

import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )


class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=True						,       a=True						,       a=True						,       a=99						,       a=32						,       a=5						,       a=4						,       a=37						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=16						,       a=2						,       a=0.02						,       a=4						,       ):
         lowercase__   :       Union[str, Any]				=	parent
         lowercase__   :       List[Any]				=	batch_size
         lowercase__   :       str				=	seq_length
         lowercase__   :       List[Any]				=	is_training
         lowercase__   :       Union[str, Any]				=	use_attention_mask
         lowercase__   :       str				=	use_token_type_ids
         lowercase__   :       str				=	use_labels
         lowercase__   :       Union[str, Any]				=	vocab_size
         lowercase__   :       List[str]				=	hidden_size
         lowercase__   :       Optional[Any]				=	num_hidden_layers
         lowercase__   :       Dict				=	num_attention_heads
         lowercase__   :       int				=	intermediate_size
         lowercase__   :       Union[str, Any]				=	hidden_act
         lowercase__   :       Tuple				=	hidden_dropout_prob
         lowercase__   :       Dict				=	attention_probs_dropout_prob
         lowercase__   :       List[Any]				=	max_position_embeddings
         lowercase__   :       Any				=	type_vocab_size
         lowercase__   :       Union[str, Any]				=	type_sequence_label_size
         lowercase__   :       Union[str, Any]				=	initializer_range
         lowercase__   :       int				=	num_choices





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       List[Any]				=	None
         if self.use_attention_mask:
              lowercase__   :       int				=	random_attention_mask([self.batch_size, self.seq_length])

         lowercase__   :       List[Any]				=	None
         if self.use_token_type_ids:
              lowercase__   :       int				=	ids_tensor([self.batch_size, self.seq_length]						,       self.type_vocab_size)

         lowercase__   :       Optional[int]				=	AlbertConfig(
             vocab_size=self.vocab_size						,       hidden_size=self.hidden_size						,       num_hidden_layers=self.num_hidden_layers						,       num_attention_heads=self.num_attention_heads						,       intermediate_size=self.intermediate_size						,       hidden_act=self.hidden_act						,       hidden_dropout_prob=self.hidden_dropout_prob						,       attention_probs_dropout_prob=self.attention_probs_dropout_prob						,       max_position_embeddings=self.max_position_embeddings						,       type_vocab_size=self.type_vocab_size						,       is_decoder=a						,       initializer_range=self.initializer_range						,       )

         return config, input_ids, token_type_ids, attention_mask





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.prepare_config_and_inputs()
         lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       Dict				=	config_and_inputs
         lowercase__   :       Union[str, Any]				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict



@require_flax
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Union[str, Any]														=					(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	FlaxAlbertModelTester(self)





    @slow
    def 				snake_case_    (				self):
         for model_class_name in self.all_model_classes:
              lowercase__   :       str				=	model_class_name.from_pretrained('albert-base-v2')
              lowercase__   :       Any				=	model(np.ones((1, 1)))
              self.assertIsNotNone(a)



@require_flax
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	FlaxAlbertModel.from_pretrained('albert-base-v2')
         lowercase__   :       List[Any]				=	np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
         lowercase__   :       str				=	np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
         lowercase__   :       Optional[int]				=	model(a						,       attention_mask=a)[0]
         lowercase__   :       Any				=	(1, 11, 768)
         self.assertEqual(output.shape						,       a)
         lowercase__   :       Tuple				=	np.array(
             [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]])

         self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,       a						,       atol=1e-4))




import os

try:
       from .build_directory_md import good_file_paths
except ImportError:
       from build_directory_md import good_file_paths  # type: ignore

snake_case_        =			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

snake_case_        =			[file for file in filepaths if file != file.lower()]
if upper_files:
       print(F'''{len(upper_files)} files contain uppercase characters:''')
       print('''\n'''.join(upper_files) + '''\n''')

snake_case_        =			[file for file in filepaths if ''' ''' in file]
if space_files:
       print(F'''{len(space_files)} files contain space characters:''')
       print('''\n'''.join(space_files) + '''\n''')

snake_case_        =			[file for file in filepaths if '''-''' in file]
if hyphen_files:
       print(F'''{len(hyphen_files)} files contain hyphen characters:''')
       print('''\n'''.join(hyphen_files) + '''\n''')

snake_case_        =			[file for file in filepaths if os.sep not in file]
if nodir_files:
       print(F'''{len(nodir_files)} files are not in a directory:''')
       print('''\n'''.join(nodir_files) + '''\n''')

snake_case_        =			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
       import sys

       sys.exit(bad_files)

