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				,      lowerCAmelCase__				,      lowerCAmelCase__=12				,      lowerCAmelCase__=7				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__=99				,      lowerCAmelCase__=32				,      lowerCAmelCase__=32				,      lowerCAmelCase__=2				,      lowerCAmelCase__=4				,      lowerCAmelCase__=37				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=512				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=0				,      lowerCAmelCase__=None				,      ):
            _A=	parent
            _A=	batch_size
            _A=	seq_length
            _A=	is_training
            _A=	use_input_mask
            _A=	use_labels
            _A=	vocab_size
            _A=	hidden_size
            _A=	projection_dim
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	intermediate_size
            _A=	dropout
            _A=	attention_dropout
            _A=	max_position_embeddings
            _A=	initializer_range
            _A=	scope
            _A=	bos_token_id



     def 			a__  (					self       ):
            _A=	ids_tensor([self.batch_size, self.seq_length]				,      self.vocab_size       )

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

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

            _A=	self.get_config()

            return config, input_ids, tf.convert_to_tensor(lowerCAmelCase__       )



     def 			a__  (					self       ):
            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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	TFBlipTextModel(config=lowerCAmelCase__       )
            _A=	model(lowerCAmelCase__				,      attention_mask=lowerCAmelCase__				,      training=lowerCAmelCase__       )
            _A=	model(lowerCAmelCase__				,      training=lowerCAmelCase__       )
            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 			a__  (					self       ):
            _A=	self.prepare_config_and_inputs()
            _A,			_A,			_A=	config_and_inputs
            _A=	{'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict





@require_tf
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=(TFBlipTextModel,) if is_tf_available() else ()
     _SCREAMING_SNAKE_CASE     :     Tuple		=False
     _SCREAMING_SNAKE_CASE     :     Tuple		=False
     _SCREAMING_SNAKE_CASE     :     Tuple		=False



     def 			a__  (					self       ):
            _A=	BlipTextModelTester(self       )
            _A=	ConfigTester(self				,      config_class=lowerCAmelCase__				,      hidden_size=37       )



     def 			a__  (					self       ):
            self.config_tester.run_common_tests()



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            pass



     @unittest.skip(reason='Blip does not use inputs_embeds'       )
     def 			a__  (					self       ):
            pass



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



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



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





     def 			a__  (					self				,      lowerCAmelCase__=True       ):
            super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__       )






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            warnings.warn(
                'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                ' Please use PerceiverImageProcessor instead.'				,      lowerCAmelCase__				,      )
            super().__init__(*lowerCAmelCase__				,      **lowerCAmelCase__       )