
"""simple docstring"""


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  :






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







              """simple docstring"""
              UpperCamelCase					 =		parent
              UpperCamelCase					 =		batch_size
              UpperCamelCase					 =		seq_length
              UpperCamelCase					 =		is_training
              UpperCamelCase					 =		use_input_mask
              UpperCamelCase					 =		use_token_type_ids
              UpperCamelCase					 =		use_labels
              UpperCamelCase					 =		vocab_size
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		rotary_dim
              UpperCamelCase					 =		num_hidden_layers
              UpperCamelCase					 =		num_attention_heads
              UpperCamelCase					 =		intermediate_size
              UpperCamelCase					 =		hidden_act
              UpperCamelCase					 =		hidden_dropout_prob
              UpperCamelCase					 =		attention_probs_dropout_prob
              UpperCamelCase					 =		max_position_embeddings
              UpperCamelCase					 =		initializer_range
              UpperCamelCase					 =		None
              UpperCamelCase					 =		vocab_size - 1
              UpperCamelCase					 =		vocab_size - 1
              UpperCamelCase					 =		vocab_size - 1






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size			)

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

              UpperCamelCase					 =		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=UpperCAmelCase_       ,      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 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  str:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							List[Any]			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		20
              UpperCamelCase					 =		model_class_name(UpperCAmelCase_			)

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

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

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

              UpperCamelCase					 =		model(UpperCAmelCase_			)

              UpperCamelCase					 =		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 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Optional[int]			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		20
              UpperCamelCase					 =		model_class_name(UpperCAmelCase_			)

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

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

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

              UpperCamelCase					 =		model(UpperCAmelCase_       ,      attention_mask=UpperCAmelCase_			)

              UpperCamelCase					 =		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  (      _lowerCAmelCase    ,     _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					str   =			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       UpperCamelCase_		:					Any   =			(FlaxGPTJForCausalLM,) if is_flax_available() else ()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		FlaxGPTJModelTester(self			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  str:







              """simple docstring"""
              for model_class_name in self.all_model_classes:
                     UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
                     self.model_tester.check_use_cache_forward(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Any:







              """simple docstring"""
              for model_class_name in self.all_model_classes:
                     UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
                     self.model_tester.check_use_cache_forward_with_attn_mask(
                         UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)






       @tooslow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Any:







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

              UpperCamelCase					 =		FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"			)
              UpperCamelCase					 =		False
              UpperCamelCase					 =		model.config.eos_token_id

              UpperCamelCase					 =		jax.jit(model.generate			)

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

              UpperCamelCase					 =		tokenizer.batch_decode(UpperCAmelCase_       ,      skip_special_tokens=UpperCAmelCase_			)

              UpperCamelCase					 =		[
                  "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(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @is_pt_flax_cross_test
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Dict:







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		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					 =		self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)
                            UpperCamelCase					 =		{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

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

                            UpperCamelCase					 =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,      UpperCAmelCase_			)
                            UpperCamelCase					 =		fx_state

                            with torch.no_grad():
                                   UpperCamelCase					 =		pt_model(**UpperCAmelCase_			).to_tuple()

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

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

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






       @is_pt_flax_cross_test
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		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					 =		self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)
                            UpperCamelCase					 =		{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

                            UpperCamelCase					 =		pt_model_class(UpperCAmelCase_			).eval()
                            UpperCamelCase					 =		model_class(UpperCAmelCase_       ,      dtype=jnp.floataa			)

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

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

                            with torch.no_grad():
                                   UpperCamelCase					 =		pt_model(**UpperCAmelCase_			).to_tuple()

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

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

                            with torch.no_grad():
                                   UpperCamelCase					 =		pt_model_loaded(**UpperCAmelCase_			).to_tuple()

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






       @tooslow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Any:







              """simple docstring"""
              for model_class_name in self.all_model_classes:
                     UpperCamelCase					 =		model_class_name.from_pretrained("EleutherAI/gpt-j-6B"			)
                     UpperCamelCase					 =		model(np.ones((1, 1)			)			)
                     self.assertIsNotNone(UpperCAmelCase_			)







"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  :
       UpperCamelCase_		:					str
       UpperCamelCase_		:					str   =			None






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							str			)->  Any:







              """simple docstring"""
              raise NotImplementedError






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







              """simple docstring"""
              if not self.is_available():
                     raise RuntimeError(
                         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."			)






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							Union[str, Any]			)->  int:







              """simple docstring"""
              return f"`pip install {cls.pip_package or cls.name}`"





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			'''optuna'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              return is_optuna_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              return run_hp_search_optuna(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Union[str, Any]			)->  Union[str, Any]:







              """simple docstring"""
              return default_hp_space_optuna(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[int]   =			'''ray'''
       UpperCamelCase_		:					Tuple   =			'''\'ray[tune]\''''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Any:







              """simple docstring"""
              return is_ray_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							int			)->  int:







              """simple docstring"""
              return run_hp_search_ray(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							str			)->  Union[str, Any]:







              """simple docstring"""
              return default_hp_space_ray(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[str, Any]   =			'''sigopt'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[str]:







              """simple docstring"""
              return is_sigopt_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Tuple			)->  Tuple:







              """simple docstring"""
              return run_hp_search_sigopt(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              return default_hp_space_sigopt(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			'''wandb'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[Any]:







              """simple docstring"""
              return is_wandb_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							List[str]			)->  str:







              """simple docstring"""
              return run_hp_search_wandb(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[Any]			)->  List[Any]:







              """simple docstring"""
              return default_hp_space_wandb(UpperCAmelCase_			)





SCREAMING_SNAKE_CASE      =     {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def 	lowerCamelCase__			(							)->							str:







       """simple docstring"""
       UpperCamelCase					 =		[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
       if len(UpperCAmelCase_				) > 0:
              UpperCamelCase					 =		available_backends[0].name
              if len(UpperCAmelCase_				) > 1:
                     logger.info(
                         F"{len(UpperCAmelCase_				)} hyperparameter search backends available. Using {name} as the default."				)
              return name
       raise RuntimeError(
           "No hyperparameter search backend available.\n"
           + "\n".join(
               F" - To install {backend.name} run {backend.pip_install()}"
               for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)





