

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 					_lowercase     (				a_				:					Dict					,a_				:					List[str]					,a_				:					List[Any]					,a_				:					List[Any]="attention"				)				->	Any:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Any					,a_				:					Optional[int]					,a_				:					str					,a_				:					List[str]=False				)				->	Optional[int]:




					'''simple docstring'''







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

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




def 					_lowercase     (				a_				:					Tuple					,a_				:					str					,a_				:					Union[str, Any]					,a_				:					Dict				)				->	Optional[int]:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					dict					,*, a_				:					int					,a_				:					bool				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    traverse_util.flatten_dict(variables['target']				)
					__magic_name__							=    {'/'.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
					__magic_name__							=    'encoder/layers_0/mlp/wi_0/kernel' in old
					print('Split MLP:'					,a_				)

					__magic_name__							=    collections.OrderedDict()

					# Shared embeddings.
					__magic_name__							=    old['token_embedder/embedding']

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

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

					__magic_name__							=    old[
					    'encoder/relpos_bias/rel_embedding'
					].T
					__magic_name__							=    old['encoder/encoder_norm/scale']

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

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

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

										__magic_name__							=    old['decoder/decoder_norm/scale']
										__magic_name__							=    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:
															__magic_name__							=    old['decoder/logits_dense/kernel'].T

					return new




def 					_lowercase     (				a_				:					List[str]					,a_				:					bool				)				->	int:




					'''simple docstring'''







					__magic_name__							=    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:
										__magic_name__							=    state_dict['shared.weight']

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

					return state_dict




def 					_lowercase     (				a_				:					Dict					,a_				:					Optional[int]					,a_				:					Optional[int]					,a_				:					List[str]				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    checkpoints.load_tax_checkpoint(a_				)
					__magic_name__							=    convert_tax_to_pytorch(a_					,num_layers=config.num_layers					,is_encoder_only=a_				)
					__magic_name__							=    make_state_dict(a_					,a_				)
					model.load_state_dict(a_					,strict=a_				)




def 					_lowercase     (				a_				:					Dict					,a_				:					int					,a_				:					int					,a_				:					bool = False				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    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:
										__magic_name__							=    TaEncoderModel(a_				)
					else:
										__magic_name__							=    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__":
						A__						=							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__						=							parser.parse_args()
						convert_tax_checkpoint_to_pytorch(
						    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
						)







import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
						import jax

						from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
						from transformers.models.bert.modeling_flax_bert import FlaxBertModel
						from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxBertModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Optional[Any]      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxRobertaModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Any      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'bert-base is not a local folder and is not a valid model identifier'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('bert-base'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase  , revision='aaaaaa'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'  , ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						with self.assertRaisesRegex(__UpperCamelCase  , 'Use `from_pt=True` to load this model'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'      )




