




"""simple docstring"""

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
		import torch

		from transformers import BlipTextModel
		from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 							lowercase   :







				def __init__(				self :       Tuple   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       int=12   ,  _lowerCamelCase :       Optional[int]=7   ,  _lowerCamelCase :       Optional[int]=True   ,  _lowerCamelCase :       str=True   ,  _lowerCamelCase :       Optional[Any]=True   ,  _lowerCamelCase :       Union[str, Any]=99   ,  _lowerCamelCase :       int=32   ,  _lowerCamelCase :       List[Any]=32   ,  _lowerCamelCase :       List[str]=5   ,  _lowerCamelCase :       str=4   ,  _lowerCamelCase :       List[str]=37   ,  _lowerCamelCase :       Union[str, Any]=0.1   ,  _lowerCamelCase :       str=0.1   ,  _lowerCamelCase :       List[Any]=5_12   ,  _lowerCamelCase :       str=0.02   ,  _lowerCamelCase :       Any=0   ,  _lowerCamelCase :       Optional[Any]=None   ,  ):



					"""simple docstring"""

					A_	:					Optional[int]						 =		parent
					A_	:					Optional[int]						 =		batch_size
					A_	:					List[str]						 =		seq_length
					A_	:					int						 =		is_training
					A_	:					Optional[Any]						 =		use_input_mask
					A_	:					Dict						 =		use_labels
					A_	:					List[str]						 =		vocab_size
					A_	:					Optional[Any]						 =		hidden_size
					A_	:					int						 =		projection_dim
					A_	:					List[Any]						 =		num_hidden_layers
					A_	:					List[str]						 =		num_attention_heads
					A_	:					Dict						 =		intermediate_size
					A_	:					Tuple						 =		dropout
					A_	:					Optional[Any]						 =		attention_dropout
					A_	:					List[str]						 =		max_position_embeddings
					A_	:					List[str]						 =		initializer_range
					A_	:					Union[str, Any]						 =		scope
					A_	:					List[Any]						 =		bos_token_id







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size							)

					A_	:					int						 =		None
					if self.use_input_mask:
						A_	:					str						 =		random_attention_mask([self.batch_size, self.seq_length]							)

					if input_mask is not None:
						A_	:					List[str]						 =		input_mask.shape
						A_	:					List[str]						 =		np.random.randint(1   ,  seq_length - 1   ,  size=(batch_size,)							)
						for batch_idx, start_index in enumerate(UpperCAmelCase_							):
							A_	:					Dict						 =		1
							A_	:					int						 =		0

					A_	:					Tuple						 =		self.get_config()

					return config, input_ids, input_mask







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					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 :       Tuple   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Dict							):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		BlipTextModel(config=UpperCAmelCase_							)
					model.to(UpperCAmelCase_							)
					model.eval()
					with torch.no_grad():
						A_	:					str						 =		model(UpperCAmelCase_   ,  attention_mask=UpperCAmelCase_							)
						A_	:					Dict						 =		model(UpperCAmelCase_							)
					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 :       Optional[int]							):



					"""simple docstring"""

					A_	:					Tuple						 =		self.prepare_config_and_inputs()
					A_	:					Any						 =		config_and_inputs
					A_	:					List[str]						 =		{'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict

@require_torch
class 							lowercase   (     a_  ,						unittest.TestCase):
				__lowerCAmelCase     :     Optional[int]								= (BlipTextModel,) if is_torch_available() else ()
				__lowerCAmelCase     :     Dict								= False
				__lowerCAmelCase     :     Tuple								= False
				__lowerCAmelCase     :     Optional[Any]								= False







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					str						 =		BlipTextModelTester(self							)
					A_	:					Optional[int]						 =		ConfigTester(self   ,  config_class=UpperCAmelCase_   ,  hidden_size=37							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					self.config_tester.run_common_tests()







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					str						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCAmelCase_							)







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					pass







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					pass







				@unittest.skip(reason='''Blip does not use inputs_embeds'''							)
				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					pass







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



					"""simple docstring"""

					pass







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



					"""simple docstring"""

					pass







				@slow
				def        a_	(				self :       Dict							):



					"""simple docstring"""

					for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A_	:					Optional[int]						 =		BlipTextModel.from_pretrained(UpperCAmelCase_							)
						self.assertIsNotNone(UpperCAmelCase_							)







				def        a_	(				self :       Union[str, Any]							):



					"""simple docstring"""

					super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCAmelCase_							)






'''simple docstring'''





from typing import Mapping

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


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Tuple			=    {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    Union[str, Any]				=			"longt5"
    A     :    List[str]				=			["past_key_values"]
    A     :    Dict				=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





    def __init__(					self     :						int							,							UpperCAmelCase_     :						Union[str, Any]=3_2_1_2_8							,							UpperCAmelCase_     :						List[Any]=5_1_2							,							UpperCAmelCase_     :						List[Any]=6_4							,							UpperCAmelCase_     :						List[str]=2_0_4_8							,							UpperCAmelCase_     :						List[str]=6							,							UpperCAmelCase_     :						Tuple=None							,							UpperCAmelCase_     :						Tuple=8							,							UpperCAmelCase_     :						str=1_2_7							,							UpperCAmelCase_     :						Dict=1_6							,							UpperCAmelCase_     :						Optional[int]=3_2							,							UpperCAmelCase_     :						Union[str, Any]=1_2_8							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						Optional[int]=1e-6							,							UpperCAmelCase_     :						Tuple=1.0							,							UpperCAmelCase_     :						str="relu"							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Dict="local"							,							UpperCAmelCase_     :						Optional[int]=True							,							UpperCAmelCase_     :						Dict=0							,							UpperCAmelCase_     :						Union[str, Any]=1							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         a					:							Optional[int]							= vocab_size
         a					:							Tuple							= d_model
         a					:							str							= d_kv
         a					:							str							= d_ff
         a					:							Union[str, Any]							= num_layers
         # default = symmetry
         a					:							Optional[Any]							= num_decoder_layers if num_decoder_layers is not None else self.num_layers
         a					:							List[Any]							= num_heads
         a					:							List[str]							= local_radius
         a					:							Optional[Any]							= global_block_size
         a					:							Optional[int]							= relative_attention_num_buckets
         a					:							List[Any]							= relative_attention_max_distance
         a					:							int							= dropout_rate
         a					:							List[str]							= layer_norm_epsilon
         a					:							Optional[Any]							= initializer_factor
         a					:							List[Any]							= feed_forward_proj
         a					:							Dict							= encoder_attention_type
         a					:							int							= use_cache

         a					:							Optional[Any]							= self.feed_forward_proj.split('-')
         a					:							List[Any]							= act_info[-1]
         a					:							str							= act_info[0] == 'gated'

         if len(UpperCAmelCase_) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                  '\'gated-gelu\' or \'relu\'')

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              a					:							Any							= 'gelu_new'

         super().__init__(
             pad_token_id=UpperCAmelCase_							,							eos_token_id=UpperCAmelCase_							,							is_encoder_decoder=UpperCAmelCase_							,							**UpperCAmelCase_							,							)






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a					:							List[Any]							= {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
              a					:							List[str]							= 'past_encoder_sequence + sequence'
              a					:							str							= {0: 'batch'}
              a					:							List[Any]							= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
              a					:							Optional[Any]							= {0: 'batch', 1: 'decoder_sequence'}
              a					:							str							= {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
              self.fill_with_past_key_values_(UpperCAmelCase_							,							direction='inputs')

         return common_inputs







    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         return 1_3

