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()
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__   )