
import torch
from torch import nn
class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			int=1       ,							SCREAMING_SNAKE_CASE_  :			int=False       )       ->     Optional[Any]:
					super().__init__()

					lowercase_       =					n_token
					lowercase_       =					d_embed
					lowercase_       =					d_proj

					lowercase_       =					cutoffs + [n_token]
					lowercase_       =					[0] + self.cutoffs
					lowercase_       =					div_val

					lowercase_       =					self.cutoffs[0]
					lowercase_       =					len(self.cutoffs       ) - 1
					lowercase_       =					self.shortlist_size + self.n_clusters

					if self.n_clusters > 0:
									lowercase_       =					nn.Parameter(torch.zeros(self.n_clusters       ,							self.d_embed       )       )
									lowercase_       =					nn.Parameter(torch.zeros(self.n_clusters       )       )

					lowercase_       =					nn.ModuleList()
					lowercase_       =					nn.ParameterList()

					if div_val == 1:
									for i in range(len(self.cutoffs       )       ):
													if d_proj != d_embed:
																	self.out_projs.append(nn.Parameter(torch.FloatTensor(_lowerCamelCase       ,							_lowerCamelCase       )       )       )
													else:
																	self.out_projs.append(_lowerCamelCase       )

									self.out_layers.append(nn.Linear(_lowerCamelCase       ,							_lowerCamelCase       )       )
					else:
									for i in range(len(self.cutoffs       )       ):
													lowercase_       =					self.cutoff_ends[i], self.cutoff_ends[i + 1]
													lowercase_       =					d_embed // (div_val**i)

													self.out_projs.append(nn.Parameter(torch.FloatTensor(_lowerCamelCase       ,							_lowerCamelCase       )       )       )

													self.out_layers.append(nn.Linear(_lowerCamelCase       ,							r_idx - l_idx       )       )

					lowercase_       =					keep_order
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     str:
					if proj is None:
									lowercase_       =					nn.functional.linear(_lowerCamelCase       ,							_lowerCamelCase       ,							bias=_lowerCamelCase       )
					else:
									# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
									lowercase_       =					nn.functional.linear(_lowerCamelCase       ,							proj.t().contiguous()       )
									lowercase_       =					nn.functional.linear(_lowerCamelCase       ,							_lowerCamelCase       ,							bias=_lowerCamelCase       )
									# else:
									#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
									#     if bias is not None:
									#         logit = logit + bias

					return logit
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			int=None       ,							SCREAMING_SNAKE_CASE_  :			List[str]=False       )       ->     int:

					if labels is not None:
									# Shift so that tokens < n predict n
									lowercase_       =					hidden[..., :-1, :].contiguous()
									lowercase_       =					labels[..., 1:].contiguous()
									lowercase_       =					hidden.view(-1       ,							hidden.size(-1       )       )
									lowercase_       =					labels.view(-1       )
									if hidden.size(0       ) != labels.size(0       ):
													raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''       )
					else:
									lowercase_       =					hidden.view(-1       ,							hidden.size(-1       )       )

					if self.n_clusters == 0:
									lowercase_       =					self._compute_logit(_lowerCamelCase       ,							self.out_layers[0].weight       ,							self.out_layers[0].bias       ,							self.out_projs[0]       )
									if labels is not None:
													lowercase_       =					labels != -1_0_0
													lowercase_       =					torch.zeros_like(_lowerCamelCase       ,							dtype=hidden.dtype       ,							device=hidden.device       )
													lowercase_       =					(
													    -nn.functional.log_softmax(_lowerCamelCase       ,							dim=-1       )[mask].gather(1       ,							labels[mask].unsqueeze(1       )       ).squeeze(1       )
													)
									else:
													lowercase_       =					nn.functional.log_softmax(_lowerCamelCase       ,							dim=-1       )
					else:
									# construct weights and biases
									lowercase_       =					[], []
									for i in range(len(self.cutoffs       )       ):
													if self.div_val == 1:
																	lowercase_       =					self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	lowercase_       =					self.out_layers[0].weight[l_idx:r_idx]
																	lowercase_       =					self.out_layers[0].bias[l_idx:r_idx]
													else:
																	lowercase_       =					self.out_layers[i].weight
																	lowercase_       =					self.out_layers[i].bias

													if i == 0:
																	lowercase_       =					torch.cat([weight_i, self.cluster_weight]       ,							dim=0       )
																	lowercase_       =					torch.cat([bias_i, self.cluster_bias]       ,							dim=0       )

													weights.append(_lowerCamelCase       )
													biases.append(_lowerCamelCase       )

									lowercase_       =					weights[0], biases[0], self.out_projs[0]

									lowercase_       =					self._compute_logit(_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       )
									lowercase_       =					nn.functional.log_softmax(_lowerCamelCase       ,							dim=1       )

									if labels is None:
													lowercase_       =					hidden.new_empty((head_logit.size(0       ), self.n_token)       )
									else:
													lowercase_       =					torch.zeros_like(_lowerCamelCase       ,							dtype=hidden.dtype       ,							device=hidden.device       )

									lowercase_       =					0
									lowercase_       =					[0] + self.cutoffs
									for i in range(len(_lowerCamelCase       ) - 1       ):
													lowercase_       =					cutoff_values[i], cutoff_values[i + 1]

													if labels is not None:
																	lowercase_       =					(labels >= l_idx) & (labels < r_idx)
																	lowercase_       =					mask_i.nonzero().squeeze()

																	if indices_i.numel() == 0:
																					continue

																	lowercase_       =					labels.index_select(0       ,							_lowerCamelCase       ) - l_idx
																	lowercase_       =					head_logprob.index_select(0       ,							_lowerCamelCase       )
																	lowercase_       =					hidden.index_select(0       ,							_lowerCamelCase       )
													else:
																	lowercase_       =					hidden

													if i == 0:
																	if labels is not None:
																					lowercase_       =					head_logprob_i.gather(1       ,							target_i[:, None]       ).squeeze(1       )
																	else:
																					lowercase_       =					head_logprob[:, : self.cutoffs[0]]
													else:
																	lowercase_       =					weights[i], biases[i], self.out_projs[i]

																	lowercase_       =					self._compute_logit(_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       )
																	lowercase_       =					nn.functional.log_softmax(_lowerCamelCase       ,							dim=1       )
																	lowercase_       =					self.cutoffs[0] + i - 1  # No probability for the head cluster
																	if labels is not None:
																					lowercase_       =					head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																					    1       ,							target_i[:, None]       ).squeeze(1       )
																	else:
																					lowercase_       =					head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																					lowercase_       =					logprob_i

													if labels is not None:
																	if (hasattr(self       ,							'''keep_order'''       ) and self.keep_order) or keep_order:
																					out.index_copy_(0       ,							_lowerCamelCase       ,							-logprob_i       )
																	else:
																					out[offset : offset + logprob_i.size(0       )].copy_(-logprob_i       )
																	offset += logprob_i.size(0       )

					return out




	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     str:
					if self.n_clusters == 0:
									lowercase_       =					self._compute_logit(_lowerCamelCase       ,							self.out_layers[0].weight       ,							self.out_layers[0].bias       ,							self.out_projs[0]       )
									return nn.functional.log_softmax(_lowerCamelCase       ,							dim=-1       )
					else:
									# construct weights and biases
									lowercase_       =					[], []
									for i in range(len(self.cutoffs       )       ):
													if self.div_val == 1:
																	lowercase_       =					self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	lowercase_       =					self.out_layers[0].weight[l_idx:r_idx]
																	lowercase_       =					self.out_layers[0].bias[l_idx:r_idx]
													else:
																	lowercase_       =					self.out_layers[i].weight
																	lowercase_       =					self.out_layers[i].bias

													if i == 0:
																	lowercase_       =					torch.cat([weight_i, self.cluster_weight]       ,							dim=0       )
																	lowercase_       =					torch.cat([bias_i, self.cluster_bias]       ,							dim=0       )

													weights.append(_lowerCamelCase       )
													biases.append(_lowerCamelCase       )

									lowercase_       =					weights[0], biases[0], self.out_projs[0]
									lowercase_       =					self._compute_logit(_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       )

									lowercase_       =					hidden.new_empty((head_logit.size(0       ), self.n_token)       )
									lowercase_       =					nn.functional.log_softmax(_lowerCamelCase       ,							dim=1       )

									lowercase_       =					[0] + self.cutoffs
									for i in range(len(_lowerCamelCase       ) - 1       ):
													lowercase_       =					cutoff_values[i], cutoff_values[i + 1]

													if i == 0:
																	lowercase_       =					head_logprob[:, : self.cutoffs[0]]
													else:
																	lowercase_       =					weights[i], biases[i], self.out_projs[i]

																	lowercase_       =					self._compute_logit(_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       )
																	lowercase_       =					nn.functional.log_softmax(_lowerCamelCase       ,							dim=1       )

																	lowercase_       =					head_logprob[:, -i] + tail_logprob_i
																	lowercase_       =					logprob_i

									return out








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_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]="attention"						):




      """simple docstring"""





      a						:Optional[int] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a						:int 							=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





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

      a						:Dict 							=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  *, UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:str 							=       traverse_util.flatten_dict(variables['''target''']						)
      a						:Any 							=       {'''/'''.join(UpperCAmelCase_						): v for k, v in old.items()}

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

      a						:Optional[Any] 							=       collections.OrderedDict()

      # Shared embeddings.
      a						:Union[str, Any] 							=       old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCAmelCase_						):
            # Block i, layer 0 (Self Attention).
            a						:Optional[Any] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_attention_layer_norm'''						)
            a							,				a							,				a							,				a						:Optional[int] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''attention'''						)
            a						:List[Any] 							=       layer_norm
            a						:str 							=       k.T
            a						:Dict 							=       o.T
            a						:int 							=       q.T
            a						:Optional[Any] 							=       v.T

            # Block i, layer 1 (MLP).
            a						:Tuple 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_mlp_layer_norm'''						)
            a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  UpperCAmelCase_						)
            a						:Any 							=       layer_norm
            if split_mlp_wi:
                  a						:Any 							=       wi[0].T
                  a						:Tuple 							=       wi[1].T
            else:
                  a						:List[str] 							=       wi.T
            a						:List[Any] 							=       wo.T

      a						:Union[str, Any] 							=       old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a						:Optional[Any] 							=       old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(UpperCAmelCase_						):
                  # Block i, layer 0 (Self Attention).
                  a						:List[str] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_self_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:List[Any] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''self_attention'''						)
                  a						:List[Any] 							=       layer_norm
                  a						:Tuple 							=       k.T
                  a						:int 							=       o.T
                  a						:Any 							=       q.T
                  a						:Optional[int] 							=       v.T

                  # Block i, layer 1 (Cross Attention).
                  a						:str 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_cross_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:Any 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''encoder_decoder_attention'''						)
                  a						:str 							=       layer_norm
                  a						:Optional[Any] 							=       k.T
                  a						:Any 							=       o.T
                  a						:Dict 							=       q.T
                  a						:Optional[Any] 							=       v.T

                  # Block i, layer 2 (MLP).
                  a						:Optional[int] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_mlp_layer_norm'''						)
                  a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  UpperCAmelCase_						)
                  a						:Optional[int] 							=       layer_norm
                  if split_mlp_wi:
                        a						:int 							=       wi[0].T
                        a						:Tuple 							=       wi[1].T
                  else:
                        a						:str 							=       wi.T
                  a						:Dict 							=       wo.T

            a						:Any 							=       old['''decoder/decoder_norm/scale''']
            a						:Optional[Any] 							=       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:
                  a						:Union[str, Any] 							=       old['''decoder/logits_dense/kernel'''].T

      return new
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:List[Any] 							=       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:
            a						:Optional[Any] 							=       state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a						:Tuple 							=       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.'''						)
                  a						:Optional[Any] 							=       state_dict['''shared.weight''']

      return state_dict
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Tuple 							=       checkpoints.load_tax_checkpoint(UpperCAmelCase_						)
      a						:Optional[int] 							=       convert_tax_to_pytorch(UpperCAmelCase_ ,  num_layers=config.num_layers ,  is_encoder_only=UpperCAmelCase_						)
      a						:Tuple 							=       make_state_dict(UpperCAmelCase_ ,  UpperCAmelCase_						)
      model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[Any] 							=       TaConfig.from_json_file(UpperCAmelCase_						)
      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:
            a						:Any 							=       TaEncoderModel(UpperCAmelCase_						)
      else:
            a						:List[str] 							=       TaForConditionalGeneration(UpperCAmelCase_						)

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

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

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


if __name__ == "__main__":
     snake_case				:    Any       				=     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
     )
     snake_case				:    Optional[Any]       				=     parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





