


'''simple docstring'''

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 __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Tuple="attention"		):
 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 __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any=False		):
 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 __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int		):
 return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def __snake_case					(      UpperCAmelCase_						:						dict					,				*, UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						bool		):
 lowerCamelCase_      =  traverse_util.flatten_dict(variables["target"]		)
 lowerCamelCase_      =  {"/".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
 lowerCamelCase_      =  "encoder/layers_0/mlp/wi_0/kernel" in old
 print("Split MLP:"					,				UpperCAmelCase_		)

 lowerCamelCase_      =  collections.OrderedDict()

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

 # Encoder.
 for i in range(UpperCAmelCase_		):
  # Block i, layer 0 (Self Attention).
  lowerCamelCase_      =  tax_layer_norm_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"encoder"					,				"pre_attention_layer_norm"		)
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  tax_attention_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"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(UpperCAmelCase_					,				UpperCAmelCase_					,				"encoder"					,				"pre_mlp_layer_norm"		)
  lowerCamelCase_ ,lowerCamelCase_      =  tax_mlp_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"encoder"					,				UpperCAmelCase_		)
  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(UpperCAmelCase_		):
   # Block i, layer 0 (Self Attention).
   lowerCamelCase_      =  tax_layer_norm_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				"pre_self_attention_layer_norm"		)
   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  tax_attention_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"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(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				"pre_cross_attention_layer_norm"		)
   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  tax_attention_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"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(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				"pre_mlp_layer_norm"		)
   lowerCamelCase_ ,lowerCamelCase_      =  tax_mlp_lookup(UpperCAmelCase_					,				UpperCAmelCase_					,				"decoder"					,				UpperCAmelCase_		)
   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 __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						bool		):
 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 __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  checkpoints.load_tax_checkpoint(UpperCAmelCase_		)
 lowerCamelCase_      =  convert_tax_to_pytorch(UpperCAmelCase_					,				num_layers=config.num_layers					,				is_encoder_only=UpperCAmelCase_		)
 lowerCamelCase_      =  make_state_dict(UpperCAmelCase_					,				UpperCAmelCase_		)
 model.load_state_dict(UpperCAmelCase_					,				strict=UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						bool = False		):
 lowerCamelCase_      =  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:
  lowerCamelCase_      =  TaEncoderModel(UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  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__":
     a_  :				str						= 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
     )
     a_  :				List[str]						= parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
			import torch




@require_torch
@require_optimum
@slow
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoTokenizer.from_pretrained(lowercase	)
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			tokenizer('This is me'			,  return_tensors='pt'	)

				_snake_case		=			model.to_bettertransformer()

				self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				_snake_case		=			model.generate(**lowercase	)

				_snake_case		=			model.reverse_bettertransformer()

				self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase	)

							_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

							self.assertFalse(
							    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

							_snake_case		=			model_reloaded.generate(**lowercase	)
							self.assertTrue(torch.allclose(lowercase			,  lowercase	)	)





	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase	):
										model.save_pretrained(lowercase	)

							_snake_case		=			model.reverse_bettertransformer()
							model.save_pretrained(lowercase	)