





'''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 				lowerCAmelCase_						:




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=12     ,       _lowerCAmelCase=7     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=99     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=5     ,       _lowerCAmelCase=4     ,       _lowerCAmelCase=37     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=512     ,       _lowerCAmelCase=0.02     ,       _lowerCAmelCase=0     ,       _lowerCAmelCase=None     ,       )      ->							str:
     _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						)      ->							Union[str, Any]:
     _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     ,     _lowerCAmelCase		     = input_mask.shape
         _lowerCAmelCase		     = np.random.randint(1     ,       seq_length - 1     ,       size=(batch_size,)						)
         for batch_idx, start_index in enumerate(_lowerCAmelCase						):
             _lowerCAmelCase		     = 1
             _lowerCAmelCase		     = 0

     _lowerCAmelCase		     = self.get_config()

     return config, input_ids, input_mask




 def 							_snake_case				(		self						)      ->							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     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							int:
     _lowerCAmelCase		     = BlipTextModel(config=_lowerCAmelCase						)
     model.to(_lowerCAmelCase						)
     model.eval()
     with torch.no_grad():
         _lowerCAmelCase		     = model(_lowerCAmelCase     ,       attention_mask=_lowerCAmelCase						)
         _lowerCAmelCase		     = model(_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 							_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_torch
class 				lowerCAmelCase_						(						__magic_name__  ,unittest.TestCase						):
 __lowerCamelCase  :							str    					=   (BlipTextModel,) if is_torch_available() else ()
 __lowerCamelCase  :							Dict    					=   False
 __lowerCamelCase  :							List[str]    					=   False
 __lowerCamelCase  :							str    					=   False




 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = BlipTextModelTester(self						)
     _lowerCAmelCase		     = ConfigTester(self     ,       config_class=_lowerCAmelCase     ,       hidden_size=37						)




 def 							_snake_case				(		self						)      ->							List[str]:
     self.config_tester.run_common_tests()




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Dict:
     pass




 def 							_snake_case				(		self						)      ->							List[Any]:
     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						)      ->							Dict:
     pass




 @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"						)
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     pass




 @slow
 def 							_snake_case				(		self						)      ->							str:
     for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase		     = BlipTextModel.from_pretrained(_lowerCAmelCase						)
         self.assertIsNotNone(_lowerCAmelCase						)





 def 							_snake_case				(		self						)      ->							Tuple:
     super().test_pt_tf_model_equivalence(allow_missing_keys=_lowerCAmelCase						)









'''simple docstring'''


from typing import Mapping

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


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							str    					=   "mt5"
 __lowerCamelCase  :							Dict    					=   ["past_key_values"]




 def __init__(		self     ,       _lowerCAmelCase=250112     ,       _lowerCAmelCase=512     ,       _lowerCAmelCase=64     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=8     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=6     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=128     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=1E-6     ,       _lowerCAmelCase=1.0     ,       _lowerCAmelCase="gated-gelu"     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase="T5Tokenizer"     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=0     ,       _lowerCAmelCase=1     ,       _lowerCAmelCase=0     ,       **_lowerCAmelCase     ,       )      ->							Optional[Any]:
     super().__init__(
         is_encoder_decoder=_lowerCAmelCase     ,       tokenizer_class=_lowerCAmelCase     ,       tie_word_embeddings=_lowerCAmelCase     ,       pad_token_id=_lowerCAmelCase     ,       eos_token_id=_lowerCAmelCase     ,       decoder_start_token_id=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = d_model
     _lowerCAmelCase		     = d_kv
     _lowerCAmelCase		     = d_ff
     _lowerCAmelCase		     = num_layers
     _lowerCAmelCase		     = (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase		     = num_heads
     _lowerCAmelCase		     = relative_attention_num_buckets
     _lowerCAmelCase		     = relative_attention_max_distance
     _lowerCAmelCase		     = dropout_rate
     _lowerCAmelCase		     = layer_norm_epsilon
     _lowerCAmelCase		     = initializer_factor
     _lowerCAmelCase		     = feed_forward_proj
     _lowerCAmelCase		     = use_cache

     _lowerCAmelCase		     = self.feed_forward_proj.split("-"						)
     _lowerCAmelCase		     = act_info[-1]
     _lowerCAmelCase		     = act_info[0] == "gated"

     if len(_lowerCAmelCase						) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase						) > 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":
         _lowerCAmelCase		     = "gelu_new"




 @property
 def 							_snake_case				(		self						)      ->							Any:
     return self.d_model




 @property
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     return self.num_heads





 @property
 def 							_snake_case				(		self						)      ->							List[str]:
     return self.num_layers







class 				lowerCAmelCase_						(						__magic_name__						):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 							_snake_case				(		self						)      ->							Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase		     = {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
         _lowerCAmelCase		     = "past_encoder_sequence + sequence"
         _lowerCAmelCase		     = {0: "batch"}
         _lowerCAmelCase		     = {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
         self.fill_with_past_key_values_(_lowerCAmelCase     ,       direction="inputs"						)

     return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 							_snake_case				(		self						)      ->							int:
     return 13





 @property
 def 							_snake_case				(		self						)      ->							float:
     return 5E-4


