
"""simple docstring"""




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  snake_case     (							unittest.TestCase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=13       ,       SCREAMING_SNAKE_CASE_=7       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=99       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=5       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=37       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_=16       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=4       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = parent
									SCREAMING_SNAKE_CASE_						 = batch_size
									SCREAMING_SNAKE_CASE_						 = seq_length
									SCREAMING_SNAKE_CASE_						 = is_training
									SCREAMING_SNAKE_CASE_						 = use_attention_mask
									SCREAMING_SNAKE_CASE_						 = use_token_type_ids
									SCREAMING_SNAKE_CASE_						 = use_labels
									SCREAMING_SNAKE_CASE_						 = vocab_size
									SCREAMING_SNAKE_CASE_						 = hidden_size
									SCREAMING_SNAKE_CASE_						 = num_hidden_layers
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = intermediate_size
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = max_position_embeddings
									SCREAMING_SNAKE_CASE_						 = type_vocab_size
									SCREAMING_SNAKE_CASE_						 = type_sequence_label_size
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = num_choices

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size    )

									SCREAMING_SNAKE_CASE_						 = None
									if self.use_attention_mask:
											SCREAMING_SNAKE_CASE_						 = random_attention_mask([self.batch_size, self.seq_length]    )

									SCREAMING_SNAKE_CASE_						 = None
									if self.use_token_type_ids:
											SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size, self.seq_length]       ,       self.type_vocab_size    )

									SCREAMING_SNAKE_CASE_						 = 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=SCREAMING_SNAKE_CASE_       ,       initializer_range=self.initializer_range       ,       )

									return config, input_ids, token_type_ids, attention_mask







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = config_and_inputs
									SCREAMING_SNAKE_CASE_						 = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict







@require_flax
class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      (
							    (
							        FlaxAlbertModel,
							        FlaxAlbertForPreTraining,
							        FlaxAlbertForMaskedLM,
							        FlaxAlbertForMultipleChoice,
							        FlaxAlbertForQuestionAnswering,
							        FlaxAlbertForSequenceClassification,
							        FlaxAlbertForTokenClassification,
							        FlaxAlbertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = FlaxAlbertModelTester(self    )







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class_name.from_pretrained('''albert-base-v2'''    )
											SCREAMING_SNAKE_CASE_						 = model(np.ones((1, 1)    )    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )







@require_flax
class  snake_case     (							unittest.TestCase   ):

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = FlaxAlbertModel.from_pretrained('''albert-base-v2'''    )
									SCREAMING_SNAKE_CASE_						 = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]    )
									SCREAMING_SNAKE_CASE_						 = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    )[0]
									SCREAMING_SNAKE_CASE_						 = (1, 11, 7_68)
									self.assertEqual(output.shape       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]    )

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

"""simple docstring"""




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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index