"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 				__a       (			_lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Tuple  				=					TaConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Any  				=					FlaxTaForConditionalGeneration(config=_lowercase						)
					lowerCamelCase__		:   List[Any]  				=					checkpoints.load_tax_checkpoint(_lowercase						)

					lowerCamelCase__		:   Any  				=					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

					# Encoder
					for layer_index in range(config.num_layers						):
										lowerCamelCase__		:   Union[str, Any]  				=					f"""layers_{str(_lowercase						)}"""

										# Self-Attention
										lowerCamelCase__		:   Union[str, Any]  				=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
										lowerCamelCase__		:   List[Any]  				=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
										lowerCamelCase__		:   Optional[int]  				=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
										lowerCamelCase__		:   int  				=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

										# Layer Normalization
										lowerCamelCase__		:   List[str]  				=					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

										if split_mlp_wi:
															lowerCamelCase__		:   Optional[int]  				=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
															lowerCamelCase__		:   int  				=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
										else:
															lowerCamelCase__		:   Dict  				=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

										lowerCamelCase__		:   Tuple  				=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

										# Layer Normalization
										lowerCamelCase__		:   Optional[int]  				=					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

										# Assigning
										lowerCamelCase__		:   Any  				=					tax_attention_key
										lowerCamelCase__		:   List[Any]  				=					tax_attention_out
										lowerCamelCase__		:   Union[str, Any]  				=					tax_attention_query
										lowerCamelCase__		:   Any  				=					tax_attention_value

										lowerCamelCase__		:   Dict  				=					tax_attention_layer_norm

										if split_mlp_wi:
															lowerCamelCase__		:   List[str]  				=					tax_mlp_wi_a
															lowerCamelCase__		:   List[Any]  				=					tax_mlp_wi_a
										else:
															lowerCamelCase__		:   List[Any]  				=					tax_mlp_wi

										lowerCamelCase__		:   Union[str, Any]  				=					tax_mlp_wo
										lowerCamelCase__		:   List[str]  				=					tax_mlp_layer_norm

					# Only for layer 0:
					lowerCamelCase__		:   Optional[Any]  				=					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
					lowerCamelCase__		:   Optional[Any]  				=					tax_encoder_rel_embedding

					# Assigning
					lowerCamelCase__		:   List[Any]  				=					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
					lowerCamelCase__		:   str  				=					tax_encoder_norm

					# Decoder
					for layer_index in range(config.num_decoder_layers						):
										lowerCamelCase__		:   Any  				=					f"""layers_{str(_lowercase						)}"""

										# Self-Attention
										lowerCamelCase__		:   Optional[Any]  				=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
										lowerCamelCase__		:   int  				=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
										lowerCamelCase__		:   Optional[Any]  				=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
										lowerCamelCase__		:   Tuple  				=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

										# Layer Normalization
										lowerCamelCase__		:   Optional[Any]  				=					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
										    '''scale'''
										]

										# Encoder-Decoder-Attention
										lowerCamelCase__		:   Tuple  				=					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
										    '''kernel'''
										]
										lowerCamelCase__		:   Optional[Any]  				=					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
										    '''kernel'''
										]
										lowerCamelCase__		:   Optional[int]  				=					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
										    '''kernel'''
										]
										lowerCamelCase__		:   List[Any]  				=					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
										    '''kernel'''
										]

										# Layer Normalization
										lowerCamelCase__		:   Tuple  				=					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

										# MLP
										if split_mlp_wi:
															lowerCamelCase__		:   str  				=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
															lowerCamelCase__		:   int  				=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
										else:
															lowerCamelCase__		:   int  				=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

										lowerCamelCase__		:   str  				=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

										# Layer Normalization
										lowerCamelCase__		:   Tuple  				=					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

										# Assigning
										lowerCamelCase__		:   Dict  				=					tax_attention_key
										lowerCamelCase__		:   int  				=					tax_attention_out
										lowerCamelCase__		:   int  				=					tax_attention_query
										lowerCamelCase__		:   List[Any]  				=					tax_attention_value

										lowerCamelCase__		:   Union[str, Any]  				=					tax_pre_attention_layer_norm

										lowerCamelCase__		:   Optional[int]  				=					tax_enc_dec_attention_key
										lowerCamelCase__		:   Optional[int]  				=					tax_enc_dec_attention_out
										lowerCamelCase__		:   Optional[Any]  				=					tax_enc_dec_attention_query
										lowerCamelCase__		:   List[str]  				=					tax_enc_dec_attention_value

										lowerCamelCase__		:   int  				=					tax_cross_layer_norm

										if split_mlp_wi:
															lowerCamelCase__		:   Tuple  				=					tax_mlp_wi_a
															lowerCamelCase__		:   Optional[int]  				=					tax_mlp_wi_a
										else:
															lowerCamelCase__		:   List[str]  				=					tax_mlp_wi

										lowerCamelCase__		:   List[str]  				=					tax_mlp_wo

										lowerCamelCase__		:   Tuple  				=					txa_mlp_layer_norm

					# Decoder Normalization
					lowerCamelCase__		:   str  				=					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
					lowerCamelCase__		:   Dict  				=					txa_decoder_norm

					# Only for layer 0:
					lowerCamelCase__		:   str  				=					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
					lowerCamelCase__		:   int  				=					tax_decoder_rel_embedding

					# Token Embeddings
					lowerCamelCase__		:   List[str]  				=					tax_model['''target''']['''token_embedder''']['''embedding''']
					lowerCamelCase__		:   Tuple  				=					txa_token_embeddings

					# LM Head (only in v1.1 checkpoints)
					if "logits_dense" in tax_model["target"]["decoder"]:
										lowerCamelCase__		:   Union[str, Any]  				=					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

					flax_model.save_pretrained(_lowercase						)
					print('''T5X Model was sucessfully converted!'''						)


if __name__ == "__main__":
					UpperCAmelCase       :    Optional[Any]									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
					)
					parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
					parser.add_argument(
					    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
					)
					UpperCAmelCase       :    Tuple									=			parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







"""simple docstring"""




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				@unittest.skip('''Model is not available.'''  )
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''nvidia/megatron-gpt2-345m/'''
									if "MYDIR" in os.environ:
														lowerCamelCase__		:   str  				=					os.path.join(os.environ['''MYDIR''']      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.half()

									lowerCamelCase__		:   Dict  				=					torch.tensor(
									    [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      ,device=__UpperCAmelCase      ,dtype=torch.long      ,)

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  ).logits

									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 9, 5_02_57)  )
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Tuple  				=					torch.tensor(
									    [
									        4.9_414,
									        -0.2_920,
									        -1.2_148,
									        -4.0_273,
									        -0.5_161,
									        -5.2_109,
									        -1.2_412,
									        -1.8_301,
									        -1.7_734,
									        -4.7_148,
									        -0.2_317,
									        -1.0_811,
									        -2.1_777,
									        0.4_141,
									        -3.7_969,
									        -4.0_586,
									        -2.5_332,
									        -3.3_809,
									        4.3_867,
									    ]      ,device=__UpperCAmelCase      ,dtype=torch.half      ,)

									for i in range(19  ):
														lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					8 * i // 17, 27_92 * i  # along the diagonal
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					output[0, r, c], expected_diag[i]
														lowerCamelCase__		:   Dict  				=					F"""row={r} col={c} computed={computed} expected={expected}"""
														self.assertAlmostEqual(__UpperCAmelCase      ,__UpperCAmelCase      ,delta=1E-4      ,msg=__UpperCAmelCase  )






