


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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__  :List[str]             =							{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Any             =							[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		lowerCAmelCase__  :str             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
