




"""simple docstring"""



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch




class 			UpperCAmelCase_						:




    def __init__(					self		: Any			,				snake_case_		: List[str]			,				snake_case_		: List[str]=14			,				snake_case_		: Any=7			,				snake_case_		: str=True			,				snake_case_		: str=True			,				snake_case_		: List[str]=False			,				snake_case_		: int=True			,				snake_case_		: Dict=99			,				snake_case_		: Dict=32			,				snake_case_		: Tuple=4			,				snake_case_		: Union[str, Any]=4			,				snake_case_		: List[str]=[[["global", "local"], 2]]			,				snake_case_		: Dict=37			,				snake_case_		: Dict="gelu"			,				snake_case_		: Dict=0.1			,				snake_case_		: Tuple=0.1			,				snake_case_		: Any=512			,				snake_case_		: int=7			,				snake_case_		: str=0.02			,				)   ->							Any:




     '''simple docstring'''

     A__ =       parent
     A__ =       batch_size
     A__ =       seq_length
     A__ =       is_training
     A__ =       use_input_mask
     A__ =       use_token_type_ids
     A__ =       use_labels
     A__ =       vocab_size
     A__ =       hidden_size
     A__ =       num_hidden_layers
     A__ =       num_attention_heads
     A__ =       attention_types
     A__ =       intermediate_size
     A__ =       hidden_act
     A__ =       hidden_dropout_prob
     A__ =       attention_probs_dropout_prob
     A__ =       max_position_embeddings
     A__ =       window_size
     A__ =       initializer_range
     A__ =       None
     A__ =       vocab_size - 1
     A__ =       vocab_size - 1
     A__ =       vocab_size - 1




    def 							__magic_name__							(					self		: Tuple   )   ->							Optional[Any]:




     '''simple docstring'''

     A__ =       ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size   )

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

     A__ =       GPTNeoConfig(
         vocab_size=self.vocab_size			,				hidden_size=self.hidden_size			,				num_layers=self.num_hidden_layers			,				num_heads=self.num_attention_heads			,				max_position_embeddings=self.max_position_embeddings			,				use_cache=snake_case_			,				bos_token_id=self.bos_token_id			,				eos_token_id=self.eos_token_id			,				pad_token_id=self.pad_token_id			,				window_size=self.window_size			,				attention_types=self.attention_types			,				)

     return (config, input_ids, input_mask)




    def 							__magic_name__							(					self		: List[Any]   )   ->							Union[str, Any]:




     '''simple docstring'''

     A__ =       self.prepare_config_and_inputs()
     A__,   A__,   A__ =       config_and_inputs
     A__ =       {"input_ids": input_ids, "attention_mask": attention_mask}
     return config, inputs_dict




    def 							__magic_name__							(					self		: Union[str, Any]			,				snake_case_		: int			,				snake_case_		: Union[str, Any]			,				snake_case_		: List[Any]			,				snake_case_		: Union[str, Any]   )   ->							Optional[Any]:




     '''simple docstring'''

     A__ =       20
     A__ =       model_class_name(snake_case_   )

     A__ =       model.init_cache(input_ids.shape[0]			,				snake_case_   )
     A__ =       jnp.ones((input_ids.shape[0], max_decoder_length)			,				dtype="i4"   )

     A__ =       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1   )[None, :]			,				(input_ids.shape[0], input_ids.shape[-1] - 1)   )
     A__ =       model(
         input_ids[:, :-1]			,				attention_mask=snake_case_			,				past_key_values=snake_case_			,				position_ids=snake_case_			,				)

     A__ =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,				dtype="i4"   )
     A__ =       model(
         input_ids[:, -1:]			,				attention_mask=snake_case_			,				past_key_values=outputs_cache.past_key_values			,				position_ids=snake_case_			,				)

     A__ =       model(snake_case_   )

     A__ =       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 							__magic_name__							(					self		: Tuple			,				snake_case_		: int			,				snake_case_		: List[str]			,				snake_case_		: Tuple			,				snake_case_		: List[str]   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       20
     A__ =       model_class_name(snake_case_   )

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

     A__ =       model.init_cache(input_ids.shape[0]			,				snake_case_   )
     A__ =       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1   )[None, :]			,				(input_ids.shape[0], input_ids.shape[-1] - 1)   )

     A__ =       model(
         input_ids[:, :-1]			,				attention_mask=snake_case_			,				past_key_values=snake_case_			,				position_ids=snake_case_			,				)
     A__ =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,				dtype="i4"   )
     A__ =       model(
         input_ids[:, -1:]			,				past_key_values=outputs_cache.past_key_values			,				attention_mask=snake_case_			,				position_ids=snake_case_			,				)

     A__ =       model(snake_case_			,				attention_mask=snake_case_   )

     A__ =       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 			UpperCAmelCase_						(		A_,     A_,     unittest.TestCase							):
    lowercase__		      =   (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
    lowercase__		      =   (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




    def 							__magic_name__							(					self		: str   )   ->							List[Any]:




     '''simple docstring'''

     A__ =       FlaxGPTNeoModelTester(self   )




    def 							__magic_name__							(					self		: Optional[int]   )   ->							Optional[int]:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__,   A__,   A__ =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(snake_case_			,				snake_case_			,				snake_case_			,				snake_case_   )




    def 							__magic_name__							(					self		: Union[str, Any]   )   ->							List[Any]:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__,   A__,   A__ =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          snake_case_			,				snake_case_			,				snake_case_			,				snake_case_   )




    @slow
    def 							__magic_name__							(					self		: Any   )   ->							Union[str, Any]:




     '''simple docstring'''

     A__ =       GPTaTokenizer.from_pretrained("gpt2"			,				pad_token="<|endoftext|>"			,				padding_side="left"   )
     A__ =       tokenizer(["Hello this is a long string", "Hey"]			,				return_tensors="np"			,				padding=snake_case_			,				truncation=snake_case_   )

     A__ =       FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"   )
     A__ =       False
     A__ =       model.config.eos_token_id

     A__ =       jax.jit(model.generate   )

     A__ =       jit_generate(
         inputs["input_ids"]			,				attention_mask=inputs["attention_mask"]			,				pad_token_id=tokenizer.pad_token_id   ).sequences

     A__ =       tokenizer.batch_decode(snake_case_			,				skip_special_tokens=snake_case_   )

     A__ =       [
         "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(snake_case_			,				snake_case_   )




    @is_pt_flax_cross_test
    def 							__magic_name__							(					self		: Dict   )   ->							Any:




     '''simple docstring'''

     A__,   A__ =       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
       A__ =       self._prepare_for_class(snake_case_			,				snake_case_   )
       A__ =       {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       A__ =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
       A__ =       getattr(snake_case_			,				snake_case_   )

       A__,   A__ =       pt_inputs["input_ids"].shape
       A__ =       np.random.randint(0			,				seq_length - 1			,				size=(batch_size,)   )
       for batch_idx, start_index in enumerate(snake_case_   ):
        A__ =       0
        A__ =       1
        A__ =       0
        A__ =       1
       A__ =       pt_model_class(snake_case_   ).eval()
       A__ =       model_class(snake_case_			,				dtype=jnp.floataa   )

       A__ =       convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,				snake_case_   )
       A__ =       fx_state

       with torch.no_grad():
        A__ =       pt_model(**snake_case_   ).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
        pt_model.save_pretrained(snake_case_   )
        A__ =       model_class.from_pretrained(snake_case_			,				from_pt=snake_case_   )

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




    @is_pt_flax_cross_test
    def 							__magic_name__							(					self		: Optional[Any]   )   ->							Union[str, Any]:




     '''simple docstring'''

     A__,   A__ =       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
       A__ =       self._prepare_for_class(snake_case_			,				snake_case_   )
       A__ =       {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       A__ =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
       A__ =       getattr(snake_case_			,				snake_case_   )

       A__ =       pt_model_class(snake_case_   ).eval()
       A__ =       model_class(snake_case_			,				dtype=jnp.floataa   )

       A__ =       load_flax_weights_in_pytorch_model(snake_case_			,				fx_model.params   )
       A__,   A__ =       pt_inputs["input_ids"].shape
       A__ =       np.random.randint(0			,				seq_length - 1			,				size=(batch_size,)   )
       for batch_idx, start_index in enumerate(snake_case_   ):
        A__ =       0
        A__ =       1
        A__ =       0
        A__ =       1

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

       with torch.no_grad():
        A__ =       pt_model(**snake_case_   ).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
        fx_model.save_pretrained(snake_case_   )
        A__ =       pt_model_class.from_pretrained(snake_case_			,				from_flax=snake_case_   )

       with torch.no_grad():
        A__ =       pt_model_loaded(**snake_case_   ).to_tuple()

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




    @slow
    def 							__magic_name__							(					self		: Union[str, Any]   )   ->							Tuple:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__ =       model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"   )
      A__ =       model(np.ones((1, 1)   )   )
      self.assertIsNotNone(snake_case_   )











"""simple docstring"""



from transformers import PretrainedConfig




class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''custom'''




    def __init__(					self		: Any			,				snake_case_		: List[Any]=1			,				**snake_case_		: Any   )   ->							str:




     '''simple docstring'''

     A__ =       attribute
     super().__init__(**snake_case_   )






class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''custom'''




    def __init__(					self		: Any			,				snake_case_		: Tuple=1			,				**snake_case_		: Dict   )   ->							Any:




     '''simple docstring'''

     A__ =       attribute





