


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__  :Optional[Any]           =							logging.get_logger(__name__)

lowerCAmelCase__  :str           =							{
    '''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 		__a				(    UpperCAmelCase      ):
		_a     :  Dict           =     'bloom'
		_a     :  List[str]           =     ['past_key_values']
		_a     :  Tuple           =     {
		    'num_hidden_layers': 'n_layer',
		    'num_attention_heads': 'n_head',
		}
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=250880      ,   _SCREAMING_SNAKE_CASE=64      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=8      ,   _SCREAMING_SNAKE_CASE=1e-5      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=False      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      vocab_size
									# Backward compatibility with n_embed kwarg
									_UpperCAmelCase					=      kwargs.pop('n_embed'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      hidden_size if n_embed is None else n_embed
									_UpperCAmelCase					=      n_layer
									_UpperCAmelCase					=      n_head
									_UpperCAmelCase					=      layer_norm_epsilon
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      use_cache
									_UpperCAmelCase					=      pretraining_tp
									_UpperCAmelCase					=      apply_residual_connection_post_layernorm
									_UpperCAmelCase					=      hidden_dropout
									_UpperCAmelCase					=      attention_dropout

									_UpperCAmelCase					=      bos_token_id
									_UpperCAmelCase					=      eos_token_id
									_UpperCAmelCase					=      slow_but_exact

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



class 		__a				(    UpperCAmelCase      ):
		_a     :  Union[str, Any]           =     version.parse('1.12'      )
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = "default"      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   )			->	Union[str, Any]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE      ,   task=_SCREAMING_SNAKE_CASE      ,   patching_specs=_SCREAMING_SNAKE_CASE      ,   use_past=_SCREAMING_SNAKE_CASE						)
									if not getattr(self._config      ,   'pad_token_id'      ,   _SCREAMING_SNAKE_CASE						):
																# TODO: how to do that better?
																_UpperCAmelCase					=      0
		@property
		def      UpperCAmelCase__    (      self						)			->	Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									_UpperCAmelCase					=      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_(_SCREAMING_SNAKE_CASE      ,   direction='inputs'      ,   inverted_values_shape=_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      {0: 'batch', 1: 'past_sequence + sequence'}
									else:
																_UpperCAmelCase					=      {0: 'batch', 1: 'sequence'}

									return common_inputs
		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return self._config.n_layer
		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return self._config.n_head
		@property
		def      UpperCAmelCase__    (      self						)			->	float:




									"""simple docstring"""





									return 1e-3
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      super(_SCREAMING_SNAKE_CASE      ,   self						).generate_dummy_inputs(
									    _SCREAMING_SNAKE_CASE      ,   batch_size=_SCREAMING_SNAKE_CASE      ,   seq_length=_SCREAMING_SNAKE_CASE      ,   is_pair=_SCREAMING_SNAKE_CASE      ,   framework=_SCREAMING_SNAKE_CASE						)

									# We need to order the input in the way they appears in the forward()
									_UpperCAmelCase					=      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					=      common_inputs['input_ids'].shape
																							# Not using the same length for past_key_values
																							_UpperCAmelCase					=      seqlen + 2
																							_UpperCAmelCase					=      self._config.hidden_size // self.num_attention_heads
																							_UpperCAmelCase					=      (
																							    batch * self.num_attention_heads,
																							    head_dim,
																							    past_key_values_length,
																							)
																							_UpperCAmelCase					=      (
																							    batch * self.num_attention_heads,
																							    past_key_values_length,
																							    head_dim,
																							)
																							_UpperCAmelCase					=      [
																							    (torch.zeros(_SCREAMING_SNAKE_CASE						), torch.zeros(_SCREAMING_SNAKE_CASE						)) for _ in range(self.num_layers						)
																							]

									_UpperCAmelCase					=      common_inputs['attention_mask']
									if self.use_past:
																_UpperCAmelCase					=      ordered_inputs['attention_mask'].dtype
																_UpperCAmelCase					=      torch.cat(
																    [ordered_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   dtype=_SCREAMING_SNAKE_CASE						)]      ,   dim=1						)

									return ordered_inputs






		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return 13




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_UpperCAmelCase					=      image_std if image_std is not None else IMAGENET_STANDARD_STD
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return resize(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									return normalize(_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=      do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=      rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=      do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=      image_std if image_std is not None else self.image_std

									_UpperCAmelCase					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_SNAKE_CASE						):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

									if do_resize and size is None or resample is None:
																raise ValueError('Size and resample must be specified if do_resize is True.'						)

									if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'						)

									if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

									if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

									# All transformations expect numpy arrays.
									_UpperCAmelCase					=      [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

									if do_resize:
																_UpperCAmelCase					=      [self.resize(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE      ,   resample=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_center_crop:
																_UpperCAmelCase					=      [self.center_crop(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_rescale:
																_UpperCAmelCase					=      [self.rescale(image=_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_normalize:
																_UpperCAmelCase					=      [self.normalize(image=_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
