
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


_A							=       torch.cuda.is_available()
_A							=       [' Sam ate lunch today.', 'Sams lunch ingredients.']
_A							=       ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_A							=       {
    '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-0_8,
    '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__					(    a__	:	Union[str, Any]						,   a__	:	Dict  )		->						Optional[Any]:
       UpperCamelCase_											=							"""\n""".join(SCREAMING_SNAKE_CASE__  )
       Path(SCREAMING_SNAKE_CASE__  ).open("""w"""  ).writelines(SCREAMING_SNAKE_CASE__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]  )		->						Optional[int]:




       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 		lowercase_							(							snake_case_					):

 @classmethod
 def lowerCamelCase_					(   cls  ):




        """simple docstring"""


        return cls

 @require_torch_multi_gpu
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """no_teacher""": True,
            """freeze_encoder""": True,
            """gpus""": 2,
            """overwrite_output_dir""": True,
            """sortish_sampler""": True,
        }
        self._test_distiller_cli_fork(lowercase_  ,						check_contents=lowercase_  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=True  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """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(lowercase_  )
        UpperCamelCase_											=							CHEAP_ARGS.copy()
        UpperCamelCase_											=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
        UpperCamelCase_											=							self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=lowercase_  ,						output_dir=lowercase_  ,						**lowercase_  )

        def convert(__UpperCamelCase  ,						__UpperCamelCase  ):
               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_											=							[x for x in (convert(lowercase_  ,						lowercase_  ) for k, v in args_d.items()) if len(lowercase_  )]
        UpperCamelCase_											=							[sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
        execute_subprocess_async(lowercase_  ,						env=self.get_env()  )

        UpperCamelCase_											=							os.listdir(lowercase_  )
        UpperCamelCase_											=							{os.path.basename(lowercase_  ) for p in contents}
        UpperCamelCase_											=							[p for p in contents if p.endswith("""ckpt"""  )]
        assert len(lowercase_  ) > 0

        self.assertIn("""test_generations.txt"""  ,						lowercase_  )
        self.assertIn("""test_results.txt"""  ,						lowercase_  )

        # get the following from the module, (we don't have access to `model` here)
        UpperCamelCase_											=							os.path.join(lowercase_  ,						"""metrics.json"""  )
        UpperCamelCase_											=							"""rouge2"""

        UpperCamelCase_											=							load_json(lowercase_  )
        # {'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(lowercase_  )
        UpperCamelCase_											=							metrics["""val"""][-1]
        self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]  ,						0.01  )
        self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']  ,						lowercase_  )
        self.assertEqual(len(metrics["""test"""]  )  ,						1  )
        UpperCamelCase_											=							int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1  )
        self.assertEqual(len(metrics["""val"""]  )  ,						lowercase_  )








import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
		import numpy as np

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
		import torch






@require_flax
class 	_a			:






			def __init__(    self    ,  lowercase_    ,  lowercase_=14    ,  lowercase_=7    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=99    ,  lowercase_=32    ,  lowercase_=5    ,  lowercase_=4    ,  lowercase_=37    ,  lowercase_="gelu"    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=512    ,  lowercase_=0.0_2    ,  lowercase_=None    ,  )	->					Tuple:
									lowerCAmelCase	:   Union[str, Any]					     =					parent
									lowerCAmelCase	:   Optional[Any]					     =					batch_size
									lowerCAmelCase	:   Tuple					     =					seq_length
									lowerCAmelCase	:   Optional[Any]					     =					is_training
									lowerCAmelCase	:   Union[str, Any]					     =					use_input_mask
									lowerCAmelCase	:   int					     =					use_labels
									lowerCAmelCase	:   List[str]					     =					vocab_size
									lowerCAmelCase	:   List[Any]					     =					d_model
									lowerCAmelCase	:   List[Any]					     =					num_hidden_layers
									lowerCAmelCase	:   int					     =					num_attention_heads
									lowerCAmelCase	:   Optional[Any]					     =					ffn_dim
									lowerCAmelCase	:   str					     =					activation_function
									lowerCAmelCase	:   Any					     =					activation_dropout
									lowerCAmelCase	:   List[str]					     =					attention_dropout
									lowerCAmelCase	:   str					     =					max_position_embeddings
									lowerCAmelCase	:   Dict					     =					initializer_range
									lowerCAmelCase	:   str					     =					None
									lowerCAmelCase	:   Any					     =					0
									lowerCAmelCase	:   Tuple					     =					2
									lowerCAmelCase	:   Dict					     =					1






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Tuple					     =					np.clip(ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )    ,  3    ,  self.vocab_size )

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

									lowerCAmelCase	:   Tuple					     =					XGLMConfig(
									    vocab_size=self.vocab_size    ,  d_model=self.hidden_size    ,  num_layers=self.num_hidden_layers    ,  attention_heads=self.num_attention_heads    ,  ffn_dim=self.ffn_dim    ,  activation_function=self.activation_function    ,  activation_dropout=self.activation_dropout    ,  attention_dropout=self.attention_dropout    ,  max_position_embeddings=self.max_position_embeddings    ,  initializer_range=self.initializer_range    ,  use_cache=lowercase_    ,  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 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Dict					     =					self.prepare_config_and_inputs()
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					config_and_inputs
									lowerCAmelCase	:   int					     =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   str					     =					self.prepare_config_and_inputs()

									lowerCAmelCase	:   Union[str, Any]					     =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
									lowerCAmelCase	:   List[str]					     =					ids_tensor([self.batch_size, self.seq_length]    ,  vocab_size=2 )

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






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   str					     =					20
									lowerCAmelCase	:   List[str]					     =					model_class_name(lowercase_ )

									lowerCAmelCase	:   Any					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   Optional[int]					     =					jnp.ones((input_ids.shape[0], max_decoder_length)    ,  dtype="""i4""" )

									lowerCAmelCase	:   List[str]					     =					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
									lowerCAmelCase	:   Optional[int]					     =					model(
									    input_ids[:, :-1]    ,  attention_mask=lowercase_    ,  past_key_values=lowercase_    ,  position_ids=lowercase_    ,  )

									lowerCAmelCase	:   List[Any]					     =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,  dtype="""i4""" )
									lowerCAmelCase	:   str					     =					model(
									    input_ids[:, -1:]    ,  attention_mask=lowercase_    ,  past_key_values=outputs_cache.past_key_values    ,  position_ids=lowercase_    ,  )

									lowerCAmelCase	:   List[str]					     =					model(lowercase_ )

									lowerCAmelCase	:   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 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					List[str]:
									lowerCAmelCase	:   Union[str, Any]					     =					20
									lowerCAmelCase	:   List[Any]					     =					model_class_name(lowercase_ )

									lowerCAmelCase	:   int					     =					jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]    ,  axis=-1    ,  )

									lowerCAmelCase	:   int					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   int					     =					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

									lowerCAmelCase	:   Optional[int]					     =					model(
									    input_ids[:, :-1]    ,  attention_mask=lowercase_    ,  past_key_values=lowercase_    ,  position_ids=lowercase_    ,  )
									lowerCAmelCase	:   Optional[int]					     =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,  dtype="""i4""" )
									lowerCAmelCase	:   List[Any]					     =					model(
									    input_ids[:, -1:]    ,  past_key_values=outputs_cache.past_key_values    ,  attention_mask=lowercase_    ,  position_ids=lowercase_    ,  )

									lowerCAmelCase	:   Union[str, Any]					     =					model(lowercase_    ,  attention_mask=lowercase_ )
									lowerCAmelCase	:   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_sentencepiece
@require_flax
class 	_a			(      snake_case_     , snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   List[str]	     =		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			_UpperCamelCase:   str	     =		(FlaxXGLMForCausalLM,) if is_flax_available() else ()






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Any					     =					FlaxXGLMModelTester(self )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			@slow
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   List[str]					     =					XGLMTokenizer.from_pretrained("""XGLM"""    ,  padding_side="""left""" )
									lowerCAmelCase	:   int					     =					tokenizer(["""Hello this is a long string""", """Hey"""]    ,  return_tensors="""np"""    ,  padding=lowercase_    ,  truncation=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
									lowerCAmelCase	:   Union[str, Any]					     =					1
									lowerCAmelCase	:   Optional[Any]					     =					False

									lowerCAmelCase	:   int					     =					jax.jit(model.generate )

									lowerCAmelCase	:   Optional[Any]					     =					jit_generate(inputs["""input_ids"""]    ,  attention_mask=inputs["""attention_mask"""] ).sequences

									lowerCAmelCase	:   List[str]					     =					tokenizer.batch_decode(lowercase_    ,  skip_special_tokens=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					[
									    """Hello this is a long string of questions, but I'm not sure if I'm""",
									    """Hey, I'm a newbie to the forum and I'""",
									]

									self.assertListEqual(lowercase_    ,  lowercase_ )






			@is_pt_flax_cross_test
			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase     ,	lowerCAmelCase	:   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	:   Union[str, Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   List[Any]					     =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

																					lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					pt_inputs["""input_ids"""].shape
																					lowerCAmelCase	:   Tuple					     =					np.random.randint(0    ,  seq_length - 1    ,  size=(batch_size,) )
																					for batch_idx, start_index in enumerate(lowercase_ ):
																											lowerCAmelCase	:   List[str]					     =					0
																											lowerCAmelCase	:   List[Any]					     =					1
																											lowerCAmelCase	:   str					     =					0
																											lowerCAmelCase	:   Union[str, Any]					     =					1
																					lowerCAmelCase	:   List[Any]					     =					pt_model_class(lowercase_ ).eval()
																					# Flax models don't use the `use_cache` option and cache is not returned as a default.
																					# So we disable `use_cache` here for PyTorch model.
																					lowerCAmelCase	:   Optional[int]					     =					False
																					lowerCAmelCase	:   Optional[Any]					     =					model_class(lowercase_    ,  dtype=jnp.floataa )
																					lowerCAmelCase	:   Union[str, Any]					     =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,  lowercase_ )
																					lowerCAmelCase	:   List[str]					     =					fx_state

																					with torch.no_grad():
																											lowerCAmelCase	:   Dict					     =					pt_model(**lowercase_ ).to_tuple()

																					lowerCAmelCase	:   Tuple					     =					fx_model(**lowercase_ ).to_tuple()
																					self.assertEqual(len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(lowercase_ )
																											lowerCAmelCase	:   Optional[int]					     =					model_class.from_pretrained(lowercase_    ,  from_pt=lowercase_ )

																					lowerCAmelCase	:   Optional[int]					     =					fx_model_loaded(**lowercase_ ).to_tuple()
																					self.assertEqual(
																					    len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output_loaded, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output_loaded[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )






			@is_pt_flax_cross_test
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					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	:   List[Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   Tuple					     =					{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	:   Optional[int]					     =					getattr(lowercase_    ,  lowercase_ )

																					lowerCAmelCase	:   Optional[Any]					     =					pt_model_class(lowercase_ ).eval()
																					lowerCAmelCase	:   Any					     =					False
																					lowerCAmelCase	:   Optional[int]					     =					model_class(lowercase_    ,  dtype=jnp.floataa )

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

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

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					pt_model(**lowercase_ ).to_tuple()

																					lowerCAmelCase	:   Optional[Any]					     =					fx_model(**lowercase_ ).to_tuple()
																					self.assertEqual(len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(lowercase_ )
																											lowerCAmelCase	:   List[str]					     =					pt_model_class.from_pretrained(lowercase_    ,  from_flax=lowercase_ )

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					pt_model_loaded(**lowercase_ ).to_tuple()

																					self.assertEqual(
																					    len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )






			@slow
			def 							_snake_case   (    self )	->					str:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase	:   Optional[Any]					     =					model_class_name.from_pretrained("""facebook/xglm-564M""" )
															lowerCAmelCase	:   int					     =					model(np.ones((1, 1) ) )
															self.assertIsNotNone(lowercase_ )

