





'''simple docstring'''







import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


snake_case_  :							Tuple            =	torch.cuda.is_available()
snake_case_  :							Any            =	[" Sam ate lunch today.", "Sams lunch ingredients."]
snake_case_  :							Tuple            =	["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
snake_case_  :							Optional[int]            =	{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Path,      SCREAMING_SNAKE_CASE__   :      list )			->						Tuple:
						UpperCAmelCase_      :   int      =	'''\n'''.join(SCREAMING_SNAKE_CASE__ )
						Path(SCREAMING_SNAKE_CASE__ ).open('''w''' ).writelines(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						Dict:
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__,      F"""{split}.source""" ),      SCREAMING_SNAKE_CASE__ )
												_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__,      F"""{split}.target""" ),      SCREAMING_SNAKE_CASE__ )
						return tmp_dir


class        __a   (lowerCamelCase  ):

							@classmethod
							def      UpperCAmelCase__						(				cls				:  Union[str, Any] )       ->							Tuple:







													"""simple docstring"""
													return cls

							@require_torch_multi_gpu
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	{
													    '''no_teacher''': True,
													    '''freeze_encoder''': True,
													    '''gpus''': 2,
													    '''overwrite_output_dir''': True,
													    '''sortish_sampler''': True,
													}
													self._test_distiller_cli_fork(__magic_name__							,  check_contents=__magic_name__ )



							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  int=True )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	{
													    '''label_smoothing''': 0.0,
													    '''early_stopping_patience''': -1,
													    '''train_batch_size''': 1,
													    '''eval_batch_size''': 2,
													    '''max_epochs''': 2,
													    '''alpha_mlm''': 0.2,
													    '''alpha_ce''': 0.8,
													    '''do_predict''': True,
													    '''model_name_or_path''': '''sshleifer/tinier_bart''',
													    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
													    '''val_check_interval''': 0.5,
													}
													default_updates.update(__magic_name__ )
													UpperCAmelCase_      :   dict      =	CHEAP_ARGS.copy()
													UpperCAmelCase_      :   List[Any]      =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
													UpperCAmelCase_      :   List[str]      =	self.get_auto_remove_tmp_dir()
													args_d.update(data_dir=__magic_name__							,  output_dir=__magic_name__							,  **__magic_name__ )

													def convert(__magic_name__				:  str							,  __magic_name__				:  Dict ):
																			if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																									return ""
																			if v is False or v is None:
																									return ""
																			if v is True:  # or len(str(v))==0:
																									return F"""--{k}"""
																			return F"""--{k}={v}"""

													UpperCAmelCase_      :   Dict      =	[x for x in (convert(__magic_name__							,  __magic_name__ ) for k, v in args_d.items()) if len(__magic_name__ )]
													UpperCAmelCase_      :   List[Any]      =	[sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
													execute_subprocess_async(__magic_name__							,  env=self.get_env() )

													UpperCAmelCase_      :   int      =	os.listdir(__magic_name__ )
													UpperCAmelCase_      :   Optional[Any]      =	{os.path.basename(__magic_name__ ) for p in contents}
													UpperCAmelCase_      :   Dict      =	[p for p in contents if p.endswith('''ckpt''' )]
													assert len(__magic_name__ ) > 0

													self.assertIn('''test_generations.txt'''							,  __magic_name__ )
													self.assertIn('''test_results.txt'''							,  __magic_name__ )

													# get the following from the module, (we don't have access to `model` here)
													UpperCAmelCase_      :   str      =	os.path.join(__magic_name__							,  '''metrics.json''' )
													UpperCAmelCase_      :   List[str]      =	'''rouge2'''

													UpperCAmelCase_      :   int      =	load_json(__magic_name__ )
													# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
													print(__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	metrics['''val'''][-1]
													self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']							,  0.0_1 )
													self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]							,  __magic_name__ )
													self.assertEqual(len(metrics['''test'''] )							,  1 )
													UpperCAmelCase_      :   Union[str, Any]      =	int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1 )
													self.assertEqual(len(metrics['''val'''] )							,  __magic_name__ )






'''simple docstring'''
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=1_4  ,      _lowerCamelCase=7  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=False  ,      _lowerCamelCase=True  ,      _lowerCamelCase=9_9  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=5  ,      _lowerCamelCase=4  ,      _lowerCamelCase=3_7  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=0.0_2  ,      ):
         __UpperCamelCase						:						int		     =	parent
         __UpperCamelCase						:						Union[str, Any]		     =	batch_size
         __UpperCamelCase						:						Tuple		     =	seq_length
         __UpperCamelCase						:						Tuple		     =	is_training
         __UpperCamelCase						:						str		     =	use_input_mask
         __UpperCamelCase						:						int		     =	use_token_type_ids
         __UpperCamelCase						:						List[Any]		     =	use_labels
         __UpperCamelCase						:						Tuple		     =	vocab_size
         __UpperCamelCase						:						Any		     =	hidden_size
         __UpperCamelCase						:						int		     =	num_hidden_layers
         __UpperCamelCase						:						List[Any]		     =	num_attention_heads
         __UpperCamelCase						:						str		     =	intermediate_size
         __UpperCamelCase						:						Any		     =	hidden_act
         __UpperCamelCase						:						Tuple		     =	hidden_dropout_prob
         __UpperCamelCase						:						Any		     =	attention_probs_dropout_prob
         __UpperCamelCase						:						Union[str, Any]		     =	max_position_embeddings
         __UpperCamelCase						:						Optional[int]		     =	initializer_range
         __UpperCamelCase						:						str		     =	None
         __UpperCamelCase						:						Dict		     =	vocab_size - 1
         __UpperCamelCase						:						Tuple		     =	vocab_size - 1
         __UpperCamelCase						:						int		     =	vocab_size - 1







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size     )

         __UpperCamelCase						:						str		     =	None
         if self.use_input_mask:
             __UpperCamelCase						:						List[Any]		     =	random_attention_mask([self.batch_size, self.seq_length]     )

         __UpperCamelCase						:						Optional[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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Union[str, Any]		     =	self.prepare_config_and_inputs()
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	config_and_inputs
         __UpperCamelCase						:						Optional[Any]		     =	{'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.prepare_config_and_inputs()

         __UpperCamelCase						:						Optional[Any]		     =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
         __UpperCamelCase						:						List[Any]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2     )

         return (
             config,
             input_ids,
             attention_mask,
             encoder_hidden_states,
             encoder_attention_mask,
         )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						str		     =	2_0
         __UpperCamelCase						:						List[Any]		     =	model_class_name(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[int]		     =	model.init_cache(input_ids.shape[0]  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Union[str, Any]		     =	jnp.ones((input_ids.shape[0], max_decoder_length)  ,      dtype='i4'     )

         __UpperCamelCase						:						List[str]		     =	jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )
         __UpperCamelCase						:						List[Any]		     =	model(
             input_ids[:, :-1]  ,      attention_mask=_lowerCamelCase  ,      past_key_values=_lowerCamelCase  ,      position_ids=_lowerCamelCase  ,      )

         __UpperCamelCase						:						int		     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,      dtype='i4'     )
         __UpperCamelCase						:						Tuple		     =	model(
             input_ids[:, -1:]  ,      attention_mask=_lowerCamelCase  ,      past_key_values=outputs_cache.past_key_values  ,      position_ids=_lowerCamelCase  ,      )

         __UpperCamelCase						:						List[str]		     =	model(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[int]		     =	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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Optional[Any]		     =	2_0
         __UpperCamelCase						:						Any		     =	model_class_name(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]  ,      axis=-1  ,      )

         __UpperCamelCase						:						Any		     =	model.init_cache(input_ids.shape[0]  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Optional[Any]		     =	jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )

         __UpperCamelCase						:						List[Any]		     =	model(
             input_ids[:, :-1]  ,      attention_mask=_lowerCamelCase  ,      past_key_values=_lowerCamelCase  ,      position_ids=_lowerCamelCase  ,      )
         __UpperCamelCase						:						Any		     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,      dtype='i4'     )
         __UpperCamelCase						:						Dict		     =	model(
             input_ids[:, -1:]  ,      past_key_values=outputs_cache.past_key_values  ,      attention_mask=_lowerCamelCase  ,      position_ids=_lowerCamelCase  ,      )

         __UpperCamelCase						:						Optional[Any]		     =	model(_lowerCamelCase  ,      attention_mask=_lowerCamelCase     )

         __UpperCamelCase						:						Any		     =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
         self.parent.assertTrue(diff < 1E-3  ,      msg=f"""Max diff is {diff}"""     )






@require_flax
class       __lowercase		(    _lowerCamelCase      ,				_lowerCamelCase      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE__		 =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	FlaxGPTaModelTester(self     )







     def lowerCAmelCase  (		self     ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )







     @slow
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	GPTaTokenizer.from_pretrained('gpt2'  ,      pad_token='</s>'  ,      padding_side='left'     )
         __UpperCamelCase						:						str		     =	tokenizer(['Hello this is a long string', 'Hey']  ,      return_tensors='np'  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase     )

         __UpperCamelCase						:						int		     =	FlaxGPTaLMHeadModel.from_pretrained('gpt2'     )
         __UpperCamelCase						:						Union[str, Any]		     =	False
         __UpperCamelCase						:						Optional[Any]		     =	model.config.eos_token_id

         __UpperCamelCase						:						List[str]		     =	jax.jit(model.generate     )

         __UpperCamelCase						:						List[str]		     =	jit_generate(inputs['input_ids']  ,      attention_mask=inputs['attention_mask']     ).sequences

         __UpperCamelCase						:						Optional[int]		     =	tokenizer.batch_decode(_lowerCamelCase  ,      skip_special_tokens=_lowerCamelCase     )

         __UpperCamelCase						:						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 lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	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
                 __UpperCamelCase						:						Tuple		     =	self._prepare_for_class(_lowerCamelCase  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						Dict		     =	{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 __UpperCamelCase						:						Union[str, Any]		     =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 __UpperCamelCase						:						List[str]		     =	getattr(_lowerCamelCase  ,      _lowerCamelCase     )

                 __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	pt_inputs['input_ids'].shape
                 __UpperCamelCase						:						int		     =	np.random.randint(0  ,      seq_length - 1  ,      size=(batch_size,)     )
                 for batch_idx, start_index in enumerate(_lowerCamelCase     ):
                     __UpperCamelCase						:						int		     =	0
                     __UpperCamelCase						:						Tuple		     =	1
                     __UpperCamelCase						:						Optional[int]		     =	0
                     __UpperCamelCase						:						List[Any]		     =	1
                 __UpperCamelCase						:						str		     =	pt_model_class(_lowerCamelCase     ).eval()
                 __UpperCamelCase						:						Union[str, Any]		     =	model_class(_lowerCamelCase  ,      dtype=jnp.floataa     )

                 __UpperCamelCase						:						Optional[int]		     =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						List[str]		     =	fx_state

                 with torch.no_grad():
                     __UpperCamelCase						:						List[Any]		     =	pt_model(**_lowerCamelCase     ).to_tuple()

                 __UpperCamelCase						:						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     )
                     __UpperCamelCase						:						Any		     =	model_class.from_pretrained(_lowerCamelCase  ,      from_pt=_lowerCamelCase     )

                 __UpperCamelCase						:						Union[str, 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 lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[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
                 __UpperCamelCase						:						Tuple		     =	self._prepare_for_class(_lowerCamelCase  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						int		     =	{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 __UpperCamelCase						:						Optional[Any]		     =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 __UpperCamelCase						:						int		     =	getattr(_lowerCamelCase  ,      _lowerCamelCase     )

                 __UpperCamelCase						:						Optional[Any]		     =	pt_model_class(_lowerCamelCase     ).eval()
                 __UpperCamelCase						:						List[Any]		     =	model_class(_lowerCamelCase  ,      dtype=jnp.floataa     )

                 __UpperCamelCase						:						str		     =	load_flax_weights_in_pytorch_model(_lowerCamelCase  ,      fx_model.params     )
                 __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	pt_inputs['input_ids'].shape
                 __UpperCamelCase						:						Dict		     =	np.random.randint(0  ,      seq_length - 1  ,      size=(batch_size,)     )
                 for batch_idx, start_index in enumerate(_lowerCamelCase     ):
                     __UpperCamelCase						:						Union[str, Any]		     =	0
                     __UpperCamelCase						:						List[str]		     =	1
                     __UpperCamelCase						:						Optional[int]		     =	0
                     __UpperCamelCase						:						Optional[int]		     =	1

                 # make sure weights are tied in PyTorch
                 pt_model.tie_weights()

                 with torch.no_grad():
                     __UpperCamelCase						:						Any		     =	pt_model(**_lowerCamelCase     ).to_tuple()

                 __UpperCamelCase						:						Optional[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     )
                     __UpperCamelCase						:						int		     =	pt_model_class.from_pretrained(_lowerCamelCase  ,      from_flax=_lowerCamelCase     )

                 with torch.no_grad():
                     __UpperCamelCase						:						Dict		     =	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 lowerCAmelCase  (		self     ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase						:						Union[str, Any]		     =	model_class_name.from_pretrained('gpt2'  ,      from_pt=_lowerCamelCase     )
             __UpperCamelCase						:						List[str]		     =	model(np.ones((1, 1)     )     )
             self.assertIsNotNone(_lowerCamelCase     )



