
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase											=    logging.get_logger(__name__)

_lowercase											=    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 			lowercase_		(      __lowercase       ):
       __lowerCamelCase				       =						"efficientnet"


       def __init__(    self						,       __A = 3						,       __A = 600						,       __A = 2.0						,       __A = 3.1						,       __A = 8						,       __A = [3, 3, 5, 3, 5, 5, 3]						,       __A = [32, 16, 24, 40, 80, 112, 192]						,       __A = [16, 24, 40, 80, 112, 192, 320]						,       __A = []						,       __A = [1, 2, 2, 2, 1, 2, 1]						,       __A = [1, 2, 2, 3, 3, 4, 1]						,       __A = [1, 6, 6, 6, 6, 6, 6]						,       __A = 0.25						,       __A = "swish"						,       __A = 2_560						,       __A = "mean"						,       __A = 0.02						,       __A = 0.001						,       __A = 0.99						,       __A = 0.5						,       __A = 0.2						,       **__A						,       )						->    int:
              super().__init__(**_A				)

              SCREAMING_SNAKE_CASE_							:				Any		=num_channels
              SCREAMING_SNAKE_CASE_							:				Tuple		=image_size
              SCREAMING_SNAKE_CASE_							:				Dict		=width_coefficient
              SCREAMING_SNAKE_CASE_							:				int		=depth_coefficient
              SCREAMING_SNAKE_CASE_							:				List[str]		=depth_divisor
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=kernel_sizes
              SCREAMING_SNAKE_CASE_							:				str		=in_channels
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=out_channels
              SCREAMING_SNAKE_CASE_							:				str		=depthwise_padding
              SCREAMING_SNAKE_CASE_							:				str		=strides
              SCREAMING_SNAKE_CASE_							:				Any		=num_block_repeats
              SCREAMING_SNAKE_CASE_							:				Dict		=expand_ratios
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=squeeze_expansion_ratio
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=hidden_act
              SCREAMING_SNAKE_CASE_							:				List[str]		=hidden_dim
              SCREAMING_SNAKE_CASE_							:				List[str]		=pooling_type
              SCREAMING_SNAKE_CASE_							:				Dict		=initializer_range
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_norm_eps
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=batch_norm_momentum
              SCREAMING_SNAKE_CASE_							:				List[Any]		=dropout_rate
              SCREAMING_SNAKE_CASE_							:				List[Any]		=drop_connect_rate
              SCREAMING_SNAKE_CASE_							:				Dict		=sum(_A				) * 4






class 			lowercase_		(      __lowercase       ):
       __lowerCamelCase				       =						version.parse("1.11"       )


       @property
       def 	_snake_case				(    self				)						->    str:
              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]				)





       @property
       def 	_snake_case				(    self				)						->    List[str]:
              return 1e-5

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







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size		)

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

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

												_UpperCamelCase			     =     self.get_config()

												return config, input_ids, tf.convert_to_tensor(_A		)







							def  UpperCamelCase_						(   self      :     str		):
												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  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Tuple ,			_A      :     str ,			_A      :     Optional[Any]		):
												_UpperCamelCase			     =     TFBlipTextModel(config=_A		)
												_UpperCamelCase			     =     model(_A ,			attention_mask=_A ,			training=_A		)
												_UpperCamelCase			     =     model(_A ,			training=_A		)
												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  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.prepare_config_and_inputs()
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     config_and_inputs
												_UpperCamelCase			     =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict




@require_tf
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     (TFBlipTextModel,) if is_tf_available() else ()
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     BlipTextModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A ,			hidden_size=37		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												pass







							def  UpperCamelCase_						(   self      :     Tuple		):
												pass







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







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







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







							@slow
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_UpperCamelCase			     =     TFBlipTextModel.from_pretrained(_A		)
																	self.assertIsNotNone(_A		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Optional[int]=True		):
												super().test_pt_tf_model_equivalence(allow_missing_keys=_A		)
