


'''simple docstring'''

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def __snake_case      (   UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Tuple	):
 return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Tuple="attention"	):
 lowerCamelCase_  				=						lowerCamelCase_  				=						np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]	)
 lowerCamelCase_  				=						k_tmp.reshape(k_tmp.shape[0]      ,       k_tmp.shape[1] * k_tmp.shape[2]	)
 lowerCamelCase_  				=						np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]	)
 lowerCamelCase_  				=						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,       o_tmp.shape[2]	)
 lowerCamelCase_  				=						np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]	)
 lowerCamelCase_  				=						q_tmp.reshape(q_tmp.shape[0]      ,       q_tmp.shape[1] * q_tmp.shape[2]	)
 lowerCamelCase_  				=						np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]	)
 lowerCamelCase_  				=						v_tmp.reshape(v_tmp.shape[0]      ,       v_tmp.shape[1] * v_tmp.shape[2]	)
 return k, o, q, v





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Dict=False	):
 if split_mlp_wi:
  lowerCamelCase_  				=						params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
  lowerCamelCase_  				=						params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
  lowerCamelCase_  				=						(wi_a, wi_a)
 else:
  lowerCamelCase_  				=						params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

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





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Tuple	):
 return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]





def __snake_case      (   UpperCAmelCase_		:		dict      ,       *, UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		bool      ,       UpperCAmelCase_		:		bool = False	):
 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/encoder/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
  if scalable_attention:
   # convert the rel_embedding of each layer
   lowerCamelCase_  				=						tax_relpos_bias_lookup(
       UpperCAmelCase_      ,       UpperCAmelCase_      ,       "encoder"	).T

 lowerCamelCase_  				=						old["encoder/encoder_norm/scale"]

 if not scalable_attention:
  lowerCamelCase_  				=						tax_relpos_bias_lookup(
      UpperCAmelCase_      ,       0      ,       "encoder"	).T
  lowerCamelCase_  				=						tax_relpos_bias_lookup(
      UpperCAmelCase_      ,       0      ,       "decoder"	).T

 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

   if scalable_attention:
    # convert the rel_embedding of each layer
    lowerCamelCase_  				=						tax_relpos_bias_lookup(UpperCAmelCase_      ,       UpperCAmelCase_      ,       "decoder"	).T

  lowerCamelCase_  				=						old["decoder/decoder_norm/scale"]

  # 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_		:		List[str]      ,       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_		:		Dict      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Union[str, Any]	):
 lowerCamelCase_  				=						checkpoints.load_tax_checkpoint(UpperCAmelCase_	)
 lowerCamelCase_  				=						convert_tax_to_pytorch(
     UpperCAmelCase_      ,       num_layers=config.num_layers      ,       is_encoder_only=UpperCAmelCase_      ,       scalable_attention=UpperCAmelCase_	)
 lowerCamelCase_  				=						make_state_dict(UpperCAmelCase_      ,       UpperCAmelCase_	)
 model.load_state_dict(UpperCAmelCase_      ,       strict=UpperCAmelCase_	)





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		bool = False      ,       UpperCAmelCase_		:		bool = False      ,       ):
 lowerCamelCase_  				=						MTaConfig.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_  				=						UMTaEncoderModel(UpperCAmelCase_	)
 else:
  lowerCamelCase_  				=						UMTaForConditionalGeneration(UpperCAmelCase_	)

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(UpperCAmelCase_      ,       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_						:       int      			=				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
     )
     parser.add_argument(
         """--scalable_attention""",
         action="""store_true""",
         help="""Whether the model uses scaled attention (umt5 model)""",
         default=False,
     )
     a_						:       Any      			=				parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path,
         args.config_file,
         args.pytorch_dump_path,
         args.is_encoder_only,
         args.scalable_attention,
     )







'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   for model_result in results.values():
    for batch_size, sequence_length in zip(model_result["bs"]    ,						model_result["ss"]  ):
     lowerCamelCase_  				=						model_result["result"][batch_size][sequence_length]
     self.assertIsNotNone(UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sgugger/tiny-distilbert-classification"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						only_pretrain_model=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result  )
   self.check_results_dict_not_empty(results.memory_train_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result  )
   self.check_results_dict_not_empty(results.memory_train_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"patrickvonplaten/t5-tiny-random"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						configs=[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"  )  ) == 0    ,						"Cannot do xla on CPU."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						use_xla=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						inference=UpperCamelCase    ,						save_to_csv=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						inference_time_csv_file=os.path.join(UpperCamelCase    ,						"inf_time.csv"  )    ,						inference_memory_csv_file=os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )    ,						env_info_csv_file=os.path.join(UpperCamelCase    ,						"env.csv"  )    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    benchmark.run()
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_time.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"env.csv"  )  ).exists()  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"

   def _check_summary_is_not_empty(UpperCamelCase  ):
    self.assertTrue(hasattr(UpperCamelCase    ,						"sequential"  )  )
    self.assertTrue(hasattr(UpperCamelCase    ,						"cumulative"  )  )
    self.assertTrue(hasattr(UpperCamelCase    ,						"current"  )  )
    self.assertTrue(hasattr(UpperCamelCase    ,						"total"  )  )

   with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						log_filename=os.path.join(UpperCamelCase    ,						"log.txt"  )    ,						log_print=UpperCamelCase    ,						trace_memory_line_by_line=UpperCamelCase    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    lowerCamelCase_  				=						benchmark.run()
    _check_summary_is_not_empty(result.inference_summary  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"log.txt"  )  ).exists()  )



