




import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
							    FlaxRoFormerForMaskedLM,
							    FlaxRoFormerForMultipleChoice,
							    FlaxRoFormerForQuestionAnswering,
							    FlaxRoFormerForSequenceClassification,
							    FlaxRoFormerForTokenClassification,
							    FlaxRoFormerModel,
							)




class 		__A   (      unittest.TestCase      ):

						def __init__(		self :str     ,			__snake_case :str     ,			__snake_case :Tuple=13     ,			__snake_case :List[str]=7     ,			__snake_case :List[str]=True     ,			__snake_case :Dict=True     ,			__snake_case :str=True     ,			__snake_case :Optional[int]=True     ,			__snake_case :Union[str, Any]=99     ,			__snake_case :List[str]=32     ,			__snake_case :Tuple=5     ,			__snake_case :Optional[int]=4     ,			__snake_case :Any=37     ,			__snake_case :Any="gelu"     ,			__snake_case :Dict=0.1     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=5_12     ,			__snake_case :int=16     ,			__snake_case :List[Any]=2     ,			__snake_case :str=0.02     ,			__snake_case :Dict=4     ,			):
										'''simple docstring'''

										__magic_name__						:						int										=parent
										__magic_name__						:						Dict										=batch_size
										__magic_name__						:						List[str]										=seq_length
										__magic_name__						:						Optional[int]										=is_training
										__magic_name__						:						Any										=use_attention_mask
										__magic_name__						:						List[str]										=use_token_type_ids
										__magic_name__						:						Any										=use_labels
										__magic_name__						:						List[Any]										=vocab_size
										__magic_name__						:						Optional[Any]										=hidden_size
										__magic_name__						:						Tuple										=num_hidden_layers
										__magic_name__						:						List[str]										=num_attention_heads
										__magic_name__						:						int										=intermediate_size
										__magic_name__						:						Optional[int]										=hidden_act
										__magic_name__						:						str										=hidden_dropout_prob
										__magic_name__						:						int										=attention_probs_dropout_prob
										__magic_name__						:						str										=max_position_embeddings
										__magic_name__						:						List[str]										=type_vocab_size
										__magic_name__						:						Tuple										=type_sequence_label_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Any										=num_choices

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						str										=ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size			)

										__magic_name__						:						Optional[int]										=None
										if self.use_attention_mask:
														__magic_name__						:						str										=random_attention_mask([self.batch_size, self.seq_length]			)

										__magic_name__						:						Union[str, Any]										=None
										if self.use_token_type_ids:
														__magic_name__						:						List[str]										=ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size			)

										__magic_name__						:						Any										=RoFormerConfig(
										    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=__snake_case     ,			initializer_range=self.initializer_range     ,			)

										return config, input_ids, token_type_ids, attention_mask


						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.prepare_config_and_inputs()
										__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Tuple										=config_and_inputs
										__magic_name__						:						Union[str, Any]										={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
										return config, inputs_dict




@require_flax
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       True

						UpperCamelCase      =       (
						    (
						        FlaxRoFormerModel,
						        FlaxRoFormerForMaskedLM,
						        FlaxRoFormerForSequenceClassification,
						        FlaxRoFormerForTokenClassification,
						        FlaxRoFormerForMultipleChoice,
						        FlaxRoFormerForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=FlaxRoFormerModelTester(self			)


						@slow
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										for model_class_name in self.all_model_classes:
														__magic_name__						:						Optional[Any]										=model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""     ,			from_pt=__snake_case			)
														__magic_name__						:						List[str]										=model(np.ones((1, 1)			)			)
														self.assertIsNotNone(__snake_case			)




@require_flax
class 		__A   (      unittest.TestCase      ):

						@slow
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""			)
										__magic_name__						:						Tuple										=jnp.array([[0, 1, 2, 3, 4, 5]]			)
										__magic_name__						:						str										=model(__snake_case			)[0]

										__magic_name__						:						int										=5_00_00

										__magic_name__						:						Dict										=(1, 6, vocab_size)
										self.assertEqual(output.shape     ,			__snake_case			)

										__magic_name__						:						List[Any]										=jnp.array(
										    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]			)

										self.assertTrue(jnp.allclose(output[:, :3, :3]     ,			__snake_case     ,			atol=1E-4			)			)










import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """segformer"""

						def __init__(		self :List[str]     ,			__snake_case :str=3     ,			__snake_case :Optional[Any]=4     ,			__snake_case :List[Any]=[2, 2, 2, 2]     ,			__snake_case :Dict=[8, 4, 2, 1]     ,			__snake_case :Optional[int]=[32, 64, 1_60, 2_56]     ,			__snake_case :Union[str, Any]=[7, 3, 3, 3]     ,			__snake_case :Optional[Any]=[4, 2, 2, 2]     ,			__snake_case :Tuple=[1, 2, 5, 8]     ,			__snake_case :List[Any]=[4, 4, 4, 4]     ,			__snake_case :Optional[Any]="gelu"     ,			__snake_case :Tuple=0.0     ,			__snake_case :Dict=0.0     ,			__snake_case :Optional[int]=0.1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Tuple=0.1     ,			__snake_case :Union[str, Any]=1E-6     ,			__snake_case :int=2_56     ,			__snake_case :Optional[int]=2_55     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
														warnings.warn(
														    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
														    """ removed, as the behaviour will default to that of reshape_last_stage = True."""     ,			__snake_case     ,			)

										__magic_name__						:						Dict										=num_channels
										__magic_name__						:						str										=num_encoder_blocks
										__magic_name__						:						List[Any]										=depths
										__magic_name__						:						Optional[Any]										=sr_ratios
										__magic_name__						:						List[str]										=hidden_sizes
										__magic_name__						:						List[str]										=patch_sizes
										__magic_name__						:						Any										=strides
										__magic_name__						:						Optional[Any]										=mlp_ratios
										__magic_name__						:						str										=num_attention_heads
										__magic_name__						:						int										=hidden_act
										__magic_name__						:						List[Any]										=hidden_dropout_prob
										__magic_name__						:						Optional[Any]										=attention_probs_dropout_prob
										__magic_name__						:						Optional[Any]										=classifier_dropout_prob
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						List[str]										=drop_path_rate
										__magic_name__						:						List[Any]										=layer_norm_eps
										__magic_name__						:						List[str]										=decoder_hidden_size
										__magic_name__						:						Union[str, Any]										=kwargs.get("""reshape_last_stage"""     ,			__snake_case			)
										__magic_name__						:						Dict										=semantic_loss_ignore_index




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       version.parse("""1.11"""      )

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]			)

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return 1E-4


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										return 12




