





"""simple docstring"""




import copy
import os
from typing import Union

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


a						: Tuple     	=			logging.get_logger(__name__)

a						: Tuple     	=			{
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Any      					="""git_vision_model"""






							def __init__(   self      ,					lowerCAmelCase__=768      ,					lowerCAmelCase__=3072      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=3      ,					lowerCAmelCase__=224      ,					lowerCAmelCase__=16      ,					lowerCAmelCase__="quick_gelu"      ,					lowerCAmelCase__=1E-5      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=0.02      ,					**lowerCAmelCase__      ,					)	-> Any:
								super().__init__(**lowerCAmelCase__      )

								a      :		str	=				hidden_size
								a      :		Optional[int]	=				intermediate_size
								a      :		List[str]	=				num_hidden_layers
								a      :		Any	=				num_attention_heads
								a      :		List[str]	=				num_channels
								a      :		int	=				patch_size
								a      :		Tuple	=				image_size
								a      :		Dict	=				initializer_range
								a      :		Optional[int]	=				attention_dropout
								a      :		Dict	=				layer_norm_eps
								a      :		Optional[int]	=				hidden_act






							@classmethod
							def     __a   (   cls      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> "PretrainedConfig":
								cls._set_token_in_kwargs(lowerCAmelCase__      )

								a,       a      :		Any	=				cls.get_config_dict(lowerCAmelCase__      ,					**lowerCAmelCase__      )

								# get the vision config dict if we are loading from GITConfig
								if config_dict.get("model_type"      ) == "git":
									a      :		str	=				config_dict["vision_config"]

								if "model_type" in config_dict and hasattr(cls      ,					"model_type"      ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

								return cls.from_dict(lowerCAmelCase__      ,					**lowerCAmelCase__      )


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					int      					="""git"""






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=3_0522      ,					lowerCAmelCase__=768      ,					lowerCAmelCase__=6      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=3072      ,					lowerCAmelCase__="gelu"      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=1024      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=1E-12      ,					lowerCAmelCase__=0      ,					lowerCAmelCase__="absolute"      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=False      ,					lowerCAmelCase__=101      ,					lowerCAmelCase__=102      ,					lowerCAmelCase__=None      ,					**lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__(bos_token_id=lowerCAmelCase__      ,					eos_token_id=lowerCAmelCase__      ,					pad_token_id=lowerCAmelCase__      ,					**lowerCAmelCase__      )

								if vision_config is None:
									a      :		List[Any]	=				{}
									logger.info("vision_config is None. initializing the GitVisionConfig with default values."      )

								a      :		List[str]	=				GitVisionConfig(**lowerCAmelCase__      )
								a      :		int	=				vocab_size
								a      :		Optional[Any]	=				hidden_size
								a      :		Tuple	=				num_hidden_layers
								a      :		Union[str, Any]	=				num_attention_heads
								a      :		Union[str, Any]	=				hidden_act
								a      :		List[Any]	=				intermediate_size
								a      :		Tuple	=				hidden_dropout_prob
								a      :		List[Any]	=				attention_probs_dropout_prob
								a      :		Union[str, Any]	=				max_position_embeddings
								a      :		Tuple	=				initializer_range
								a      :		int	=				layer_norm_eps
								a      :		List[str]	=				position_embedding_type
								a      :		int	=				use_cache
								a      :		List[Any]	=				tie_word_embeddings
								a      :		str	=				num_image_with_embedding

								a      :		List[Any]	=				bos_token_id
								a      :		int	=				eos_token_id






							def     __a   (   self      )	-> int:
								a      :		Union[str, Any]	=				copy.deepcopy(self.__dict__      )
								a      :		Optional[int]	=				self.vision_config.to_dict()
								a      :		List[str]	=				self.__class__.model_type
								return output




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				)





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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

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

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

				__magic_name__		:		List[str]	   =     RobertaPreLayerNormConfig(
				    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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[Any]	   =     config_and_inputs
				__magic_name__		:		List[str]	   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     config_and_inputs

				__magic_name__		:		Tuple	   =     True
				__magic_name__		:		int	   =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				__magic_name__		:		Optional[Any]	   =     ids_tensor([self.batch_size, self.seq_length]						,   vocab_size=2		)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    True

			UpperCamelCase__          =    (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     FlaxRobertaPreLayerNormModelTester(self		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				for model_class_name in self.all_model_classes:
					__magic_name__		:		Optional[Any]	   =     model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)
					__magic_name__		:		Dict	   =     model(np.ones((1, 1)		)		)
					self.assertIsNotNone(_a		)







@require_flax
class       _snake_case					(     unittest.TestCase				):







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Union[str, Any]	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		List[str]	   =     model(_a		)[0]
				__magic_name__		:		str	   =     [1, 11, 50_265]
				self.assertEqual(list(output.shape		)						,   _a		)
				# compare the actual values for a slice.
				__magic_name__		:		List[str]	   =     np.array(
				    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Tuple	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		Tuple	   =     model(_a		)[0]
				# compare the actual values for a slice.
				__magic_name__		:		Dict	   =     np.array(
				    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)
