




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


lowerCamelCase    :		Dict			   =       logging.get_logger(__name__)

lowerCamelCase    :		Any			   =       {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}







class   A(						a_       ):



	'''simple docstring'''


	UpperCamelCase						       =						"switch_transformers"
	UpperCamelCase						       =						["past_key_values"]
	UpperCamelCase						       =						{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

	def __init__(    self :  Tuple						,							A_ :  Optional[Any]=32128						,							A_ :  Optional[Any]=768						,							A_ :  Tuple=64						,							A_ :  List[Any]=2048						,							A_ :  str=64						,							A_ :  Union[str, Any]=12						,							A_ :  List[Any]=3						,							A_ :  Optional[int]=12						,							A_ :  int=3						,							A_ :  Any=12						,							A_ :  Union[str, Any]=8						,							A_ :  List[Any]=False						,							A_ :  int=0.01						,							A_ :  Any="float32"						,							A_ :  Union[str, Any]=False						,							A_ :  Optional[int]=32						,							A_ :  Optional[Any]=128						,							A_ :  List[str]=0.1						,							A_ :  Union[str, Any]=1E-6						,							A_ :  List[Any]=0.001						,							A_ :  int=0.001						,							A_ :  Optional[int]=1.0						,							A_ :  Optional[int]="relu"						,							A_ :  Optional[Any]=True						,							A_ :  Any=False						,							A_ :  Tuple=True						,							A_ :  List[Any]=0						,							A_ :  Optional[Any]=1						,							**A_ :  Any						,							)       ->     str:

							"""simple docstring"""






							lowerCamelCase_							=   vocab_size
							lowerCamelCase_							=   d_model
							lowerCamelCase_							=   d_kv
							lowerCamelCase_							=   d_ff

							lowerCamelCase_							=   num_sparse_encoder_layers

							lowerCamelCase_							=   num_layers
							lowerCamelCase_							=   (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							lowerCamelCase_							=   num_sparse_decoder_layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_encoder_layers > 0:
													lowerCamelCase_							=   self.num_layers // self.num_sparse_encoder_layers
							else:
													lowerCamelCase_							=   self.num_layers  # HACK: this will create 0 sparse layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_decoder_layers > 0:
													lowerCamelCase_							=   self.num_decoder_layers // self.num_sparse_decoder_layers
							else:
													lowerCamelCase_							=   self.num_decoder_layers  # HACK: this will create 0 sparse layers

							lowerCamelCase_							=   num_heads
							lowerCamelCase_							=   num_experts
							lowerCamelCase_							=   expert_capacity
							lowerCamelCase_							=   router_bias
							lowerCamelCase_							=   router_jitter_noise
							if router_dtype not in ["float32", "float16", "bfloat16"]:
													raise ValueError(f"""`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}"""  )
							lowerCamelCase_							=   router_dtype

							lowerCamelCase_							=   router_ignore_padding_tokens
							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_							=   add_router_probs

							lowerCamelCase_							=   router_z_loss_coef
							lowerCamelCase_							=   router_aux_loss_coef

							lowerCamelCase_							=   self.feed_forward_proj.split('-'  )
							lowerCamelCase_							=   act_info[-1]
							lowerCamelCase_							=   act_info[0] == 'gated'

							if len(A_  ) > 1 and act_info[0] != "gated" or len(A_  ) > 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'

							super().__init__(
							    pad_token_id=A_						,							eos_token_id=A_						,							is_encoder_decoder=A_						,							**A_						,							)









import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''image_processor''', '''tokenizer''']
 UpperCamelCase						       =						'''OwlViTImageProcessor'''
 UpperCamelCase						       =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(    self :  Optional[Any]						,							A_ :  Tuple=None						,							A_ :  Tuple=None						,							**A_ :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'						,							A_						,							)
             lowerCamelCase_							=   kwargs.pop('feature_extractor'  )

       lowerCamelCase_							=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(A_						,							A_  )

 def __call__(    self :  List[str]						,							A_ :  List[str]=None						,							A_ :  List[Any]=None						,							A_ :  Dict=None						,							A_ :  Tuple="max_length"						,							A_ :  int="np"						,							**A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       if text is None and query_images is None and images is None:
             raise ValueError(
                 'You have to specify at least one text or query image or image. All three cannot be none.'  )

       if text is not None:
             if isinstance(A_						,							A_  ) or (isinstance(A_						,							A_  ) and not isinstance(text[0]						,							A_  )):
                   lowerCamelCase_							=   [self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )]

             elif isinstance(A_						,							A_  ) and isinstance(text[0]						,							A_  ):
                   lowerCamelCase_							=   []

                   # Maximum number of queries across batch
                   lowerCamelCase_							=   max([len(A_  ) for t in text]  )

                   # Pad all batch samples to max number of text queries
                   for t in text:
                         if len(A_  ) != max_num_queries:
                               lowerCamelCase_							=   t + [' '] * (max_num_queries - len(A_  ))

                         lowerCamelCase_							=   self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )
                         encodings.append(A_  )
             else:
                   raise TypeError('Input text should be a string, a list of strings or a nested list of strings'  )

             if return_tensors == "np":
                   lowerCamelCase_							=   np.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   np.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "jax" and is_flax_available():
                   import jax.numpy as jnp

                   lowerCamelCase_							=   jnp.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   jnp.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "pt" and is_torch_available():
                   import torch

                   lowerCamelCase_							=   torch.cat([encoding['input_ids'] for encoding in encodings]						,							dim=0  )
                   lowerCamelCase_							=   torch.cat([encoding['attention_mask'] for encoding in encodings]						,							dim=0  )

             elif return_tensors == "tf" and is_tf_available():
                   import tensorflow as tf

                   lowerCamelCase_							=   tf.stack([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   tf.stack([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             else:
                   raise ValueError('Target return tensor type could not be returned'  )

             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   input_ids
             lowerCamelCase_							=   attention_mask

       if query_images is not None:
             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   self.image_processor(
                 A_						,							return_tensors=A_						,							**A_  ).pixel_values
             lowerCamelCase_							=   query_pixel_values

       if images is not None:
             lowerCamelCase_							=   self.image_processor(A_						,							return_tensors=A_						,							**A_  )

       if text is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif query_images is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif text is not None or query_images is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**A_  )						,							tensor_type=A_  )

 def     a__			(    self :  Tuple						,							*A_ :  Dict						,							**A_ :  Dict  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process(*A_						,							**A_  )

 def     a__			(    self :  List[str]						,							*A_ :  Any						,							**A_ :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       return self.image_processor.post_process_object_detection(*A_						,							**A_  )

 def     a__			(    self :  Any						,							*A_ :  str						,							**A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process_image_guided_detection(*A_						,							**A_  )

 def     a__			(    self :  Union[str, Any]						,							*A_ :  Any						,							**A_ :  Union[str, Any]  )       ->     str:

       """simple docstring"""






       return self.tokenizer.batch_decode(*A_						,							**A_  )

 def     a__			(    self :  Optional[int]						,							*A_ :  List[Any]						,							**A_ :  int  )       ->     int:

       """simple docstring"""






       return self.tokenizer.decode(*A_						,							**A_  )

 @property
 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,							A_						,							)
       return self.image_processor_class





 @property
 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,							A_						,							)
       return self.image_processor



