
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


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Union[str, Any]     						=  'instructblip_vision_model'
      def __init__(      self		,						lowerCamelCase=14_08		,						lowerCamelCase=61_44		,						lowerCamelCase=39		,						lowerCamelCase=16		,						lowerCamelCase=2_24		,						lowerCamelCase=14		,						lowerCamelCase="gelu"		,						lowerCamelCase=1e-6		,						lowerCamelCase=0.0		,						lowerCamelCase=1e-10		,						lowerCamelCase=True		,						**lowerCamelCase		,						):
          super().__init__(**lowerCamelCase			)

          snake_case__    =					hidden_size
          snake_case__    =					intermediate_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					patch_size
          snake_case__    =					image_size
          snake_case__    =					initializer_range
          snake_case__    =					attention_dropout
          snake_case__    =					layer_norm_eps
          snake_case__    =					hidden_act
          snake_case__    =					qkv_bias



      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						**lowerCamelCase			):
          cls._set_token_in_kwargs(lowerCamelCase			)

          snake_case__    ,       snake_case__    =					cls.get_config_dict(lowerCamelCase		,						**lowerCamelCase			)

          # get the vision config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"			) == "instructblip":
              snake_case__    =					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        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		int     						=  'instructblip_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		,						):
          super().__init__(pad_token_id=lowerCamelCase		,						**lowerCamelCase			)

          snake_case__    =					vocab_size
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					hidden_act
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					max_position_embeddings
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					position_embedding_type
          snake_case__    =					cross_attention_frequency
          snake_case__    =					encoder_hidden_size



      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						**lowerCamelCase			):
          cls._set_token_in_kwargs(lowerCamelCase			)

          snake_case__    ,       snake_case__    =					cls.get_config_dict(lowerCamelCase		,						**lowerCamelCase			)

          # get the qformer config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"			) == "instructblip":
              snake_case__    =					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        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		int     						=  'instructblip'
      _A    :		Optional[Any]     						=  True
      def __init__(      self		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=32		,						**lowerCamelCase			):
          super().__init__(**lowerCamelCase			)

          if vision_config is None:
              snake_case__    =					{}
              logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."			)

          if qformer_config is None:
              snake_case__    =					{}
              logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."			)

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

          snake_case__    =					InstructBlipVisionConfig(**lowerCamelCase			)
          snake_case__    =					InstructBlipQFormerConfig(**lowerCamelCase			)
          snake_case__    =					text_config["model_type"] if "model_type" in text_config else "opt"
          snake_case__    =					CONFIG_MAPPING[text_model_type](**lowerCamelCase			)

          snake_case__    =					self.text_config.tie_word_embeddings
          snake_case__    =					self.text_config.is_encoder_decoder

          snake_case__    =					num_query_tokens
          snake_case__    =					self.vision_config.hidden_size
          snake_case__    =					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          snake_case__    =					1.0
          snake_case__    =					0.0_2
      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						**lowerCamelCase		,						):
          return cls(
              vision_config=vision_config.to_dict()		,						qformer_config=qformer_config.to_dict()		,						text_config=text_config.to_dict()		,						**lowerCamelCase		,						)



      def A_      (      self			):
          snake_case__    =					copy.deepcopy(self.__dict__			)
          snake_case__    =					self.vision_config.to_dict()
          snake_case__    =					self.qformer_config.to_dict()
          snake_case__    =					self.text_config.to_dict()
          snake_case__    =					self.__class__.model_type
          return output





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
     from transformers import TvltImageProcessor

if is_speech_available():
     from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					"ZinengTang/tvlt-base"
          snake_case__    =					tempfile.mkdtemp()
      def A_      (      self		,						**lowerCamelCase			):
          return TvltImageProcessor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self		,						**lowerCamelCase			):
          return TvltFeatureExtractor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)
          processor.save_pretrained(self.tmpdirname			)
          snake_case__    =					TvltProcessor.from_pretrained(self.tmpdirname			)

          self.assertIsInstance(processor.feature_extractor		,						lowerCamelCase			)
          self.assertIsInstance(processor.image_processor		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)

          snake_case__    =					feature_extractor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(audio=lowerCamelCase		,						return_tensors="np"			)

          for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(images=lowerCamelCase		,						return_tensors="np"			)

          for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)
          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					processor(audio=lowerCamelCase		,						images=lowerCamelCase			)

          self.assertListEqual(list(inputs.keys()			)		,						["audio_values", "audio_mask", "pixel_values", "pixel_mask"]			)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase			):
              processor()



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          self.assertListEqual(
              processor.model_input_names		,						image_processor.model_input_names + feature_extractor.model_input_names		,						msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"		,						)



