




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


_A     =			logging.get_logger(__name__)


_A     =			{
    '''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		(  __UpperCAmelCase     ):
		__snake_case									=   'codegen'
		__snake_case									=   {
		    'max_position_embeddings': 'n_positions',
		    'hidden_size': 'n_embd',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}



		def __init__(     self,	UpperCamelCase__=5_0400,	UpperCamelCase__=2048,	UpperCamelCase__=2048,	UpperCamelCase__=4096,	UpperCamelCase__=28,	UpperCamelCase__=16,	UpperCamelCase__=64,	UpperCamelCase__=None,	UpperCamelCase__="gelu_new",	UpperCamelCase__=0.0,	UpperCamelCase__=0.0,	UpperCamelCase__=0.0,	UpperCamelCase__=1E-5,	UpperCamelCase__=0.02,	UpperCamelCase__=True,	UpperCamelCase__=5_0256,	UpperCamelCase__=5_0256,	UpperCamelCase__=False,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  vocab_size
								lowerCAmelCase_      			=  n_ctx
								lowerCAmelCase_      			=  n_positions
								lowerCAmelCase_      			=  n_embd
								lowerCAmelCase_      			=  n_layer
								lowerCAmelCase_      			=  n_head
								lowerCAmelCase_      			=  n_inner
								lowerCAmelCase_      			=  rotary_dim
								lowerCAmelCase_      			=  activation_function
								lowerCAmelCase_      			=  resid_pdrop
								lowerCAmelCase_      			=  embd_pdrop
								lowerCAmelCase_      			=  attn_pdrop
								lowerCAmelCase_      			=  layer_norm_epsilon
								lowerCAmelCase_      			=  initializer_range
								lowerCAmelCase_      			=  use_cache

								lowerCAmelCase_      			=  bos_token_id
								lowerCAmelCase_      			=  eos_token_id

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







class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__ = "default",	UpperCamelCase__ = None,	UpperCamelCase__ = False,	):







								"""simple docstring"""



								super().__init__(UpperCamelCase__,	task=UpperCamelCase__,	patching_specs=UpperCamelCase__,	use_past=UpperCamelCase__   )
								if not getattr(self._config,	'''pad_token_id''',	UpperCamelCase__   ):
														# TODO: how to do that better?
														lowerCAmelCase_      			=  0



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
								if self.use_past:
														self.fill_with_past_key_values_(UpperCamelCase__,	direction='''inputs'''   )
														lowerCAmelCase_      			=  {0: '''batch''', 1: '''past_sequence + sequence'''}
								else:
														lowerCAmelCase_      			=  {0: '''batch''', 1: '''sequence'''}

								return common_inputs



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._config.n_layer



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._config.n_head



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = -1,	UpperCamelCase__ = -1,	UpperCamelCase__ = False,	UpperCamelCase__ = None,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  super(UpperCamelCase__,	self   ).generate_dummy_inputs(
								    UpperCamelCase__,	batch_size=UpperCamelCase__,	seq_length=UpperCamelCase__,	is_pair=UpperCamelCase__,	framework=UpperCamelCase__   )

								# We need to order the input in the way they appears in the forward()
								lowerCAmelCase_      			=  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

																				lowerCAmelCase_			,					lowerCAmelCase_      			=  common_inputs['''input_ids'''].shape
																				# Not using the same length for past_key_values
																				lowerCAmelCase_      			=  seqlen + 2
																				lowerCAmelCase_      			=  (
																				    batch,
																				    self.num_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // self.num_attention_heads,
																				)
																				lowerCAmelCase_      			=  [
																				    (torch.zeros(UpperCamelCase__   ), torch.zeros(UpperCamelCase__   )) for _ in range(self.num_layers   )
																				]

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

								return ordered_inputs







		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return 13










import argparse
from collections import defaultdict

import yaml


_A     =			'''docs/source/en/_toctree.yml'''





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  defaultdict(_A  )
						for doc in model_doc:
												counts[doc["local"]] += 1
						lowerCAmelCase_      			=  [key for key, value in counts.items() if value > 1]

						lowerCAmelCase_      			=  []
						for duplicate_key in duplicates:
												lowerCAmelCase_      			=  list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}  )
												if len(_A  ) > 1:
																		raise ValueError(
																		    f"{duplicate_key} is present several times in the documentation table of content at "
																		    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																		    '''others.'''  )
												# Only add this once
												new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}  )

						# Add none duplicate-keys
						new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]  )

						# Sort
						return sorted(_A ,  key=lambda _A  : s["title"].lower()  )





def __UpperCamelCase			(       _A=False  ):
						with open(_A ,  encoding='''utf-8'''  ) as f:
												lowerCAmelCase_      			=  yaml.safe_load(f.read()  )

						# Get to the API doc
						lowerCAmelCase_      			=  0
						while content[api_idx]["title"] != "API":
												api_idx += 1
						lowerCAmelCase_      			=  content[api_idx]['''sections''']

						# Then to the model doc
						lowerCAmelCase_      			=  0
						while api_doc[model_idx]["title"] != "Models":
												model_idx += 1

						lowerCAmelCase_      			=  api_doc[model_idx]['''sections''']

						lowerCAmelCase_      			=  [(idx, section) for idx, section in enumerate(_A  ) if '''sections''' in section]
						lowerCAmelCase_      			=  False
						for idx, modality_doc in modalities_docs:
												lowerCAmelCase_      			=  modality_doc['''sections''']
												lowerCAmelCase_      			=  clean_model_doc_toc(_A  )

												if old_modality_doc != new_modality_doc:
																		lowerCAmelCase_      			=  True
																		if overwrite:
																								lowerCAmelCase_      			=  new_modality_doc

						if diff:
												if overwrite:
																		lowerCAmelCase_      			=  model_doc
																		lowerCAmelCase_      			=  api_doc
																		with open(_A ,  '''w''' ,  encoding='''utf-8'''  ) as f:
																								f.write(yaml.dump(_A ,  allow_unicode=_A  )  )
												else:
																		raise ValueError(
																		    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''  )


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						_A     =			parser.parse_args()

						check_model_doc(args.fix_and_overwrite)




