






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  lowerCAmelCase_       :




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   str=12 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=7 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=True ,							SCREAMING_SNAKE_CASE_     :   List[str]=True ,							SCREAMING_SNAKE_CASE_     :   str=True ,							SCREAMING_SNAKE_CASE_     :   str=99 ,							SCREAMING_SNAKE_CASE_     :   Any=32 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=32 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=2 ,							SCREAMING_SNAKE_CASE_     :   List[str]=4 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=37 ,							SCREAMING_SNAKE_CASE_     :   Any=0.1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=512 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=0.02 ,							SCREAMING_SNAKE_CASE_     :   Dict=0 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=None ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						seq_length
											lowerCAmelCase__      			=						is_training
											lowerCAmelCase__      			=						use_input_mask
											lowerCAmelCase__      			=						use_labels
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						projection_dim
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						dropout
											lowerCAmelCase__      			=						attention_dropout
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						scope
											lowerCAmelCase__      			=						bos_token_id




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size	)

											lowerCAmelCase__      			=						None
											if self.use_input_mask:
															lowerCAmelCase__      			=						random_attention_mask([self.batch_size, self.seq_length]	)

											if input_mask is not None:
															lowerCAmelCase__      			=						input_mask.numpy()
															lowerCAmelCase__			,		lowerCAmelCase__      			=						input_mask.shape
															lowerCAmelCase__      			=						np.random.randint(1 ,							seq_length - 1 ,							size=(batch_size,)	)
															for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_	):
																			lowerCAmelCase__      			=						1
																			lowerCAmelCase__      			=						0

											lowerCAmelCase__      			=						self.get_config()

											return config, input_ids, tf.convert_to_tensor(SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int]	):
											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 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]	):
											lowerCAmelCase__      			=						TFBlipTextModel(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							training=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						self.prepare_config_and_inputs()
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						config_and_inputs
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict



@require_tf
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Optional[int]		      =    (TFBlipTextModel,) if is_tf_available() else ()
							UpperCamelCase_		:Dict		      =    False
							UpperCamelCase_		:Optional[int]		      =    False
							UpperCamelCase_		:List[str]		      =    False




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						BlipTextModelTester(self	)
											lowerCAmelCase__      			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_ ,							hidden_size=37	)




							def 						__snake_case					(   self     :   Union[str, Any]	):
											self.config_tester.run_common_tests()




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											pass




							def 						__snake_case					(   self     :   Tuple	):
											pass




							@unittest.skip(reason='''Blip does not use inputs_embeds'''	)
							def 						__snake_case					(   self     :   Union[str, Any]	):
											pass




							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''	)
							def 						__snake_case					(   self     :   List[str]	):
											pass




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




							@slow
							def 						__snake_case					(   self     :   Dict	):
											for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase__      			=						TFBlipTextModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
															self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   int=True	):
											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			:		str   				=    {
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    ["BlenderbotSmallTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "BlenderbotSmallForCausalLM",
			    "BlenderbotSmallForConditionalGeneration",
			    "BlenderbotSmallModel",
			    "BlenderbotSmallPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "TFBlenderbotSmallForConditionalGeneration",
			    "TFBlenderbotSmallModel",
			    "TFBlenderbotSmallPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "FlaxBlenderbotSmallForConditionalGeneration",
			    "FlaxBlenderbotSmallModel",
			    "FlaxBlenderbotSmallPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		int   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





