






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, 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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch
class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A=14							,  _A=7							,  _A=True							,  _A=True							,  _A=False							,  _A=True							,  _A=99							,  _A=32							,  _A=4							,  _A=4							,  _A=4							,  _A=37							,  _A="gelu"							,  _A=0.1							,  _A=0.1							,  _A=512							,  _A=0.02							,  ) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				parent
     _UpperCAmelCase				:      Union[str, Any]			  =				batch_size
     _UpperCAmelCase				:      str			  =				seq_length
     _UpperCAmelCase				:      Any			  =				is_training
     _UpperCAmelCase				:      Optional[Any]			  =				use_input_mask
     _UpperCAmelCase				:      List[str]			  =				use_token_type_ids
     _UpperCAmelCase				:      Tuple			  =				use_labels
     _UpperCAmelCase				:      Union[str, Any]			  =				vocab_size
     _UpperCAmelCase				:      Optional[Any]			  =				hidden_size
     _UpperCAmelCase				:      Union[str, Any]			  =				rotary_dim
     _UpperCAmelCase				:      Optional[int]			  =				num_hidden_layers
     _UpperCAmelCase				:      Optional[int]			  =				num_attention_heads
     _UpperCAmelCase				:      Any			  =				intermediate_size
     _UpperCAmelCase				:      List[str]			  =				hidden_act
     _UpperCAmelCase				:      Tuple			  =				hidden_dropout_prob
     _UpperCAmelCase				:      Dict			  =				attention_probs_dropout_prob
     _UpperCAmelCase				:      Optional[int]			  =				max_position_embeddings
     _UpperCAmelCase				:      Any			  =				initializer_range
     _UpperCAmelCase				:      Dict			  =				None
     _UpperCAmelCase				:      List[Any]			  =				vocab_size - 1
     _UpperCAmelCase				:      Optional[int]			  =				vocab_size - 1
     _UpperCAmelCase				:      Any			  =				vocab_size - 1


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size)

     _UpperCAmelCase				:      int			  =				None
     if self.use_input_mask:
         _UpperCAmelCase				:      int			  =				random_attention_mask([self.batch_size, self.seq_length])

     _UpperCAmelCase				:      int			  =				GPTJConfig(
         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=_A							,  bos_token_id=self.bos_token_id							,  eos_token_id=self.eos_token_id							,  pad_token_id=self.pad_token_id							,  rotary_dim=self.rotary_dim							,  )

     return (config, input_ids, input_mask)


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				20
     _UpperCAmelCase				:      List[Any]			  =				model_class_name(_A)

     _UpperCAmelCase				:      Optional[Any]			  =				model.init_cache(input_ids.shape[0]							,  _A)
     _UpperCAmelCase				:      int			  =				jnp.ones((input_ids.shape[0], max_decoder_length)							,  dtype='''i4''')

     _UpperCAmelCase				:      Union[str, Any]			  =				jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1)[None, :]							,  (input_ids.shape[0], input_ids.shape[-1] - 1))
     _UpperCAmelCase				:      Tuple			  =				model(
         input_ids[:, :-1]							,  attention_mask=_A							,  past_key_values=_A							,  position_ids=_A							,  )

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

     _UpperCAmelCase				:      List[str]			  =				model(_A)

     _UpperCAmelCase				:      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}''')


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				20
     _UpperCAmelCase				:      List[str]			  =				model_class_name(_A)

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

     _UpperCAmelCase				:      List[str]			  =				model.init_cache(input_ids.shape[0]							,  _A)
     _UpperCAmelCase				:      int			  =				jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1)[None, :]							,  (input_ids.shape[0], input_ids.shape[-1] - 1))

     _UpperCAmelCase				:      List[str]			  =				model(
         input_ids[:, :-1]							,  attention_mask=_A							,  past_key_values=_A							,  position_ids=_A							,  )
     _UpperCAmelCase				:      List[str]			  =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,  dtype='''i4''')
     _UpperCAmelCase				:      str			  =				model(
         input_ids[:, -1:]							,  past_key_values=outputs_cache.past_key_values							,  attention_mask=_A							,  position_ids=_A							,  )

     _UpperCAmelCase				:      Dict			  =				model(_A							,  attention_mask=_A)

     _UpperCAmelCase				:      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 							A_    (						__lowercase ,   __lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 _SCREAMING_SNAKE_CASE							:			List[str] =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				FlaxGPTJModelTester(self)


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     for model_class_name in self.all_model_classes:
         _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(_A							,  _A							,  _A							,  _A)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     for model_class_name in self.all_model_classes:
         _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Tuple			  =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             _A							,  _A							,  _A							,  _A)


 @tooslow
 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				GPTaTokenizer.from_pretrained('''gpt2'''							,  pad_token='''<|endoftext|>'''							,  padding_side='''left''')
     _UpperCAmelCase				:      Any			  =				tokenizer(['''Hello this is a long string''', '''Hey''']							,  return_tensors='''np'''							,  padding=_A							,  truncation=_A)

     _UpperCAmelCase				:      Dict			  =				FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
     _UpperCAmelCase				:      int			  =				False
     _UpperCAmelCase				:      Optional[Any]			  =				model.config.eos_token_id

     _UpperCAmelCase				:      Optional[Any]			  =				jax.jit(model.generate)

     _UpperCAmelCase				:      Optional[int]			  =				jit_generate(
         inputs['''input_ids''']							,  attention_mask=inputs['''attention_mask''']							,  pad_token_id=tokenizer.pad_token_id).sequences

     _UpperCAmelCase				:      List[str]			  =				tokenizer.batch_decode(_A							,  skip_special_tokens=_A)

     _UpperCAmelCase				:      int			  =				[
         '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
         '''Hey, I\'m a little late to the party. I\'m going to''',
     ]

     self.assertListEqual(_A							,  _A)


 @is_pt_flax_cross_test
 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      List[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				:      List[str]			  =				self._prepare_for_class(_A							,  _A)
             _UpperCAmelCase				:      int			  =				{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

             _UpperCAmelCase				:      List[str]			  =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,  _A)
             _UpperCAmelCase				:      List[Any]			  =				fx_state

             with torch.no_grad():
                 _UpperCAmelCase				:      List[Any]			  =				pt_model(**_A).to_tuple()

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(_A)
                 _UpperCAmelCase				:      Dict			  =				model_class.from_pretrained(_A							,  from_pt=_A)

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


 @is_pt_flax_cross_test
 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _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(_A							,  _A)
             _UpperCAmelCase				:      List[str]			  =				{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				:      Union[str, Any]			  =				getattr(_A							,  _A)

             _UpperCAmelCase				:      str			  =				pt_model_class(_A).eval()
             _UpperCAmelCase				:      List[Any]			  =				model_class(_A							,  dtype=jnp.floataa)

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

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

             with torch.no_grad():
                 _UpperCAmelCase				:      Dict			  =				pt_model(**_A).to_tuple()

             _UpperCAmelCase				:      Union[str, Any]			  =				fx_model(**_A).to_tuple()
             self.assertEqual(len(_A)							,  len(_A)							,  '''Output lengths differ between Flax and PyTorch''')
             for fx_output, pt_output in zip(_A							,  _A):
                 self.assert_almost_equals(fx_output[:, -1]							,  pt_output[:, -1].numpy()							,  4e-2)

             with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(_A)
                 _UpperCAmelCase				:      int			  =				pt_model_class.from_pretrained(_A							,  from_flax=_A)

             with torch.no_grad():
                 _UpperCAmelCase				:      List[Any]			  =				pt_model_loaded(**_A).to_tuple()

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


 @tooslow
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     for model_class_name in self.all_model_classes:
         _UpperCAmelCase				:      Dict			  =				model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
         _UpperCAmelCase				:      int			  =				model(np.ones((1, 1)))
         self.assertIsNotNone(_A)












from math import pow, sqrt

def      _lowerCamelCase	(						*__A    :							float )      ->					bool:
    _UpperCAmelCase				:      str			  =				len(__A ) > 0 and all(value > 0.0 for value in values )
    return result

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A )
        else ValueError('''Input Error: Molar mass values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,		2 )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )


def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(pow(effusion_rate_a / effusion_rate_a		,		2 ) / molar_mass		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )




