





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__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    str="attention"  )     ->	Optional[int]:



     '''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    List[Any]=False  )     ->	List[str]:



     '''simple docstring'''




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

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





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Dict  )     ->	int:



     '''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    dict    ,      *, UpperCamelCase__     :    int    ,      UpperCamelCase__     :    bool  )     ->	List[str]:



     '''simple docstring'''




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

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

     _snake_case													=			collections.OrderedDict()

     # Shared embeddings.
     _snake_case													=			old["""token_embedder/embedding"""]

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

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

     _snake_case													=			old[
         """encoder/relpos_bias/rel_embedding"""
     ].T
     _snake_case													=			old["""encoder/encoder_norm/scale"""]

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

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

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

          _snake_case													=			old["""decoder/decoder_norm/scale"""]
          _snake_case													=			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:
               _snake_case													=			old["""decoder/logits_dense/kernel"""].T

     return new





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    bool  )     ->	int:



     '''simple docstring'''




     _snake_case													=			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:
          _snake_case													=			state_dict["""shared.weight"""]

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

     return state_dict





def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    str  )     ->	List[str]:



     '''simple docstring'''




     _snake_case													=			checkpoints.load_tax_checkpoint(A__  )
     _snake_case													=			convert_tax_to_pytorch(A__    ,      num_layers=config.num_layers    ,      is_encoder_only=A__  )
     _snake_case													=			make_state_dict(A__    ,      A__  )
     model.load_state_dict(A__    ,      strict=A__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    bool = False  )     ->	List[Any]:



     '''simple docstring'''




     _snake_case													=			TaConfig.from_json_file(A__  )
     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:
          _snake_case													=			TaEncoderModel(A__  )
     else:
          _snake_case													=			TaForConditionalGeneration(A__  )

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

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

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


if __name__ == "__main__":
     UpperCAmelCase_ 							=       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_ 							=       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, Mapping, Optional

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


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		'''gpt_neo'''
					lowerCAmelCase_							=		['''past_key_values''']
					lowerCAmelCase_							=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




					def __init__(						self			,  lowerCAmelCase_=5_0257			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=24			,  lowerCAmelCase_=[[["global", "local"], 12]]			,  lowerCAmelCase_=16			,  lowerCAmelCase_=None			,  lowerCAmelCase_=256			,  lowerCAmelCase_="gelu_new"			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.1			,  lowerCAmelCase_=1E-5			,  lowerCAmelCase_=0.02			,  lowerCAmelCase_=True			,  lowerCAmelCase_=5_0256			,  lowerCAmelCase_=5_0256			,  **lowerCAmelCase_			,  )						->							Tuple:
										_snake_case													=			vocab_size
										_snake_case													=			max_position_embeddings
										_snake_case													=			hidden_size
										_snake_case													=			num_layers
										_snake_case													=			num_heads
										_snake_case													=			intermediate_size
										_snake_case													=			window_size
										_snake_case													=			activation_function
										_snake_case													=			resid_dropout
										_snake_case													=			embed_dropout
										_snake_case													=			attention_dropout
										_snake_case													=			classifier_dropout
										_snake_case													=			layer_norm_epsilon
										_snake_case													=			initializer_range
										_snake_case													=			use_cache

										_snake_case													=			bos_token_id
										_snake_case													=			eos_token_id

										_snake_case													=			attention_types
										_snake_case													=			self.expand_attention_types_params(lowerCAmelCase_					)

										if len(self.attention_layers					) != self.num_layers:
															raise ValueError(
															    'Configuration for convolutional module is incorrect. '
															    'It is required that `len(config.attention_layers)` == `config.num_layers` '
															    F'''but is `len(config.attention_layers) = {len(self.attention_layers					)}`, '''
															    F'''`config.num_layers = {self.num_layers}`. '''
															    '`config.attention_layers` is prepared using `config.attention_types`. '
															    'Please verify the value of `config.attention_types` argument.'					)

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






					@staticmethod
					def 		lowerCAmelCase     (						lowerCAmelCase_					)						->							Any:
										_snake_case													=			[]
										for item in attention_types:
															for _ in range(item[1]					):
																				attentions.extend(item[0]					)
										return attentions





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Any:



					'''simple docstring'''




					import torch

					_snake_case													=			input.size()
					_snake_case													=			len(UpperCamelCase__  )
					_snake_case													=			shape[dimension]

					_snake_case													=			torch.arange(0    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					_snake_case													=			torch.div(sizedim - size    ,      UpperCamelCase__    ,      rounding_mode='floor'  ) + 1
					_snake_case													=			torch.arange(UpperCamelCase__  ) + low_indices[:min_length][:, None]

					_snake_case													=			[slice(UpperCamelCase__  )] * rank
					_snake_case													=			indices
					_snake_case													=			input[s]

					_snake_case													=			list(range(0    ,      rank + 1  )  )
					perm.append(perm.pop(dimension + 1  )  )

					return sliced.permute(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict  )     ->	str:



					'''simple docstring'''




					import torch

					_snake_case													=			torch.arange(1    ,      UpperCamelCase__  )
					_snake_case													=			torch.remainder(UpperCamelCase__    ,      UpperCamelCase__  )
					_snake_case													=			remainders == 0
					_snake_case													=			candidates[divisor_indices]
					_snake_case													=			torch.max(UpperCamelCase__  )
					return largest_divisor, torch.div(UpperCamelCase__    ,      UpperCamelCase__    ,      rounding_mode='floor'  )




class      UpperCamelCase_						(					_lowerCamelCase   ):




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

										return common_inputs




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return self._config.num_heads




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = None			,  )						->							Mapping[str, Any]:
										_snake_case													=			super(lowerCAmelCase_			,  self					).generate_dummy_inputs(
										    lowerCAmelCase_			,  batch_size=lowerCAmelCase_			,  seq_length=lowerCAmelCase_			,  is_pair=lowerCAmelCase_			,  framework=lowerCAmelCase_					)

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

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

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

										return ordered_inputs






					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return 13



