



"""simple docstring"""


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

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


snake_case_				     =       logging.get_logger(__name__)


snake_case_				     =       {
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							"""codegen"""
						__UpperCamelCase						=							{
						    """max_position_embeddings""": """n_positions""",
						    """hidden_size""": """n_embd""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(					self :Optional[Any]   ,  lowercase_ :Optional[int]=5_04_00   ,  lowercase_ :Optional[int]=20_48   ,  lowercase_ :Union[str, Any]=20_48   ,  lowercase_ :int=40_96   ,  lowercase_ :List[str]=28   ,  lowercase_ :Dict=16   ,  lowercase_ :Tuple=64   ,  lowercase_ :Dict=None   ,  lowercase_ :int="gelu_new"   ,  lowercase_ :str=0.0   ,  lowercase_ :Optional[int]=0.0   ,  lowercase_ :Dict=0.0   ,  lowercase_ :List[str]=1E-5   ,  lowercase_ :Union[str, Any]=0.02   ,  lowercase_ :Optional[Any]=True   ,  lowercase_ :Optional[int]=5_02_56   ,  lowercase_ :int=5_02_56   ,  lowercase_ :List[str]=False   ,  **lowercase_ :Dict   ,  )				->			str:
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = n_ctx
												UpperCAmelCase             = n_positions
												UpperCAmelCase             = n_embd
												UpperCAmelCase             = n_layer
												UpperCAmelCase             = n_head
												UpperCAmelCase             = n_inner
												UpperCAmelCase             = rotary_dim
												UpperCAmelCase             = activation_function
												UpperCAmelCase             = resid_pdrop
												UpperCAmelCase             = embd_pdrop
												UpperCAmelCase             = attn_pdrop
												UpperCAmelCase             = layer_norm_epsilon
												UpperCAmelCase             = initializer_range
												UpperCAmelCase             = use_cache

												UpperCAmelCase             = bos_token_id
												UpperCAmelCase             = eos_token_id

												super().__init__(
												    bos_token_id=lowercase_   ,  eos_token_id=lowercase_   ,  tie_word_embeddings=lowercase_   ,  **lowercase_					)







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






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






						@property
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Mapping[str, Mapping[int, str]]:
												UpperCAmelCase             = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
												if self.use_past:
																		self.fill_with_past_key_values_(lowercase_   ,  direction='inputs'					)
																		UpperCAmelCase             = {0: 'batch', 1: 'past_sequence + sequence'}
												else:
																		UpperCAmelCase             = {0: 'batch', 1: 'sequence'}

												return common_inputs






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






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






						def 	UpperCAmelCase__     (					self :Optional[int]   ,  lowercase_ :PreTrainedTokenizer   ,  lowercase_ :int = -1   ,  lowercase_ :int = -1   ,  lowercase_ :bool = False   ,  lowercase_ :Optional[TensorType] = None   ,  )				->			Mapping[str, Any]:
												UpperCAmelCase             = super(lowercase_   ,  self					).generate_dummy_inputs(
												    lowercase_   ,  batch_size=lowercase_   ,  seq_length=lowercase_   ,  is_pair=lowercase_   ,  framework=lowercase_					)

												# 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             = (
																								    batch,
																								    self.num_attention_heads,
																								    past_key_values_length,
																								    self._config.hidden_size // self.num_attention_heads,
																								)
																								UpperCAmelCase             = [
																								    (torch.zeros(lowercase_					), torch.zeros(lowercase_					)) 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(lowercase_   ,  lowercase_   ,  dtype=lowercase_					)]   ,  dim=1					)

												return ordered_inputs






						@property
						def 	UpperCAmelCase__     (					self :List[str]					)				->			int:
												return 13







"""simple docstring"""





from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


_a														=		logging.get_logger(__name__)






class     _UpperCAmelCase(    lowerCamelCase	):



		def 						UpperCAmelCase							(					self				,		__a)       ->       Tuple:






							'''simple docstring'''
							if isinstance(__a				,		__a):
												_UpperCamelCase							 =  [label.strip() for label in labels.split(''',''') if label.strip()]
							return labels




		def __call__(					self				,		__a				,		__a				,		__a)       ->       Optional[int]:






							'''simple docstring'''
							if len(__a) == 0 or len(__a) == 0:
												raise ValueError('''You must include at least one label and at least one sequence.''')
							if hypothesis_template.format(labels[0]) == hypothesis_template:
												raise ValueError(
												    (
												        '''The provided hypothesis_template "{}" was not able to be formatted with the target labels. '''
												        '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
												    ).format(__a))

							if isinstance(__a				,		__a):
												_UpperCamelCase							 =  [sequences]

							_UpperCamelCase							 =  []
							for sequence in sequences:
												sequence_pairs.extend([[sequence, hypothesis_template.format(__a)] for label in labels])

							return sequence_pairs, sequences






@add_end_docstrings(lowerCamelCase	)
class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a=ZeroShotClassificationArgumentHandler()				,		*__a				,		**__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  args_parser
							super().__init__(*__a				,		**__a)
							if self.entailment_id == -1:
												logger.warning(
												    '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
												    '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.''')



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






							'''simple docstring'''
							for label, ind in self.model.config.labelaid.items():
												if label.lower().startswith('''entail'''):
																	return ind
							return -1



		def 						UpperCAmelCase							(					self				,		__a				,		__a=True				,		__a=True				,		__a=TruncationStrategy.ONLY_FIRST				,		**__a)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.framework
							if self.tokenizer.pad_token is None:
												# Override for tokenizers not supporting padding
												logger.error(
												    '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
												    ''' `pad_token=eos_token`''')
												_UpperCamelCase							 =  self.tokenizer.eos_token
							try:
												_UpperCamelCase							 =  self.tokenizer(
												    __a				,		add_special_tokens=__a				,		return_tensors=__a				,		padding=__a				,		truncation=__a				,		)
							except Exception as e:
												if "too short" in str(__a):
																	# tokenizers might yell that we want to truncate
																	# to a value that is not even reached by the input.
																	# In that case we don't want to truncate.
																	# It seems there's not a really better way to catch that
																	# exception.

																	_UpperCamelCase							 =  self.tokenizer(
																	    __a				,		add_special_tokens=__a				,		return_tensors=__a				,		padding=__a				,		truncation=TruncationStrategy.DO_NOT_TRUNCATE				,		)
												else:
																	raise e

							return inputs



		def 						UpperCAmelCase							(					self				,		**__a)       ->       Any:






							'''simple docstring'''
							if kwargs.get('''multi_class'''				,		__a) is not None:
												_UpperCamelCase							 =  kwargs['''multi_class''']
												logger.warning(
												    '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
												    '''`multi_class` will be removed in a future version of Transformers.''')
							_UpperCamelCase							 =  {}
							if "candidate_labels" in kwargs:
												_UpperCamelCase							 =  self._args_parser._parse_labels(kwargs['''candidate_labels'''])
							if "hypothesis_template" in kwargs:
												_UpperCamelCase							 =  kwargs['''hypothesis_template''']

							_UpperCamelCase							 =  {}
							if "multi_label" in kwargs:
												_UpperCamelCase							 =  kwargs['''multi_label''']
							return preprocess_params, {}, postprocess_params



		def __call__(					self				,		__a				,		*__a				,		**__a				,		)       ->       int:






							'''simple docstring'''
							if len(__a) == 0:
												pass
							elif len(__a) == 1 and "candidate_labels" not in kwargs:
												_UpperCamelCase							 =  args[0]
							else:
												raise ValueError(F'''Unable to understand extra arguments {args}''')

							return super().__call__(__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a=None				,		__a="This example is {}.")       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self._args_parser(__a				,		__a				,		__a)

							for i, (candidate_label, sequence_pair) in enumerate(zip(__a				,		__a)):
												_UpperCamelCase							 =  self._parse_and_tokenize([sequence_pair])

												yield {
												    "candidate_label": candidate_label,
												    "sequence": sequences[0],
												    "is_last": i == len(__a) - 1,
												    **model_input,
												}



		def 						UpperCAmelCase							(					self				,		__a)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  inputs['''candidate_label''']
							_UpperCamelCase							 =  inputs['''sequence''']
							_UpperCamelCase							 =  {k: inputs[k] for k in self.tokenizer.model_input_names}
							_UpperCamelCase							 =  self.model(**__a)

							_UpperCamelCase							 =  {
							    '''candidate_label''': candidate_label,
							    '''sequence''': sequence,
							    '''is_last''': inputs['''is_last'''],
							    **outputs,
							}
							return model_outputs




		def 						UpperCAmelCase							(					self				,		__a				,		__a=False)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  [outputs['''candidate_label'''] for outputs in model_outputs]
							_UpperCamelCase							 =  [outputs['''sequence'''] for outputs in model_outputs]
							_UpperCamelCase							 =  np.concatenate([output['''logits'''].numpy() for output in model_outputs])
							_UpperCamelCase							 =  logits.shape[0]
							_UpperCamelCase							 =  len(__a)
							_UpperCamelCase							 =  N // n
							_UpperCamelCase							 =  logits.reshape((num_sequences, n, -1))

							if multi_label or len(__a) == 1:
												# softmax over the entailment vs. contradiction dim for each label independently
												_UpperCamelCase							 =  self.entailment_id
												_UpperCamelCase							 =  -1 if entailment_id == 0 else 0
												_UpperCamelCase							 =  reshaped_outputs[..., [contradiction_id, entailment_id]]
												_UpperCamelCase							 =  np.exp(__a) / np.exp(__a).sum(-1				,		keepdims=__a)
												_UpperCamelCase							 =  scores[..., 1]
							else:
												# softmax the "entailment" logits over all candidate labels
												_UpperCamelCase							 =  reshaped_outputs[..., self.entailment_id]
												_UpperCamelCase							 =  np.exp(__a) / np.exp(__a).sum(-1				,		keepdims=__a)

							_UpperCamelCase							 =  list(reversed(scores[0].argsort()))
							return {
							    "sequence": sequences[0],
							    "labels": [candidate_labels[i] for i in top_inds],
							    "scores": scores[0, top_inds].tolist(),
							}






