

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
			import tensorflow as tf

			from transformers import TFBlipTextModel
			from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=1_2       ,							_a=7       ,							_a=True       ,							_a=True       ,							_a=True       ,							_a=9_9       ,							_a=3_2       ,							_a=3_2       ,							_a=2       ,							_a=4       ,							_a=3_7       ,							_a=0.1       ,							_a=0.1       ,							_a=5_1_2       ,							_a=0.02       ,							_a=0       ,							_a=None       ,							)    ->    Dict:
												_a	:    int         =		parent
												_a	:    List[str]         =		batch_size
												_a	:    str         =		seq_length
												_a	:    Union[str, Any]         =		is_training
												_a	:    Union[str, Any]         =		use_input_mask
												_a	:    str         =		use_labels
												_a	:    Union[str, Any]         =		vocab_size
												_a	:    Dict         =		hidden_size
												_a	:    Tuple         =		projection_dim
												_a	:    List[Any]         =		num_hidden_layers
												_a	:    Optional[Any]         =		num_attention_heads
												_a	:    List[str]         =		intermediate_size
												_a	:    Union[str, Any]         =		dropout
												_a	:    Any         =		attention_dropout
												_a	:    List[str]         =		max_position_embeddings
												_a	:    Union[str, Any]         =		initializer_range
												_a	:    List[str]         =		scope
												_a	:    List[str]         =		bos_token_id


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Union[str, Any]         =		ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

												_a	:    Union[str, Any]         =		None
												if self.use_input_mask:
																	_a	:    Optional[int]         =		random_attention_mask([self.batch_size, self.seq_length]       )

												if input_mask is not None:
																	_a	:    List[Any]         =		input_mask.numpy()
																	_a	:    int         =		input_mask.shape
																	_a	:    List[Any]         =		np.random.randint(1       ,							seq_length - 1       ,							size=(batch_size,)       )
																	for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE       ):
																						_a	:    Optional[Any]         =		1
																						_a	:    Optional[int]         =		0

												_a	:    List[str]         =		self.get_config()

												return config, input_ids, tf.convert_to_tensor(_SCREAMING_SNAKE_CASE       )


							def 				__lowercase				(			self       )    ->    Tuple:
												return BlipTextConfig(
												    vocab_size=self.vocab_size       ,							hidden_size=self.hidden_size       ,							projection_dim=self.projection_dim       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							dropout=self.dropout       ,							attention_dropout=self.attention_dropout       ,							max_position_embeddings=self.max_position_embeddings       ,							initializer_range=self.initializer_range       ,							bos_token_id=self.bos_token_id       ,							)


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    Union[str, Any]         =		TFBlipTextModel(config=_SCREAMING_SNAKE_CASE       )
												_a	:    str         =		model(_SCREAMING_SNAKE_CASE       ,							attention_mask=_SCREAMING_SNAKE_CASE       ,							training=_SCREAMING_SNAKE_CASE       )
												_a	:    Dict         =		model(_SCREAMING_SNAKE_CASE       ,							training=_SCREAMING_SNAKE_CASE       )
												self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)       )
												self.parent.assertEqual(result.pooler_output.shape       ,							(self.batch_size, self.hidden_size)       )




							def 				__lowercase				(			self       )    ->    int:
												_a	:    Optional[Any]         =		self.prepare_config_and_inputs()
												_a	:    Optional[Any]         =		config_and_inputs
												_a	:    Dict         =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict



@require_tf
class 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    (TFBlipTextModel,) if is_tf_available() else ()
							UpperCAmelCase__       :						Any	    =    False
							UpperCAmelCase__       :						Tuple	    =    False
							UpperCAmelCase__       :						Tuple	    =    False


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Optional[Any]         =		BlipTextModelTester(self       )
												_a	:    Dict         =		ConfigTester(self       ,							config_class=_SCREAMING_SNAKE_CASE       ,							hidden_size=3_7       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												self.config_tester.run_common_tests()


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    str         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE       )


							def 				__lowercase				(			self       )    ->    Any:
												pass


							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							@unittest.skip(reason='''Blip does not use inputs_embeds'''       )
							def 				__lowercase				(			self       )    ->    Optional[int]:
												pass


							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''       )
							def 				__lowercase				(			self       )    ->    int:
												pass


							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''       )
							def 				__lowercase				(			self       )    ->    str:
												pass


							@slow
							def 				__lowercase				(			self       )    ->    int:





												for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a	:    Tuple         =		TFBlipTextModel.from_pretrained(_SCREAMING_SNAKE_CASE       )
																	self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )




							def 				__lowercase				(			self       ,							_a=True       )    ->    Tuple:
												super().test_pt_tf_model_equivalence(allow_missing_keys=_SCREAMING_SNAKE_CASE       )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




