






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       Any     ,					lowercase__						:       Any="attention"       )		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
				return k, o, q, v





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       int     ,					lowercase__						:       Union[str, Any]=False       )		->    str:


				'''simple docstring'''




				if split_mlp_wi:
								lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
								lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
								lowerCAmelCase__      			=						(wi_a, wi_a)
				else:
								lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wi/kernel']

				lowerCAmelCase__      			=						params[f'{prefix}/layers_{i}/mlp/wo/kernel']
				return wi, wo





def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       str       )		->    Optional[int]:


				'''simple docstring'''




				return params[f'{prefix}/layers_{i}/{layer_name}/scale']





def 	lowerCAmelCase_		(lowercase__						:       dict     ,					*, lowercase__						:       int     ,					lowercase__						:       bool       )		->    List[str]:


				'''simple docstring'''




				lowerCAmelCase__      			=						traverse_util.flatten_dict(variables['''target''']       )
				lowerCAmelCase__      			=						{'''/'''.join(lowercase__       ): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				lowerCAmelCase__      			=						'''encoder/layers_0/mlp/wi_0/kernel''' in old
				print('''Split MLP:'''     ,					lowercase__       )

				lowerCAmelCase__      			=						collections.OrderedDict()

				# Shared embeddings.
				lowerCAmelCase__      			=						old['''token_embedder/embedding''']

				# Encoder.
				for i in range(lowercase__       ):
								# Block i, layer 0 (Self Attention).
								lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					'''pre_attention_layer_norm'''       )
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_attention_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					'''attention'''       )
								lowerCAmelCase__      			=						layer_norm
								lowerCAmelCase__      			=						k.T
								lowerCAmelCase__      			=						o.T
								lowerCAmelCase__      			=						q.T
								lowerCAmelCase__      			=						v.T

								# Block i, layer 1 (MLP).
								lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					'''pre_mlp_layer_norm'''       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_mlp_lookup(lowercase__     ,					lowercase__     ,					'''encoder'''     ,					lowercase__       )
								lowerCAmelCase__      			=						layer_norm
								if split_mlp_wi:
												lowerCAmelCase__      			=						wi[0].T
												lowerCAmelCase__      			=						wi[1].T
								else:
												lowerCAmelCase__      			=						wi.T
								lowerCAmelCase__      			=						wo.T

				lowerCAmelCase__      			=						old[
				    '''encoder/relpos_bias/rel_embedding'''
				].T
				lowerCAmelCase__      			=						old['''encoder/encoder_norm/scale''']

				if not is_encoder_only:
								# Decoder.
								for i in range(lowercase__       ):
												# Block i, layer 0 (Self Attention).
												lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''pre_self_attention_layer_norm'''       )
												lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_attention_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''self_attention'''       )
												lowerCAmelCase__      			=						layer_norm
												lowerCAmelCase__      			=						k.T
												lowerCAmelCase__      			=						o.T
												lowerCAmelCase__      			=						q.T
												lowerCAmelCase__      			=						v.T

												# Block i, layer 1 (Cross Attention).
												lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''pre_cross_attention_layer_norm'''       )
												lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_attention_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''encoder_decoder_attention'''       )
												lowerCAmelCase__      			=						layer_norm
												lowerCAmelCase__      			=						k.T
												lowerCAmelCase__      			=						o.T
												lowerCAmelCase__      			=						q.T
												lowerCAmelCase__      			=						v.T

												# Block i, layer 2 (MLP).
												lowerCAmelCase__      			=						tax_layer_norm_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					'''pre_mlp_layer_norm'''       )
												lowerCAmelCase__			,		lowerCAmelCase__      			=						tax_mlp_lookup(lowercase__     ,					lowercase__     ,					'''decoder'''     ,					lowercase__       )
												lowerCAmelCase__      			=						layer_norm
												if split_mlp_wi:
																lowerCAmelCase__      			=						wi[0].T
																lowerCAmelCase__      			=						wi[1].T
												else:
																lowerCAmelCase__      			=						wi.T
												lowerCAmelCase__      			=						wo.T

								lowerCAmelCase__      			=						old['''decoder/decoder_norm/scale''']
								lowerCAmelCase__      			=						old[
								    '''decoder/relpos_bias/rel_embedding'''
								].T

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												lowerCAmelCase__      			=						old['''decoder/logits_dense/kernel'''].T

				return new





def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       bool       )		->    Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						collections.OrderedDict([(k, torch.from_numpy(v.copy()       )) for (k, v) in converted_params.items()]       )

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								lowerCAmelCase__      			=						state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												lowerCAmelCase__      			=						state_dict['''shared.weight''']

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print('''Using shared word embeddings as lm_head.'''       )
												lowerCAmelCase__      			=						state_dict['''shared.weight''']

				return state_dict





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,					lowercase__						:       str     ,					lowercase__						:       Tuple     ,					lowercase__						:       Tuple       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						checkpoints.load_tax_checkpoint(lowercase__       )
				lowerCAmelCase__      			=						convert_tax_to_pytorch(lowercase__     ,					num_layers=config.num_layers     ,					is_encoder_only=lowercase__       )
				lowerCAmelCase__      			=						make_state_dict(lowercase__     ,					lowercase__       )
				model.load_state_dict(lowercase__     ,					strict=lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       List[str]     ,					lowercase__						:       List[str]     ,					lowercase__						:       Any     ,					lowercase__						:       bool = False       )		->    Tuple:


				'''simple docstring'''




				lowerCAmelCase__      			=						TaConfig.from_json_file(lowercase__       )
				print(f'Building PyTorch model from configuration: {config}'       )
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								lowerCAmelCase__      			=						TaEncoderModel(lowercase__       )
				else:
								lowerCAmelCase__      			=						TaForConditionalGeneration(lowercase__       )

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )

				# Save pytorch-model
				print(f'Save PyTorch model to {pytorch_dump_path}'       )
				model.save_pretrained(lowercase__       )

				# Verify that we can load the checkpoint.
				model.from_pretrained(lowercase__       )
				print('''Done'''       )


if __name__ == "__main__":
							_UpperCAmelCase			:		Dict   				=    argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
							# Required parameters
							parser.add_argument(
							    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							parser.add_argument(
							    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
							)
							_UpperCAmelCase			:		str   				=    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)













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


_UpperCAmelCase			:		Tuple   				=    logging.get_logger(__name__)


_UpperCAmelCase			:		int   				=    {
    "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  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:int		      =    'codegen'
							UpperCamelCase_		:int		      =    {
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}




							def __init__(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   str=50_400 ,							SCREAMING_SNAKE_CASE_     :   str=2_048 ,							SCREAMING_SNAKE_CASE_     :   int=2_048 ,							SCREAMING_SNAKE_CASE_     :   Any=4_096 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=28 ,							SCREAMING_SNAKE_CASE_     :   str=16 ,							SCREAMING_SNAKE_CASE_     :   str=64 ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict="gelu_new" ,							SCREAMING_SNAKE_CASE_     :   str=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=1e-5 ,							SCREAMING_SNAKE_CASE_     :   Any=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=50_256 ,							SCREAMING_SNAKE_CASE_     :   Any=50_256 ,							SCREAMING_SNAKE_CASE_     :   List[str]=False ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):
											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=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)



class  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   PretrainedConfig ,							SCREAMING_SNAKE_CASE_     :   str = "default" ,							SCREAMING_SNAKE_CASE_     :   List[PatchingSpec] = None ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							):
											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?
															lowerCAmelCase__      			=						0




							@property
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
											if self.use_past:
															self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ ,							direction='''inputs'''	)
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''sequence'''}

											return common_inputs




							@property
							def 						__snake_case					(   self     :   Dict	):
											return self._config.n_layer




							@property
							def 						__snake_case					(   self     :   Union[str, Any]	):
											return self._config.n_head




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   PreTrainedTokenizer ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							SCREAMING_SNAKE_CASE_     :   Optional[TensorType] = None ,							):
											lowerCAmelCase__      			=						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()
											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(SCREAMING_SNAKE_CASE_	), torch.zeros(SCREAMING_SNAKE_CASE_	)) 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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							dtype=SCREAMING_SNAKE_CASE_	)] ,							dim=1	)

											return ordered_inputs




							@property
							def 						__snake_case					(   self     :   Optional[int]	):
											return 13





