


'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"bloom"
 UpperCAmelCase				=					["past_key_values"]
 UpperCAmelCase				=					{
     "num_hidden_layers": "n_layer",
     "num_attention_heads": "n_head",
 }


 def __init__(      self							:	Optional[Any]			,       __lowerCamelCase							:	str=2_5_0_8_8_0			,       __lowerCamelCase							:	Any=6_4			,       __lowerCamelCase							:	Optional[int]=2			,       __lowerCamelCase							:	int=8			,       __lowerCamelCase							:	Optional[int]=1e-5			,       __lowerCamelCase							:	Union[str, Any]=0.02			,       __lowerCamelCase							:	str=True			,       __lowerCamelCase							:	Tuple=1			,       __lowerCamelCase							:	List[str]=2			,       __lowerCamelCase							:	Tuple=False			,       __lowerCamelCase							:	Union[str, Any]=0.0			,       __lowerCamelCase							:	Optional[Any]=0.0			,       __lowerCamelCase							:	Union[str, Any]=1			,       __lowerCamelCase							:	Optional[Any]=False			,       **__lowerCamelCase							:	Any			,       ):
    UpperCAmelCase__	:Optional[Any]      =      vocab_size
    # Backward compatibility with n_embed kwarg
    UpperCAmelCase__	:Optional[int]      =      kwargs.pop('''n_embed'''			,       __lowerCamelCase )
    UpperCAmelCase__	:Any      =      hidden_size if n_embed is None else n_embed
    UpperCAmelCase__	:List[Any]      =      n_layer
    UpperCAmelCase__	:Optional[int]      =      n_head
    UpperCAmelCase__	:Optional[int]      =      layer_norm_epsilon
    UpperCAmelCase__	:Optional[Any]      =      initializer_range
    UpperCAmelCase__	:List[str]      =      use_cache
    UpperCAmelCase__	:Optional[Any]      =      pretraining_tp
    UpperCAmelCase__	:Optional[int]      =      apply_residual_connection_post_layernorm
    UpperCAmelCase__	:List[Any]      =      hidden_dropout
    UpperCAmelCase__	:int      =      attention_dropout

    UpperCAmelCase__	:List[str]      =      bos_token_id
    UpperCAmelCase__	:Any      =      eos_token_id
    UpperCAmelCase__	:Union[str, Any]      =      slow_but_exact

    super().__init__(bos_token_id=__lowerCamelCase			,       eos_token_id=__lowerCamelCase			,       **__lowerCamelCase )




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					version.parse("1.12"						)


 def __init__(      self							:	str			,       __lowerCamelCase							:	PretrainedConfig			,       __lowerCamelCase							:	str = "default"			,       __lowerCamelCase							:	List[PatchingSpec] = None			,       __lowerCamelCase							:	bool = False			,       ):
    super().__init__(__lowerCamelCase			,       task=__lowerCamelCase			,       patching_specs=__lowerCamelCase			,       use_past=__lowerCamelCase )
    if not getattr(self._config			,       '''pad_token_id'''			,       __lowerCamelCase ):
       # TODO: how to do that better?
       UpperCAmelCase__	:Union[str, Any]      =      0


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:List[str]      =      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
    if self.use_past:
       # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
       self.fill_with_past_key_values_(__lowerCamelCase			,       direction='''inputs'''			,       inverted_values_shape=__lowerCamelCase )
       UpperCAmelCase__	:Any      =      {0: '''batch''', 1: '''past_sequence + sequence'''}
    else:
       UpperCAmelCase__	:Any      =      {0: '''batch''', 1: '''sequence'''}

    return common_inputs


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self._config.n_layer


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self._config.n_head


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    return 1e-3


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	"PreTrainedTokenizer"			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional["TensorType"] = None			,       ):
    UpperCAmelCase__	:Optional[Any]      =      super(__lowerCamelCase			,       self ).generate_dummy_inputs(
        __lowerCamelCase			,       batch_size=__lowerCamelCase			,       seq_length=__lowerCamelCase			,       is_pair=__lowerCamelCase			,       framework=__lowerCamelCase )

    # We need to order the input in the way they appears in the forward()
    UpperCAmelCase__	:Any      =      OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )

    # Need to add the past_keys
    if self.use_past:
       if not is_torch_available():
          raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
       else:
          import torch

          UpperCAmelCase__   ,						UpperCAmelCase__	:int      =      common_inputs['''input_ids'''].shape
          # Not using the same length for past_key_values
          UpperCAmelCase__	:List[Any]      =      seqlen + 2
          UpperCAmelCase__	:List[str]      =      self._config.hidden_size // self.num_attention_heads
          UpperCAmelCase__	:Optional[Any]      =      (
              batch * self.num_attention_heads,
              head_dim,
              past_key_values_length,
          )
          UpperCAmelCase__	:Union[str, Any]      =      (
              batch * self.num_attention_heads,
              past_key_values_length,
              head_dim,
          )
          UpperCAmelCase__	:List[Any]      =      [
              (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(self.num_layers )
          ]

    UpperCAmelCase__	:int      =      common_inputs['''attention_mask''']
    if self.use_past:
       UpperCAmelCase__	:List[Any]      =      ordered_inputs['''attention_mask'''].dtype
       UpperCAmelCase__	:Tuple      =      torch.cat(
           [ordered_inputs['''attention_mask'''], torch.ones(__lowerCamelCase			,       __lowerCamelCase			,       dtype=__lowerCamelCase )]			,       dim=1 )

    return ordered_inputs





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    return 1_3







'''simple docstring'''





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 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"OwlViTImageProcessor"
 UpperCAmelCase				=					("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int]=None			,       __lowerCamelCase							:	Optional[Any]=None			,       **__lowerCamelCase							:	str ):
    UpperCAmelCase__	:int      =      None
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''			,       __lowerCamelCase			,       )
       UpperCAmelCase__	:Union[str, Any]      =      kwargs.pop('''feature_extractor''' )

    UpperCAmelCase__	:List[str]      =      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__(__lowerCamelCase			,       __lowerCamelCase )


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

    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(__lowerCamelCase			,       __lowerCamelCase ) or (isinstance(__lowerCamelCase			,       __lowerCamelCase ) and not isinstance(text[0]			,       __lowerCamelCase )):
          UpperCAmelCase__	:Any      =      [self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )]

       elif isinstance(__lowerCamelCase			,       __lowerCamelCase ) and isinstance(text[0]			,       __lowerCamelCase ):
          UpperCAmelCase__	:Tuple      =      []

          # Maximum number of queries across batch
          UpperCAmelCase__	:List[str]      =      max([len(__lowerCamelCase ) for t in text] )

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

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

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

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

          UpperCAmelCase__	:List[Any]      =      jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Union[str, Any]      =      jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

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

          UpperCAmelCase__	:Any      =      torch.cat([encoding['''input_ids'''] for encoding in encodings]			,       dim=0 )
          UpperCAmelCase__	:List[str]      =      torch.cat([encoding['''attention_mask'''] for encoding in encodings]			,       dim=0 )

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

          UpperCAmelCase__	:Optional[Any]      =      tf.stack([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:int      =      tf.stack([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

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

       UpperCAmelCase__	:List[Any]      =      BatchEncoding()
       UpperCAmelCase__	:Union[str, Any]      =      input_ids
       UpperCAmelCase__	:Dict      =      attention_mask

    if query_images is not None:
       UpperCAmelCase__	:Tuple      =      BatchEncoding()
       UpperCAmelCase__	:int      =      self.image_processor(
           __lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase ).pixel_values
       UpperCAmelCase__	:Optional[int]      =      query_pixel_values

    if images is not None:
       UpperCAmelCase__	:str      =      self.image_processor(__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	int ):
    return self.image_processor.post_process(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Tuple ):
    return self.image_processor.post_process_object_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       *__lowerCamelCase							:	Tuple			,       **__lowerCamelCase							:	str ):
    return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[Any]			,       **__lowerCamelCase							:	Union[str, Any] ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       *__lowerCamelCase							:	Union[str, Any]			,       **__lowerCamelCase							:	Dict ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor_class





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor



