import unittest

import numpy as np

from transformers import BertConfig, 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():
					from transformers.models.bert.modeling_flax_bert import (
					    FlaxBertForMaskedLM,
					    FlaxBertForMultipleChoice,
					    FlaxBertForNextSentencePrediction,
					    FlaxBertForPreTraining,
					    FlaxBertForQuestionAnswering,
					    FlaxBertForSequenceClassification,
					    FlaxBertForTokenClassification,
					    FlaxBertModel,
					)





class 					lowercase__						(   unittest.TestCase):


						def __init__(       self	:				int    ,		UpperCamelCase__	:				List[str]    ,		UpperCamelCase__	:				Any=13    ,		UpperCamelCase__	:				Optional[Any]=7    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				List[Any]=True    ,		UpperCamelCase__	:				Tuple=True    ,		UpperCamelCase__	:				str=True    ,		UpperCamelCase__	:				Optional[Any]=99    ,		UpperCamelCase__	:				Dict=32    ,		UpperCamelCase__	:				str=5    ,		UpperCamelCase__	:				List[Any]=4    ,		UpperCamelCase__	:				List[str]=37    ,		UpperCamelCase__	:				Optional[int]="gelu"    ,		UpperCamelCase__	:				List[Any]=0.1    ,		UpperCamelCase__	:				Dict=0.1    ,		UpperCamelCase__	:				List[str]=512    ,		UpperCamelCase__	:				str=16    ,		UpperCamelCase__	:				Optional[int]=2    ,		UpperCamelCase__	:				Dict=0.02    ,		UpperCamelCase__	:				Dict=4    ,		):





												'''simple docstring'''





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


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							None
												if self.use_attention_mask:
																		SCREAMING_SNAKE_CASE							:    Dict							=							random_attention_mask([self.batch_size, self.seq_length]						)

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

												SCREAMING_SNAKE_CASE							:    List[str]							=							BertConfig(
												    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=lowerCAmelCase_    ,		initializer_range=self.initializer_range    ,		)

												return config, input_ids, token_type_ids, attention_mask


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							config_and_inputs
												SCREAMING_SNAKE_CASE							:    Any							=							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    str							=							config_and_inputs

												SCREAMING_SNAKE_CASE							:    Tuple							=							True
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							ids_tensor([self.batch_size, self.seq_length]    ,		vocab_size=2						)

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






@require_flax
class 					lowercase__						(   _lowerCamelCase	,							unittest.TestCase):
						UpperCamelCase_     =		True

						UpperCamelCase_     =		(
						    (
						        FlaxBertModel,
						        FlaxBertForPreTraining,
						        FlaxBertForMaskedLM,
						        FlaxBertForMultipleChoice,
						        FlaxBertForQuestionAnswering,
						        FlaxBertForNextSentencePrediction,
						        FlaxBertForSequenceClassification,
						        FlaxBertForTokenClassification,
						        FlaxBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							FlaxBertModelTester(self						)



						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							FlaxBertModel.from_pretrained('''bert-base-cased'''						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							model(np.ones((1, 1)						)						)
												self.assertIsNotNone(lowerCAmelCase_						)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :							List[str]      =					{
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[int]      =					["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					lowercase    :							List[str]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

