





'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


a__							:							Dict =logging.get_logger(__name__)

a__							:							Optional[int] ={
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					="perceiver"






 def __init__(       self       :							Any							,	__A       :							Any=2_5_6							,	__A       :							Tuple=1_2_8_0							,	__A       :							Dict=7_6_8							,	__A       :							Optional[Any]=1							,	__A       :							str=2_6							,	__A       :							Optional[Any]=8							,	__A       :							Dict=8							,	__A       :							Any=None							,	__A       :							Tuple=None							,	__A       :							Optional[int]="kv"							,	__A       :							Optional[int]=1							,	__A       :							Any=1							,	__A       :							Any="gelu"							,	__A       :							Dict=0.1							,	__A       :							Any=0.02							,	__A       :							int=1e-12							,	__A       :							Optional[int]=True							,	__A       :							Optional[Any]=2_6_2							,	__A       :							List[Any]=2_0_4_8							,	__A       :							List[str]=5_6							,	__A       :							Optional[int]=[3_6_8, 4_9_6]							,	__A       :							List[Any]=1_6							,	__A       :							Optional[int]=1_9_2_0							,	__A       :							Optional[int]=1_6							,	__A       :							Dict=[1, 1_6, 2_2_4, 2_2_4]							,	**__A       :							Union[str, Any]							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  num_latents
        __UpperCamelCase       =  d_latents
        __UpperCamelCase       =  d_model
        __UpperCamelCase       =  num_blocks
        __UpperCamelCase       =  num_self_attends_per_block
        __UpperCamelCase       =  num_self_attention_heads
        __UpperCamelCase       =  num_cross_attention_heads
        __UpperCamelCase       =  qk_channels
        __UpperCamelCase       =  v_channels
        __UpperCamelCase       =  cross_attention_shape_for_attention
        __UpperCamelCase       =  self_attention_widening_factor
        __UpperCamelCase       =  cross_attention_widening_factor
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  use_query_residual
        # masked language modeling attributes
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  max_position_embeddings
        # image classification attributes
        __UpperCamelCase       =  image_size
        # flow attributes
        __UpperCamelCase       =  train_size
        # multimodal autoencoding attributes
        __UpperCamelCase       =  num_frames
        __UpperCamelCase       =  audio_samples_per_frame
        __UpperCamelCase       =  samples_per_patch
        __UpperCamelCase       =  output_shape






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @property
 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        if self.task == "multiple-choice":
               __UpperCamelCase       =  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
               __UpperCamelCase       =  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('inputs', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]	)






 @property
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return 1e-4






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,	__A       :							int = -1							,	__A       :							int = -1							,	__A       :							int = -1							,	__A       :							bool = False							,	__A       :							Optional[TensorType] = None							,	__A       :							int = 3							,	__A       :							int = 4_0							,	__A       :							int = 4_0							,	):
        # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

        if isinstance(__A							,	__A	):
               # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
               __UpperCamelCase       =  compute_effective_axis_dimension(
                   __A							,	fixed_dimension=OnnxConfig.default_fixed_batch							,	num_token_to_add=0	)
               # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
               __UpperCamelCase       =  preprocessor.num_special_tokens_to_add(__A	)
               __UpperCamelCase       =  compute_effective_axis_dimension(
                   __A							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__A	)
               # Generate dummy inputs according to compute batch and sequence
               __UpperCamelCase       =  [' '.join(['a']	) * seq_length] * batch_size
               __UpperCamelCase       =  dict(preprocessor(__A							,	return_tensors=__A	)	)
               __UpperCamelCase       =  inputs.pop('input_ids'	)
               return inputs
        elif isinstance(__A							,	__A	) and preprocessor.model_input_names[0] == "pixel_values":
               # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
               __UpperCamelCase       =  compute_effective_axis_dimension(__A							,	fixed_dimension=OnnxConfig.default_fixed_batch	)
               __UpperCamelCase       =  self._generate_dummy_images(__A							,	__A							,	__A							,	__A	)
               __UpperCamelCase       =  dict(preprocessor(images=__A							,	return_tensors=__A	)	)
               __UpperCamelCase       =  inputs.pop('pixel_values'	)
               return inputs
        else:
               raise ValueError(
                   'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'	)







'''simple docstring'''
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__							:							List[Any] =logging.get_logger(__name__)

a__							:							List[Any] ={
    '''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_text_model"






 def __init__(       self       :							str							,	__A       :							List[Any]=2_5_0_0_0_2							,	__A       :							Any=1_0_2_4							,	__A       :							int=2_4							,	__A       :							Dict=1_6							,	__A       :							Optional[Any]=4_0_9_6							,	__A       :							Union[str, Any]="gelu"							,	__A       :							Dict=0.1							,	__A       :							Dict=0.1							,	__A       :							List[str]=5_1_4							,	__A       :							Optional[int]=1							,	__A       :							int=0.02							,	__A       :							Optional[Any]=0.02							,	__A       :							Optional[Any]=1e-05							,	__A       :							Dict=1							,	__A       :							List[Any]=0							,	__A       :							int=2							,	__A       :							Tuple="absolute"							,	__A       :							Optional[Any]=True							,	__A       :							Optional[int]=7_6_8							,	**__A       :							List[str]							,	):
        super().__init__(pad_token_id=__A							,	bos_token_id=__A							,	eos_token_id=__A							,	**__A	)

        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  type_vocab_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  position_embedding_type
        __UpperCamelCase       =  use_cache
        __UpperCamelCase       =  project_dim






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_vision_model"






 def __init__(       self       :							List[Any]							,	__A       :							Union[str, Any]=7_6_8							,	__A       :							Optional[int]=3_0_7_2							,	__A       :							Optional[Any]=5_1_2							,	__A       :							Tuple=1_2							,	__A       :							Union[str, Any]=1_2							,	__A       :							Optional[int]=3							,	__A       :							Dict=2_2_4							,	__A       :							Tuple=3_2							,	__A       :							str="quick_gelu"							,	__A       :							Dict=1e-5							,	__A       :							Optional[int]=0.0							,	__A       :							List[Any]=0.02							,	__A       :							int=1.0							,	**__A       :							Optional[int]							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  hidden_act






 @classmethod
 def        _lowerCamelCase					(       cls       :							Optional[Any]							,	__A       :							Union[str, os.PathLike]							,	**__A       :							Optional[Any]	):
        cls._set_token_in_kwargs(__A	)

        __UpperCamelCase       ,						__UpperCamelCase       =  cls.get_config_dict(__A							,	**__A	)

        # get the vision config dict if we are loading from AltCLIPConfig
        if config_dict.get('model_type'	) == "altclip":
               __UpperCamelCase       =  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(__A							,	**__A	)






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					="altclip"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=True






 def __init__(       self       :							Any							,	__A       :							List[str]=None							,	__A       :							List[Any]=None							,	__A       :							List[str]=7_6_8							,	__A       :							List[str]=2.6592							,	**__A       :							Dict	):
        # If `_config_dict` exist, we use them for the backward compatibility.
        # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
        # of confusion!).
        __UpperCamelCase       =  kwargs.pop('text_config_dict'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('vision_config_dict'							,	__A	)

        super().__init__(**__A	)

        # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
        # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
        # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
        if text_config_dict is not None:
               if text_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `text_config_dict`.
               __UpperCamelCase       =  AltCLIPTextConfig(**__A	).to_dict()

               # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
               for key, value in _text_config_dict.items():
                      if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                             # If specified in `text_config_dict`
                             if key in text_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
                                        f'''The value `text_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
                                        f'''value `text_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
               text_config.update(_text_config_dict	)

        if vision_config_dict is not None:
               if vision_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `vision_config_dict`.
               __UpperCamelCase       =  AltCLIPVisionConfig(**__A	).to_dict()
               # convert keys to string instead of integer
               if "id2label" in _vision_config_dict:
                      __UpperCamelCase       =  {
                          str(__A	): value for key, value in _vision_config_dict['id2label'].items()
                      }

               # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
               for key, value in _vision_config_dict.items():
                      if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                             # If specified in `vision_config_dict`
                             if key in vision_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
                                        f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
                                        f'''The value `vision_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
               vision_config.update(_vision_config_dict	)

        if text_config is None:
               __UpperCamelCase       =  {}
               logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.'	)

        if vision_config is None:
               __UpperCamelCase       =  {}
               logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.'	)

        __UpperCamelCase       =  AltCLIPTextConfig(**__A	)
        __UpperCamelCase       =  AltCLIPVisionConfig(**__A	)

        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  logit_scale_init_value
        __UpperCamelCase       =  1.0






 @classmethod
 def        _lowerCamelCase					(       cls       :							Union[str, Any]							,	__A       :							AltCLIPTextConfig							,	__A       :							AltCLIPVisionConfig							,	**__A       :							Optional[Any]	):
        return cls(text_config=text_config.to_dict()							,	vision_config=vision_config.to_dict()							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  copy.deepcopy(self.__dict__	)
        __UpperCamelCase       =  self.text_config.to_dict()
        __UpperCamelCase       =  self.vision_config.to_dict()
        __UpperCamelCase       =  self.__class__.model_type
        return output
