import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
    import jax.numpy as jnp

if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch



class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  {
            """task_specific_params""": {
                """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
                """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
                """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
            }
        }
        _A        =  {
            """task_specific_params.summarization.length_penalty""": 1.0,
            """task_specific_params.summarization.max_length""": 1_28,
            """task_specific_params.summarization.min_length""": 12,
            """task_specific_params.summarization.num_beams""": 4,
            """task_specific_params.summarization_cnn.length_penalty""": 2.0,
            """task_specific_params.summarization_cnn.max_length""": 1_42,
            """task_specific_params.summarization_cnn.min_length""": 56,
            """task_specific_params.summarization_cnn.num_beams""": 4,
            """task_specific_params.summarization_xsum.length_penalty""": 1.0,
            """task_specific_params.summarization_xsum.max_length""": 62,
            """task_specific_params.summarization_xsum.min_length""": 11,
            """task_specific_params.summarization_xsum.num_beams""": 6,
        }

        self.assertEqual(flatten_dict(lowerCAmelCase_							)		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  np.random.randn(3		,				4							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_							)		,				x.transpose()							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_		,				axes=(1, 2, 0)							)		,				x.transpose((1, 2, 0)							)							)							)



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  np.random.randn(3		,				4							)
        _A        =  torch.tensor(lowerCAmelCase_							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_							)		,				transpose(lowerCAmelCase_							).numpy()							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        _A        =  torch.tensor(lowerCAmelCase_							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_		,				axes=(1, 2, 0)							)		,				transpose(lowerCAmelCase_		,				axes=(1, 2, 0)							).numpy()							)							)



 @require_tf
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  np.random.randn(3		,				4							)
        _A        =  tf.constant(lowerCAmelCase_							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_							)		,				transpose(lowerCAmelCase_							).numpy()							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        _A        =  tf.constant(lowerCAmelCase_							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_		,				axes=(1, 2, 0)							)		,				transpose(lowerCAmelCase_		,				axes=(1, 2, 0)							).numpy()							)							)



 @require_flax
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  np.random.randn(3		,				4							)
        _A        =  jnp.array(lowerCAmelCase_							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_							)		,				np.asarray(transpose(lowerCAmelCase_							)							)							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        _A        =  jnp.array(lowerCAmelCase_							)
        self.assertTrue(np.allclose(transpose(lowerCAmelCase_		,				axes=(1, 2, 0)							)		,				np.asarray(transpose(lowerCAmelCase_		,				axes=(1, 2, 0)							)							)							)							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  np.random.randn(3		,				4							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(4, 3)							)		,				np.reshape(lowerCAmelCase_		,				(4, 3)							)							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(12, 5)							)		,				np.reshape(lowerCAmelCase_		,				(12, 5)							)							)							)



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  np.random.randn(3		,				4							)
        _A        =  torch.tensor(lowerCAmelCase_							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(4, 3)							)		,				reshape(lowerCAmelCase_		,				(4, 3)							).numpy()							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        _A        =  torch.tensor(lowerCAmelCase_							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(12, 5)							)		,				reshape(lowerCAmelCase_		,				(12, 5)							).numpy()							)							)



 @require_tf
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  np.random.randn(3		,				4							)
        _A        =  tf.constant(lowerCAmelCase_							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(4, 3)							)		,				reshape(lowerCAmelCase_		,				(4, 3)							).numpy()							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        _A        =  tf.constant(lowerCAmelCase_							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(12, 5)							)		,				reshape(lowerCAmelCase_		,				(12, 5)							).numpy()							)							)



 @require_flax
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  np.random.randn(3		,				4							)
        _A        =  jnp.array(lowerCAmelCase_							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(4, 3)							)		,				np.asarray(reshape(lowerCAmelCase_		,				(4, 3)							)							)							)							)

        _A        =  np.random.randn(3		,				4		,				5							)
        _A        =  jnp.array(lowerCAmelCase_							)
        self.assertTrue(np.allclose(reshape(lowerCAmelCase_		,				(12, 5)							)		,				np.asarray(reshape(lowerCAmelCase_		,				(12, 5)							)							)							)							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  np.random.randn(1		,				3		,				4							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							)		,				np.squeeze(lowerCAmelCase_							)							)							)

        _A        =  np.random.randn(1		,				4		,				1		,				5							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_		,				axis=2							)		,				np.squeeze(lowerCAmelCase_		,				axis=2							)							)							)



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  np.random.randn(1		,				3		,				4							)
        _A        =  torch.tensor(lowerCAmelCase_							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							)		,				squeeze(lowerCAmelCase_							).numpy()							)							)

        _A        =  np.random.randn(1		,				4		,				1		,				5							)
        _A        =  torch.tensor(lowerCAmelCase_							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_		,				axis=2							)		,				squeeze(lowerCAmelCase_		,				axis=2							).numpy()							)							)



 @require_tf
 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  np.random.randn(1		,				3		,				4							)
        _A        =  tf.constant(lowerCAmelCase_							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							)		,				squeeze(lowerCAmelCase_							).numpy()							)							)

        _A        =  np.random.randn(1		,				4		,				1		,				5							)
        _A        =  tf.constant(lowerCAmelCase_							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_		,				axis=2							)		,				squeeze(lowerCAmelCase_		,				axis=2							).numpy()							)							)



 @require_flax
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  np.random.randn(1		,				3		,				4							)
        _A        =  jnp.array(lowerCAmelCase_							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							)		,				np.asarray(squeeze(lowerCAmelCase_							)							)							)							)

        _A        =  np.random.randn(1		,				4		,				1		,				5							)
        _A        =  jnp.array(lowerCAmelCase_							)
        self.assertTrue(np.allclose(squeeze(lowerCAmelCase_		,				axis=2							)		,				np.asarray(squeeze(lowerCAmelCase_		,				axis=2							)							)							)							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  np.random.randn(3		,				4							)
        self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_		,				axis=1							)		,				np.expand_dims(lowerCAmelCase_		,				axis=1							)							)							)



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  np.random.randn(3		,				4							)
        _A        =  torch.tensor(lowerCAmelCase_							)
        self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_		,				axis=1							)		,				expand_dims(lowerCAmelCase_		,				axis=1							).numpy()							)							)



 @require_tf
 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  np.random.randn(3		,				4							)
        _A        =  tf.constant(lowerCAmelCase_							)
        self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_		,				axis=1							)		,				expand_dims(lowerCAmelCase_		,				axis=1							).numpy()							)							)



 @require_flax
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  np.random.randn(3		,				4							)
        _A        =  jnp.array(lowerCAmelCase_							)
        self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_		,				axis=1							)		,				np.asarray(expand_dims(lowerCAmelCase_		,				axis=1							)							)							)							)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

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



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''biogpt'''



 def __init__(					self		,				lowerCAmelCase_=4_23_84		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=24		,				lowerCAmelCase_=16		,				lowerCAmelCase_=40_96		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=1E-12		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=2		,				**lowerCAmelCase_		,				)  ->					Tuple:
        _A        =  vocab_size
        _A        =  max_position_embeddings
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  initializer_range
        _A        =  layer_norm_eps
        _A        =  scale_embedding
        _A        =  use_cache
        _A        =  layerdrop
        _A        =  activation_dropout
        super().__init__(pad_token_id=lowerCAmelCase_		,				bos_token_id=lowerCAmelCase_		,				eos_token_id=lowerCAmelCase_		,				**lowerCAmelCase_							)




