ,							scale=(0.5, 1.0)				), ToTensor()]				)
     lowerCAmelCase_:       int         =  PetsDataset(
         [file_names[i] for i in train_split]       ,							image_transform=snake_case__       ,							label_to_id=snake_case__				)
  
     # For evaluation, we use a deterministic Resize
     lowerCAmelCase_:       List[str]         =  Compose([Resize(snake_case__				), ToTensor()]				)
     lowerCAmelCase_:       Tuple         =  PetsDataset([file_names[i] for i in eval_split]       ,							image_transform=snake_case__       ,							label_to_id=snake_case__				)
  
     # Instantiate dataloaders.
     lowerCAmelCase_:       List[Any]         =  DataLoader(snake_case__       ,							shuffle=snake_case__       ,							batch_size=snake_case__       ,							num_workers=4				)
     lowerCAmelCase_:       Any         =  DataLoader(snake_case__       ,							shuffle=snake_case__       ,							batch_size=snake_case__       ,							num_workers=4				)
  
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase_:       Optional[int]         =  create_model("resnet50d"       ,							pretrained=snake_case__       ,							num_classes=len(snake_case__				)				)
  
     # 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_:       Union[str, Any]         =  model.to(accelerator.device				)
  
     # Freezing the base model
     for param in model.parameters():
        lowerCAmelCase_:       Tuple         =  False
     for param in model.get_classifier().parameters():
        lowerCAmelCase_:       List[Any]         =  True
  
     # We normalize the batches of images to be a bit faster.
     lowerCAmelCase_:       List[str]         =  torch.tensor(model.default_cfg["mean"]				)[None, :, None, None].to(accelerator.device				)
     lowerCAmelCase_:       Union[str, Any]         =  torch.tensor(model.default_cfg["std"]				)[None, :, None, None].to(accelerator.device				)
  
     # Instantiate optimizer
     lowerCAmelCase_:       List[str]         =  torch.optim.Adam(params=model.parameters()       ,							lr=lr / 25				)
  
     # Instantiate learning rate scheduler
     lowerCAmelCase_:       Union[str, Any]         =  OneCycleLR(optimizer=snake_case__       ,							max_lr=snake_case__       ,							epochs=snake_case__       ,							steps_per_epoch=len(snake_case__				)				)
  
     # 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_:       Optional[int]         =  accelerator.prepare(
         snake_case__       ,							snake_case__       ,							snake_case__       ,							snake_case__       ,							snake_case__				)
  
     # Now we train the model
     for epoch in range(snake_case__				):
        model.train()
        for step, batch in enumerate(snake_case__				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           lowerCAmelCase_:       Union[str, Any]         =  {k: v.to(accelerator.device				) for k, v in batch.items()}
           lowerCAmelCase_:       Tuple         =  (batch["image"] - mean) / std
           lowerCAmelCase_:       Tuple         =  model(snake_case__				)
           lowerCAmelCase_:       Union[str, Any]         =  torch.nn.functional.cross_entropy(snake_case__       ,							batch["label"]				)
           accelerator.backward(snake_case__				)
           optimizer.step()
           lr_scheduler.step()
           optimizer.zero_grad()
  
        model.eval()
        lowerCAmelCase_:       int         =  0
        lowerCAmelCase_:       Union[str, Any]         =  0
        for _, batch in enumerate(snake_case__				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           lowerCAmelCase_:       Tuple         =  {k: v.to(accelerator.device				) for k, v in batch.items()}
           lowerCAmelCase_:       List[Any]         =  (batch["image"] - mean) / std
           with torch.no_grad():
              lowerCAmelCase_:       List[str]         =  model(snake_case__				)
           lowerCAmelCase_:       Union[str, Any]         =  outputs.argmax(dim=-1				)
           lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  accelerator.gather_for_metrics((predictions, batch["label"])				)
           lowerCAmelCase_:       Any         =  predictions == references
           num_elems += accurate_preds.shape[0]
           accurate += accurate_preds.long().sum()
  
        lowerCAmelCase_:       List[str]         =  accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''				)
  def 		snake_case__  (		):
  
     '''simple docstring'''
  
  
  
  
  
     lowerCAmelCase_:       Optional[int]         =  argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument("--data_dir"       ,							required=snake_case__       ,							help="The data folder on disk."				)
     parser.add_argument(
         "--mixed_precision"       ,							type=snake_case__       ,							default=snake_case__       ,							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(
         "--checkpointing_steps"       ,							type=snake_case__       ,							default=snake_case__       ,							help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."       ,							)
     parser.add_argument("--cpu"       ,							action="store_true"       ,							help="If passed, will train on the CPU."				)
     lowerCAmelCase_:       List[Any]         =  parser.parse_args()
     lowerCAmelCase_:       Any         =  {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
     training_function(snake_case__       ,							snake_case__				)
  
  
  if __name__ == "__main__":
   main()
style_context: import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
   import jax
   import jax.numpy as jnp
  
   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel
  
  if is_torch_available():
   import torch
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=14					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	):
         lowerCAmelCase_:       int         =  parent
         lowerCAmelCase_:       Tuple         =  batch_size
         lowerCAmelCase_:       int         =  seq_length
         lowerCAmelCase_:       Any         =  is_training
         lowerCAmelCase_:       Any         =  use_input_mask
         lowerCAmelCase_:       Optional[Any]         =  use_token_type_ids
         lowerCAmelCase_:       str         =  use_labels
         lowerCAmelCase_:       Tuple         =  vocab_size
         lowerCAmelCase_:       List[Any]         =  hidden_size
         lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
         lowerCAmelCase_:       List[str]         =  num_attention_heads
         lowerCAmelCase_:       int         =  intermediate_size
         lowerCAmelCase_:       Tuple         =  hidden_act
         lowerCAmelCase_:       Dict         =  hidden_dropout_prob
         lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
         lowerCAmelCase_:       Any         =  max_position_embeddings
         lowerCAmelCase_:       Dict         =  initializer_range
         lowerCAmelCase_:       List[Any]         =  None
         lowerCAmelCase_:       List[Any]         =  vocab_size - 1
         lowerCAmelCase_:       Union[str, Any]         =  vocab_size - 1
         lowerCAmelCase_:       int         =  vocab_size - 1
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )
  
         lowerCAmelCase_:       Any         =  None
         if self.use_input_mask:
            lowerCAmelCase_:       Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]   )
  
         lowerCAmelCase_:       Union[str, Any]         =  GPTaConfig(
             vocab_size=self.vocab_size					,	n_embd=self.hidden_size					,	n_layer=self.num_hidden_layers					,	n_head=self.num_attention_heads					,	n_positions=self.max_position_embeddings					,	use_cache=lowerCamelCase__					,	bos_token_id=self.bos_token_id					,	eos_token_id=self.eos_token_id					,	pad_token_id=self.pad_token_id					,	)
  
         return (config, input_ids, input_mask)
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  config_and_inputs
         lowerCAmelCase_:       List[str]         =  {"input_ids": input_ids, "attention_mask": attention_mask}
         return config, inputs_dict
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
  
         lowerCAmelCase_:       List[str]         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
         lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	vocab_size=2   )
  
         return (
             config,
             input_ids,
             attention_mask,
             encoder_hidden_states,
             encoder_attention_mask,
         )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[Any]         =  20
         lowerCAmelCase_:       Union[str, Any]         =  model_class_name(lowerCamelCase__   )
  
         lowerCAmelCase_:       List[str]         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
         lowerCAmelCase_:       Any         =  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"   )
  
         lowerCAmelCase_:       Union[str, Any]         =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
         lowerCAmelCase_:       Optional[Any]         =  model(
             input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       Any         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       Optional[int]         =  model(
             input_ids[:, -1:]					,	attention_mask=lowerCamelCase__					,	past_key_values=outputs_cache.past_key_values					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       List[Any]         =  model(lowerCamelCase__   )
  
         lowerCAmelCase_:       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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Union[str, Any]         =  20
         lowerCAmelCase_:       List[str]         =  model_class_name(lowerCamelCase__   )
  
         lowerCAmelCase_:       Any         =  jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]					,	axis=-1					,	)
  
         lowerCAmelCase_:       str         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
         lowerCAmelCase_:       List[Any]         =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
  
         lowerCAmelCase_:       List[Any]         =  model(
             input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
         lowerCAmelCase_:       Dict         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       Dict         =  model(
             input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       Union[str, Any]         =  model(lowerCamelCase__					,	attention_mask=lowerCamelCase__   )
  
         lowerCAmelCase_:       List[str]         =  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 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Optional[int]        =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Any         =  FlaxGPTaModelTester(self   )
  
  
  
  
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained("gpt2"					,	pad_token="</s>"					,	padding_side="left"   )
         lowerCAmelCase_:       Optional[int]         =  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=lowerCamelCase__					,	truncation=lowerCamelCase__   )
  
         lowerCAmelCase_:       List[Any]         =  FlaxGPTaLMHeadModel.from_pretrained("gpt2"   )
         lowerCAmelCase_:       List[str]         =  False
         lowerCAmelCase_:       int         =  model.config.eos_token_id
  
         lowerCAmelCase_:       Optional[int]         =  jax.jit(model.generate   )
  
         lowerCAmelCase_:       Any         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences
  
         lowerCAmelCase_:       Any         =  tokenizer.batch_decode(lowerCamelCase__					,	skip_special_tokens=lowerCamelCase__   )
  
         lowerCAmelCase_:       Dict         =  [
             "Hello this is a long string of words. I'm going to start with the first one.\n",
             "Hey, I'm not sure if I'm going to be able to do",
         ]
  
         self.assertListEqual(lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      @is_pt_flax_cross_test
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       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__   ):
               # prepare inputs
               lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
               lowerCAmelCase_:       str         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               lowerCAmelCase_:       Dict         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
               lowerCAmelCase_:       Union[str, Any]         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )
  
               lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  pt_inputs["input_ids"].shape
               lowerCAmelCase_:       str         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
               for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                  lowerCAmelCase_:       List[str]         =  0
                  lowerCAmelCase_:       int         =  1
                  lowerCAmelCase_:       List[Any]         =  0
                  lowerCAmelCase_:       List[Any]         =  1
               lowerCAmelCase_:       Tuple         =  pt_model_class(lowerCamelCase__   ).eval()
               lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )
  
               lowerCAmelCase_:       int         =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	lowerCamelCase__   )
               lowerCAmelCase_:       Optional[int]         =  fx_state
  
               with torch.no_grad():
                  lowerCAmelCase_:       Union[str, Any]         =  pt_model(**lowerCamelCase__   ).to_tuple()
  
               lowerCAmelCase_:       List[str]         =  fx_model(**lowerCamelCase__   ).to_tuple()
               self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                  pt_model.save_pretrained(lowerCamelCase__   )
                  lowerCAmelCase_:       Dict         =  model_class.from_pretrained(lowerCamelCase__					,	from_pt=lowerCamelCase__   )
  
               lowerCAmelCase_:       List[Any]         =  fx_model_loaded(**lowerCamelCase__   ).to_tuple()
               self.assertEqual(
                   len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output_loaded, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output_loaded[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
  
  
  
      @is_pt_flax_cross_test
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__   ):
               # prepare inputs
               lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
               lowerCAmelCase_:       int         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               lowerCAmelCase_:       List[str]         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
               lowerCAmelCase_:       Dict         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )
  
               lowerCAmelCase_:       Dict         =  pt_model_class(lowerCamelCase__   ).eval()
               lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )
  
               lowerCAmelCase_:       Optional[Any]         =  load_flax_weights_in_pytorch_model(lowerCamelCase__					,	fx_model.params   )
               lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  pt_inputs["input_ids"].shape
               lowerCAmelCase_:       Optional[Any]         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
               for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                  lowerCAmelCase_:       Dict         =  0
                  lowerCAmelCase_:       List[Any]         =  1
                  lowerCAmelCase_:       Dict         =  0
                  lowerCAmelCase_:       Optional[int]         =  1
  
               # make sure weights are tied in PyTorch
               pt_model.tie_weights()
  
               with torch.no_grad():
                  lowerCAmelCase_:       Any         =  pt_model(**lowerCamelCase__   ).to_tuple()
  
               lowerCAmelCase_:       Union[str, Any]         =  fx_model(**lowerCamelCase__   ).to_tuple()
               self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                  fx_model.save_pretrained(lowerCamelCase__   )
                  lowerCAmelCase_:       str         =  pt_model_class.from_pretrained(lowerCamelCase__					,	from_flax=lowerCamelCase__   )
  
               with torch.no_grad():
                  lowerCAmelCase_:       int         =  pt_model_loaded(**lowerCamelCase__   ).to_tuple()
  
               self.assertEqual(
                   len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("gpt2"					,	from_pt=lowerCamelCase__   )
            lowerCAmelCase_:       int         =  model(np.ones((1, 1)   )   )
            self.assertIsNotNone(lowerCamelCase__   )