






'''simple docstring'''
import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  a_					=	"0.12"  # assumed parallelism: 8


if is_torch_available():
  import torch





def 	_a(					UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  int=None    ):




      '''simple docstring'''






      if rng is None:
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=random.Random()

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=1
      for dim in shape:
            total_dims *= dim

      SCREAMING_SNAKE_CASE__       :			List[str]													=[]
      for _ in range(__lowerCAmelCase    ):
            values.append(rng.randint(0,		vocab_size - 1    )    )

      SCREAMING_SNAKE_CASE__       :			Optional[int]													=np.array(__lowerCAmelCase,		dtype=jnp.intaa    ).reshape(__lowerCAmelCase    )

      return output





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  List[Any]=None    ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=ids_tensor(__lowerCAmelCase,		vocab_size=2,		rng=__lowerCAmelCase    )
      # make sure that at least one token is attended to for each batch
      SCREAMING_SNAKE_CASE__       :			str													=1
      return attn_mask






@require_flax
class        __SCREAMING_SNAKE_CASE :
 snake_case_												=							None
 snake_case_												=							()




 def        __magic_name__		( self		:							List[str] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=2
       SCREAMING_SNAKE_CASE__       :			List[str]													=inputs["""input_ids"""].shape[-1] // 2
       SCREAMING_SNAKE_CASE__       :			Dict													=inputs["""input_ids"""][:max_batch_size, :sequence_length]

       SCREAMING_SNAKE_CASE__       :			Dict													=jnp.ones_like(_a )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       SCREAMING_SNAKE_CASE__       :			str													=input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
             # hack to allow generate for models such as GPT2 as is done in `generate()`
             SCREAMING_SNAKE_CASE__       :			Any													=config.eos_token_id
       return config, input_ids, attention_mask, max_length




 @is_pt_flax_cross_test
 def        __magic_name__		( self		:							int )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			Any													=False
       SCREAMING_SNAKE_CASE__       :			Dict													=max_length
       SCREAMING_SNAKE_CASE__       :			int													=0

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_class.__name__[4:]  # Skip the "Flax" at the beginning
             SCREAMING_SNAKE_CASE__       :			List[Any]													=getattr(_a   , _a )
             SCREAMING_SNAKE_CASE__       :			Dict													=pt_model_class(_a ).eval()
             SCREAMING_SNAKE_CASE__       :			Tuple													=load_flax_weights_in_pytorch_model(_a   , flax_model.params )

             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model.generate(_a ).sequences
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=pt_model.generate(torch.tensor(_a   , dtype=torch.long ) )

             if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                   SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

             self.assertListEqual(pt_generation_outputs.numpy().tolist()   , flax_generation_outputs.tolist() )




 def        __magic_name__		( self		:							str )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			int													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			Any													=False
       SCREAMING_SNAKE_CASE__       :			Dict													=max_length

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			Tuple													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model.generate(_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			int													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			str													=jit_generate(_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							str )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=True
       SCREAMING_SNAKE_CASE__       :			str													=max_length

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			str													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Optional[int]													=model.generate(_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			Dict													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			int													=jit_generate(_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							Any )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			List[str]													=False
       SCREAMING_SNAKE_CASE__       :			Any													=max_length
       SCREAMING_SNAKE_CASE__       :			Dict													=2

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Optional[int]													=model.generate(_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			List[Any]													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=jit_generate(_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							Optional[Any] )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			str													=False
       SCREAMING_SNAKE_CASE__       :			str													=max_length
       SCREAMING_SNAKE_CASE__       :			Tuple													=2
       SCREAMING_SNAKE_CASE__       :			Tuple													=2

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			Dict													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Tuple													=model.generate(_a ).sequences
             self.assertEqual(generation_outputs.shape[0]   , input_ids.shape[0] * config.num_return_sequences )




 def        __magic_name__		( self		:							List[str] )		->  int:
       SCREAMING_SNAKE_CASE__       :			int													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			str													=True
       SCREAMING_SNAKE_CASE__       :			Any													=max_length
       SCREAMING_SNAKE_CASE__       :			List[str]													=0.8
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=10
       SCREAMING_SNAKE_CASE__       :			str													=0.3
       SCREAMING_SNAKE_CASE__       :			str													=1
       SCREAMING_SNAKE_CASE__       :			int													=8
       SCREAMING_SNAKE_CASE__       :			int													=9

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			int													=model.generate(_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			Dict													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			List[str]													=jit_generate(_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							Optional[int] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			Any													=max_length
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=1
       SCREAMING_SNAKE_CASE__       :			Dict													=8
       SCREAMING_SNAKE_CASE__       :			int													=9

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Optional[int]													=model.generate(_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			Any													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			Any													=jit_generate(_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							Dict )		->  str:
       SCREAMING_SNAKE_CASE__       :			List[str]													=self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE__       :			Tuple													=max_length
       SCREAMING_SNAKE_CASE__       :			List[str]													=2
       SCREAMING_SNAKE_CASE__       :			Tuple													=1
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=8
       SCREAMING_SNAKE_CASE__       :			str													=9

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			List[str]													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			List[str]													=model.generate(_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			Dict													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=jit_generate(_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							List[str] )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Dict													=self._get_input_ids_and_config()

       # pad attention mask on the left
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=attention_mask.at[(0, 0)].set(0 )

       SCREAMING_SNAKE_CASE__       :			List[str]													=False
       SCREAMING_SNAKE_CASE__       :			int													=max_length

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Tuple													=model.generate(_a   , attention_mask=_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			Optional[int]													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			str													=jit_generate(_a   , attention_mask=_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							int )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Any													=self._get_input_ids_and_config()

       # pad attention mask on the left
       SCREAMING_SNAKE_CASE__       :			Dict													=attention_mask.at[(0, 0)].set(0 )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=True
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=max_length

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			Any													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=model.generate(_a   , attention_mask=_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=jit_generate(_a   , attention_mask=_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )




 def        __magic_name__		( self		:							str )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self._get_input_ids_and_config()

       # pad attention mask on the left
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=attention_mask.at[(0, 0)].set(0 )

       SCREAMING_SNAKE_CASE__       :			Any													=2
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_length

       for model_class in self.all_generative_model_classes:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_class(_a )

             SCREAMING_SNAKE_CASE__       :			str													=model.generate(_a   , attention_mask=_a ).sequences
             self.assertEqual(generation_outputs.shape[-1]   , _a )

             SCREAMING_SNAKE_CASE__       :			Tuple													=jit(model.generate )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=jit_generate(_a   , attention_mask=_a ).sequences

             self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist() )






@require_flax
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							int )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			int													=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													="""Hello world"""
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tokenizer(_a   , return_tensors='''np''' ).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(_a   , '''do_samples''' ):
             model.generate(_a   , do_samples=_a )

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(_a   , '''foo''' ):
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={"""foo""": """bar"""}
             model.generate(_a   , **_a )







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					=	logging.get_logger(__name__)

a_					=	{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""biogpt"""




 def __init__( self		:							str   , __lowercase		:							Union[str, Any]=4_23_84   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							Any=24   , __lowercase		:							Any=16   , __lowercase		:							Optional[Any]=40_96   , __lowercase		:							Any="gelu"   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							List[Any]=0.1   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							List[Any]=0.02   , __lowercase		:							Tuple=1e-12   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Any=0.0   , __lowercase		:							int=0.0   , __lowercase		:							str=1   , __lowercase		:							int=0   , __lowercase		:							Optional[int]=2   , **__lowercase		:							Dict   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			str													=hidden_size
       SCREAMING_SNAKE_CASE__       :			int													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Tuple													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Any													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			int													=hidden_act
       SCREAMING_SNAKE_CASE__       :			List[Any]													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Dict													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=layer_norm_eps
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=scale_embedding
       SCREAMING_SNAKE_CASE__       :			str													=use_cache
       SCREAMING_SNAKE_CASE__       :			str													=layerdrop
       SCREAMING_SNAKE_CASE__       :			Dict													=activation_dropout
       super().__init__(pad_token_id=__lowercase   , bos_token_id=__lowercase   , eos_token_id=__lowercase   , **__lowercase )