





import copy
import os
from typing import Union

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


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {
    """BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''altclip_text_model'''
      def __init__(		self      ,       a__=250002      ,       a__=1024      ,       a__=24      ,       a__=16      ,       a__=4096      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=514      ,       a__=1      ,       a__=0.02      ,       a__=0.02      ,       a__=1E-05      ,       a__=1      ,       a__=0      ,       a__=2      ,       a__="absolute"      ,       a__=True      ,       a__=768      ,       **a__      ,       ):
         super().__init__(pad_token_id=a__      ,       bos_token_id=a__      ,       eos_token_id=a__      ,       **a__       )

         A_     :				Tuple			      =	vocab_size
         A_     :				Union[str, Any]			      =	hidden_size
         A_     :				Optional[Any]			      =	num_hidden_layers
         A_     :				List[Any]			      =	num_attention_heads
         A_     :				Tuple			      =	hidden_act
         A_     :				int			      =	intermediate_size
         A_     :				Optional[int]			      =	hidden_dropout_prob
         A_     :				int			      =	attention_probs_dropout_prob
         A_     :				List[str]			      =	max_position_embeddings
         A_     :				int			      =	type_vocab_size
         A_     :				Optional[int]			      =	initializer_range
         A_     :				Union[str, Any]			      =	initializer_factor
         A_     :				Dict			      =	layer_norm_eps
         A_     :				int			      =	position_embedding_type
         A_     :				List[str]			      =	use_cache
         A_     :				Optional[int]			      =	project_dim






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''altclip_vision_model'''
      def __init__(		self      ,       a__=768      ,       a__=3072      ,       a__=512      ,       a__=12      ,       a__=12      ,       a__=3      ,       a__=224      ,       a__=32      ,       a__="quick_gelu"      ,       a__=1E-5      ,       a__=0.0      ,       a__=0.02      ,       a__=1.0      ,       **a__      ,       ):
         super().__init__(**a__       )

         A_     :				Optional[int]			      =	hidden_size
         A_     :				Union[str, Any]			      =	intermediate_size
         A_     :				Any			      =	projection_dim
         A_     :				str			      =	num_hidden_layers
         A_     :				Optional[int]			      =	num_attention_heads
         A_     :				Tuple			      =	num_channels
         A_     :				Any			      =	patch_size
         A_     :				Any			      =	image_size
         A_     :				Any			      =	initializer_range
         A_     :				int			      =	initializer_factor
         A_     :				Any			      =	attention_dropout
         A_     :				List[str]			      =	layer_norm_eps
         A_     :				Dict			      =	hidden_act






      @classmethod
      def 		_lowerCamelCase      (		cls      ,       a__      ,       **a__       ):
         cls._set_token_in_kwargs(a__       )

         A_      ,				A_     :				Any			      =	cls.get_config_dict(a__      ,       **a__       )

         # get the vision config dict if we are loading from AltCLIPConfig
         if config_dict.get("""model_type"""       ) == "altclip":
            A_     :				Any			      =	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    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''altclip'''
      a						       =							True
      def __init__(		self      ,       a__=None      ,       a__=None      ,       a__=768      ,       a__=2.6592      ,       **a__       ):
         # 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!).
         A_     :				Optional[Any]			      =	kwargs.pop("""text_config_dict"""      ,       a__       )
         A_     :				Optional[Any]			      =	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:
               A_     :				Optional[int]			      =	{}

            # This is the complete result when using `text_config_dict`.
            A_     :				str			      =	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:
                     A_     :				Union[str, Any]			      =	(
                         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:
                     A_     :				Tuple			      =	(
                         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:
               A_     :				Optional[Any]			      =	{}

            # This is the complete result when using `vision_config_dict`.
            A_     :				int			      =	AltCLIPVisionConfig(**a__       ).to_dict()
            # convert keys to string instead of integer
            if "id2label" in _vision_config_dict:
               A_     :				Tuple			      =	{
                   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:
                     A_     :				Optional[Any]			      =	(
                         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:
                     A_     :				Dict			      =	(
                         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:
            A_     :				Optional[Any]			      =	{}
            logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."""       )

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

         A_     :				List[Any]			      =	AltCLIPTextConfig(**a__       )
         A_     :				Tuple			      =	AltCLIPVisionConfig(**a__       )

         A_     :				Optional[Any]			      =	projection_dim
         A_     :				int			      =	logit_scale_init_value
         A_     :				Optional[Any]			      =	1.0
      @classmethod
      def 		_lowerCamelCase      (		cls      ,       a__      ,       a__      ,       **a__       ):
         return cls(text_config=text_config.to_dict()      ,       vision_config=vision_config.to_dict()      ,       **a__       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	copy.deepcopy(self.__dict__       )
         A_     :				Tuple			      =	self.text_config.to_dict()
         A_     :				str			      =	self.vision_config.to_dict()
         A_     :				int			      =	self.__class__.model_type
         return output













def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	[0] * len(_lowerCAmelCase     )
   A_     :				Optional[int]			      =	[]
   A_     :				str			      =	[]
   A_     :				Dict			      =	0

   for values in graph.values():
      for i in values:
         indegree[i] += 1

   for i in range(len(_lowerCAmelCase     )     ):
      if indegree[i] == 0:
         queue.append(_lowerCAmelCase     )

   while queue:
      A_     :				List[str]			      =	queue.pop(0     )
      cnt += 1
      topo.append(_lowerCAmelCase     )
      for x in graph[vertex]:
         indegree[x] -= 1
         if indegree[x] == 0:
            queue.append(_lowerCAmelCase     )

   if cnt != len(_lowerCAmelCase     ):
      print("""Cycle exists"""     )
   else:
      print(_lowerCAmelCase     )


# Adjacency List of Graph
_lowerCAmelCase 				=     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)






