


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE       :      Tuple												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= {
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	int     =			"""blip_2_vision_model"""
       def __init__(					self,      lowerCamelCase=14_08,      lowerCamelCase=61_44,      lowerCamelCase=39,      lowerCamelCase=16,      lowerCamelCase=2_24,      lowerCamelCase=14,      lowerCamelCase="gelu",      lowerCamelCase=0.0_0_0_0_1,      lowerCamelCase=0.0,      lowerCamelCase=1E-10,      lowerCamelCase=True,      **lowerCamelCase,      )       ->   str:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

           _lowercase       :							Tuple      		=		hidden_size
           _lowercase       :							Tuple      		=		intermediate_size
           _lowercase       :							Optional[int]      		=		num_hidden_layers
           _lowercase       :							Tuple      		=		num_attention_heads
           _lowercase       :							List[Any]      		=		patch_size
           _lowercase       :							List[str]      		=		image_size
           _lowercase       :							Dict      		=		initializer_range
           _lowercase       :							str      		=		attention_dropout
           _lowercase       :							Tuple      		=		layer_norm_eps
           _lowercase       :							List[Any]      		=		hidden_act
           _lowercase       :							Optional[Any]      		=		qkv_bias




       @classmethod
       def 			UpperCamelCase     (					cls,      lowerCamelCase,      **lowerCamelCase)       ->   "PretrainedConfig":




           """simple docstring"""

           cls._set_token_in_kwargs(lowerCamelCase)

           _lowercase		,				_lowercase       :							Dict      		=		cls.get_config_dict(lowerCamelCase,      **lowerCamelCase)

           # get the vision config dict if we are loading from Blip2Config
           if config_dict.get('model_type') == "blip-2":
               _lowercase       :							Tuple      		=		config_dict['vision_config']

           if "model_type" in config_dict and hasattr(cls,      'model_type') and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''')

           return cls.from_dict(lowerCamelCase,      **lowerCamelCase)






class _lowerCamelCase(						_a   ):
       lowercase_ :	List[Any]     =			"""blip_2_qformer"""
       def __init__(					self,      lowerCamelCase=3_05_22,      lowerCamelCase=7_68,      lowerCamelCase=12,      lowerCamelCase=12,      lowerCamelCase=30_72,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=5_12,      lowerCamelCase=0.0_2,      lowerCamelCase=1E-12,      lowerCamelCase=0,      lowerCamelCase="absolute",      lowerCamelCase=2,      lowerCamelCase=14_08,      **lowerCamelCase,      )       ->   Tuple:




           """simple docstring"""

           super().__init__(pad_token_id=lowerCamelCase,      **lowerCamelCase)

           _lowercase       :							Any      		=		vocab_size
           _lowercase       :							int      		=		hidden_size
           _lowercase       :							List[str]      		=		num_hidden_layers
           _lowercase       :							List[str]      		=		num_attention_heads
           _lowercase       :							Dict      		=		hidden_act
           _lowercase       :							str      		=		intermediate_size
           _lowercase       :							Optional[int]      		=		hidden_dropout_prob
           _lowercase       :							str      		=		attention_probs_dropout_prob
           _lowercase       :							int      		=		max_position_embeddings
           _lowercase       :							Dict      		=		initializer_range
           _lowercase       :							Optional[int]      		=		layer_norm_eps
           _lowercase       :							Tuple      		=		position_embedding_type
           _lowercase       :							Tuple      		=		cross_attention_frequency
           _lowercase       :							List[Any]      		=		encoder_hidden_size




       @classmethod
       def 			UpperCamelCase     (					cls,      lowerCamelCase,      **lowerCamelCase)       ->   "PretrainedConfig":




           """simple docstring"""

           cls._set_token_in_kwargs(lowerCamelCase)

           _lowercase		,				_lowercase       :							int      		=		cls.get_config_dict(lowerCamelCase,      **lowerCamelCase)

           # get the qformer config dict if we are loading from Blip2Config
           if config_dict.get('model_type') == "blip-2":
               _lowercase       :							str      		=		config_dict['qformer_config']

           if "model_type" in config_dict and hasattr(cls,      'model_type') and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''')

           return cls.from_dict(lowerCamelCase,      **lowerCamelCase)






class _lowerCamelCase(						_a   ):
       lowercase_ :	Dict     =			"""blip-2"""
       lowercase_ :	Union[str, Any]     =			True
       def __init__(					self,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=32,      **lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

           if vision_config is None:
               _lowercase       :							List[Any]      		=		{}
               logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.')

           if qformer_config is None:
               _lowercase       :							str      		=		{}
               logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.')

           if text_config is None:
               _lowercase       :							Tuple      		=		{}
               logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).')

           _lowercase       :							Tuple      		=		BlipaVisionConfig(**lowerCamelCase)
           _lowercase       :							List[Any]      		=		BlipaQFormerConfig(**lowerCamelCase)
           _lowercase       :							Tuple      		=		text_config['model_type'] if 'model_type' in text_config else 'opt'
           _lowercase       :							Optional[Any]      		=		CONFIG_MAPPING[text_model_type](**lowerCamelCase)

           _lowercase       :							Optional[int]      		=		self.text_config.tie_word_embeddings
           _lowercase       :							int      		=		self.text_config.is_encoder_decoder

           _lowercase       :							List[Any]      		=		num_query_tokens
           _lowercase       :							Dict      		=		self.vision_config.hidden_size
           _lowercase       :							Any      		=		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           _lowercase       :							Union[str, Any]      		=		1.0
           _lowercase       :							List[str]      		=		0.0_2
       @classmethod
       def 			UpperCamelCase     (					cls,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase,      )       ->   Union[str, Any]:




           """simple docstring"""

           return cls(
               vision_config=vision_config.to_dict(),      qformer_config=qformer_config.to_dict(),      text_config=text_config.to_dict(),      **lowerCamelCase,      )




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		copy.deepcopy(self.__dict__)
           _lowercase       :							Union[str, Any]      		=		self.vision_config.to_dict()
           _lowercase       :							Optional[Any]      		=		self.qformer_config.to_dict()
           _lowercase       :							Dict      		=		self.text_config.to_dict()
           _lowercase       :							int      		=		self.__class__.model_type
           return output









import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      Union[str, Any]				,			a__       :      int=13				,			a__       :      int=7				,			a__       :      Optional[Any]=True				,			a__       :      Optional[int]=True				,			a__       :      Any=True				,			a__       :      str=True				,			a__       :      List[Any]=99				,			a__       :      Any=24				,			a__       :      List[str]=2				,			a__       :      Optional[int]=6				,			a__       :      int=37				,			a__       :      List[str]="gelu"				,			a__       :      List[Any]=0.1				,			a__       :      Optional[int]=0.1				,			a__       :      Union[str, Any]=512				,			a__       :      List[str]=16				,			a__       :      Optional[int]=2				,			a__       :      Union[str, Any]=0.0_2				,			a__       :      str=3				,			a__       :      Optional[Any]=None				,			a__       :      Any=1000				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   seq_length
         __snake_case							       =   is_training
         __snake_case							       =   use_input_mask
         __snake_case							       =   use_token_type_ids
         __snake_case							       =   use_labels
         __snake_case							       =   vocab_size
         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   type_vocab_size
         __snake_case							       =   type_sequence_label_size
         __snake_case							       =   initializer_range
         __snake_case							       =   num_labels
         __snake_case							       =   scope
         __snake_case							       =   range_bbox


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size      )

         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length, 4]				,			self.range_bbox      )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]      ):
            for j in range(bbox.shape[1]      ):
               if bbox[i, j, 3] < bbox[i, j, 1]:
                  __snake_case							       =   bbox[i, j, 3]
                  __snake_case							       =   bbox[i, j, 1]
                  __snake_case							       =   t
               if bbox[i, j, 2] < bbox[i, j, 0]:
                  __snake_case							       =   bbox[i, j, 2]
                  __snake_case							       =   bbox[i, j, 0]
                  __snake_case							       =   t

         __snake_case							       =   None
         if self.use_input_mask:
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2      )

         __snake_case							       =   None
         if self.use_token_type_ids:
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size      )

         __snake_case							       =   None
         __snake_case							       =   None
         if self.use_labels:
            __snake_case							       =   ids_tensor([self.batch_size]				,			self.type_sequence_label_size      )
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels      )

         __snake_case							       =   self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels


      def    a (self       :      List[str]      ):





         """simple docstring"""



         return LiltConfig(
             vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			initializer_range=self.initializer_range				,			)


      def    a (self       :      List[Any]				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      int				,			a__       :      Optional[int]				,			a__       :      str				,			a__       :      Optional[int]				,			):





         """simple docstring"""



         __snake_case							       =   LiltModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=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    a (self       :      Any				,			a__       :      Tuple				,			a__       :      Dict				,			a__       :      Optional[int]				,			a__       :      Dict				,			a__       :      Union[str, Any]				,			a__       :      str				,			a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LiltForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			labels=a__      )
         self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)      )


      def    a (self       :      int				,			a__       :      Optional[Any]				,			a__       :      int				,			a__       :      int				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      str				,			):





         """simple docstring"""



         __snake_case							       =   LiltForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			start_positions=a__				,			end_positions=a__				,			)
         self.parent.assertEqual(result.start_logits.shape				,			(self.batch_size, self.seq_length)      )
         self.parent.assertEqual(result.end_logits.shape				,			(self.batch_size, self.seq_length)      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	
         )							       =   config_and_inputs
         __snake_case							       =   {
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Any							=    (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A_						:				Optional[int]							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Dict				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         return True


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





         """simple docstring"""



         __snake_case							       =   LiltModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            __snake_case							       =   type
            self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__      )


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





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__      )


      @slow
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   LiltModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






@require_torch
@slow
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''      ).to(a__      )

         __snake_case							       =   torch.tensor([[1, 2]]				,			device=a__      )
         __snake_case							       =   torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,			device=a__      )

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(input_ids=a__				,			bbox=a__      )

         __snake_case							       =   torch.Size([1, 2, 768]      )
         __snake_case							       =   torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]				,			device=a__				,			)

         self.assertTrue(outputs.last_hidden_state.shape				,			a__      )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,			a__				,			atol=1E-3      )      )

