'''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   UpperCamelCase					:







    """simple docstring"""





    def __init__(					self     :						Tuple							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						int=1_2							,							UpperCAmelCase_     :						Optional[int]=7							,							UpperCAmelCase_     :						Optional[int]=True							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Optional[Any]=True							,							UpperCAmelCase_     :						Union[str, Any]=9_9							,							UpperCAmelCase_     :						int=3_2							,							UpperCAmelCase_     :						List[Any]=3_2							,							UpperCAmelCase_     :						List[str]=5							,							UpperCAmelCase_     :						str=4							,							UpperCAmelCase_     :						List[str]=3_7							,							UpperCAmelCase_     :						Union[str, Any]=0.1							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						List[Any]=5_1_2							,							UpperCAmelCase_     :						str=0.02							,							UpperCAmelCase_     :						Any=0							,							UpperCAmelCase_     :						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    SCREAMING_SNAKE_CASE_     (					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							,			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    SCREAMING_SNAKE_CASE_     (					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    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						List[Any]							,							UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						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    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







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






@require_torch
class   UpperCamelCase					(       a_    ,     unittest.TestCase       ):







    """simple docstring"""





    A     :    Optional[int]				=			(BlipTextModel,) if is_torch_available() else ()
    A     :    Dict				=			False
    A     :    Tuple				=			False
    A     :    Optional[Any]				=			False





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							str							= BlipTextModelTester(self)
         a					:							Optional[int]							= ConfigTester(self							,							config_class=UpperCAmelCase_							,							hidden_size=3_7)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         self.config_tester.run_common_tests()





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         pass





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         pass





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


         """simple docstring"""







         pass





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


         """simple docstring"""







         pass





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


         """simple docstring"""







         pass





    @slow
    def    SCREAMING_SNAKE_CASE_     (					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    SCREAMING_SNAKE_CASE_     (					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

