



"""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 				lowercase__       :







       '''simple docstring'''


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


             '''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 					lowercase__    (							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 					lowercase__    (							self      :  Dict   )			->						Any:


             '''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 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  Optional[int]		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  Any   )			->						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 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  Optional[int]		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Union[str, Any]   )			->						Union[str, 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 				lowercase__       (   SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       UpperCamelCase						=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()



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


             '''simple docstring'''




             UpperCAmelCase_      			=							FlaxGPTJModelTester(self   )



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


             '''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 					lowercase__    (							self      :  Union[str, Any]   )			->						Union[str, 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 					lowercase__    (							self      :  Union[str, Any]   )			->						Tuple:


             '''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 					lowercase__    (							self      :  Dict   )			->						List[str]:


             '''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 					lowercase__    (							self      :  List[str]   )			->						List[str]:


             '''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 					lowercase__    (							self      :  Union[str, Any]   )			->						Dict:


             '''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 .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




