





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
							import torch






class        _a:







		def __init__(    self ,						__snake_case ,						__snake_case=1_4 ,						__snake_case=7 ,						__snake_case=True ,						__snake_case=True ,						__snake_case=False ,						__snake_case=True ,						__snake_case=9_9 ,						__snake_case=3_2 ,						__snake_case=4 ,						__snake_case=4 ,						__snake_case=4 ,						__snake_case=3_7 ,						__snake_case="gelu" ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=5_1_2 ,						__snake_case=0.02 ,						)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   parent
							_snake_case       :					Dict				       =   batch_size
							_snake_case       :					Any				       =   seq_length
							_snake_case       :					Optional[int]				       =   is_training
							_snake_case       :					List[Any]				       =   use_input_mask
							_snake_case       :					Tuple				       =   use_token_type_ids
							_snake_case       :					Dict				       =   use_labels
							_snake_case       :					List[Any]				       =   vocab_size
							_snake_case       :					Dict				       =   hidden_size
							_snake_case       :					Union[str, Any]				       =   rotary_dim
							_snake_case       :					Dict				       =   num_hidden_layers
							_snake_case       :					Dict				       =   num_attention_heads
							_snake_case       :					Optional[Any]				       =   intermediate_size
							_snake_case       :					Optional[int]				       =   hidden_act
							_snake_case       :					Optional[int]				       =   hidden_dropout_prob
							_snake_case       :					Optional[int]				       =   attention_probs_dropout_prob
							_snake_case       :					Optional[int]				       =   max_position_embeddings
							_snake_case       :					Union[str, Any]				       =   initializer_range
							_snake_case       :					Any				       =   None
							_snake_case       :					Tuple				       =   vocab_size - 1
							_snake_case       :					str				       =   vocab_size - 1
							_snake_case       :					int				       =   vocab_size - 1







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)

							_snake_case       :					Optional[Any]				       =   None
							if self.use_input_mask:
												_snake_case       :					List[Any]				       =   random_attention_mask([self.batch_size, self.seq_length]				)

							_snake_case       :					Tuple				       =   GPTJConfig(
							    vocab_size=self.vocab_size ,						n_embd=self.hidden_size ,						n_layer=self.num_hidden_layers ,						n_head=self.num_attention_heads ,						n_positions=self.max_position_embeddings ,						use_cache=__snake_case ,						bos_token_id=self.bos_token_id ,						eos_token_id=self.eos_token_id ,						pad_token_id=self.pad_token_id ,						rotary_dim=self.rotary_dim ,						)

							return (config, input_ids, input_mask)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.prepare_config_and_inputs()
							_snake_case       , _snake_case       , _snake_case       :					Dict				       =   config_and_inputs
							_snake_case       :					Tuple				       =   {"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   2_0
							_snake_case       :					int				       =   model_class_name(__snake_case				)

							_snake_case       :					List[Any]				       =   model.init_cache(input_ids.shape[0] ,						__snake_case				)
							_snake_case       :					Any				       =   jnp.ones((input_ids.shape[0], max_decoder_length) ,						dtype="i4"				)

							_snake_case       :					Optional[int]				       =   jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,						(input_ids.shape[0], input_ids.shape[-1] - 1)				)
							_snake_case       :					Dict				       =   model(
							    input_ids[:, :-1] ,						attention_mask=__snake_case ,						past_key_values=__snake_case ,						position_ids=__snake_case ,						)

							_snake_case       :					Union[str, Any]				       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,						dtype="i4"				)
							_snake_case       :					Any				       =   model(
							    input_ids[:, -1:] ,						attention_mask=__snake_case ,						past_key_values=outputs_cache.past_key_values ,						position_ids=__snake_case ,						)

							_snake_case       :					int				       =   model(__snake_case				)

							_snake_case       :					Union[str, Any]				       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
							self.parent.assertTrue(diff < 1E-3 ,						msg=f"""Max diff is {diff}"""				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Any				       =   2_0
							_snake_case       :					Optional[int]				       =   model_class_name(__snake_case				)

							_snake_case       :					Dict				       =   jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)] ,						axis=-1 ,						)

							_snake_case       :					Optional[int]				       =   model.init_cache(input_ids.shape[0] ,						__snake_case				)
							_snake_case       :					Optional[Any]				       =   jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,						(input_ids.shape[0], input_ids.shape[-1] - 1)				)

							_snake_case       :					Union[str, Any]				       =   model(
							    input_ids[:, :-1] ,						attention_mask=__snake_case ,						past_key_values=__snake_case ,						position_ids=__snake_case ,						)
							_snake_case       :					Dict				       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,						dtype="i4"				)
							_snake_case       :					List[str]				       =   model(
							    input_ids[:, -1:] ,						past_key_values=outputs_cache.past_key_values ,						attention_mask=__snake_case ,						position_ids=__snake_case ,						)

							_snake_case       :					Optional[Any]				       =   model(__snake_case ,						attention_mask=__snake_case				)

							_snake_case       :					Dict				       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
							self.parent.assertTrue(diff < 1E-3 ,						msg=f"""Max diff is {diff}"""				)






@require_flax
class        _a(    __A	,   __A	,   unittest.TestCase    ):
		lowerCamelCase__     :str   				=		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
		lowerCamelCase__     :Any   				=		(FlaxGPTJForCausalLM,) if is_flax_available() else ()







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       :					Any				       =   FlaxGPTJModelTester(self				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							for model_class_name in self.all_model_classes:
												_snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							for model_class_name in self.all_model_classes:
												_snake_case       , _snake_case       , _snake_case       :					List[str]				       =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    __snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)







		@tooslow
		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   GPTaTokenizer.from_pretrained("gpt2" ,						pad_token="<|endoftext|>" ,						padding_side="left"				)
							_snake_case       :					Tuple				       =   tokenizer(["Hello this is a long string", "Hey"] ,						return_tensors="np" ,						padding=__snake_case ,						truncation=__snake_case				)

							_snake_case       :					Dict				       =   FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				)
							_snake_case       :					Optional[int]				       =   False
							_snake_case       :					Any				       =   model.config.eos_token_id

							_snake_case       :					str				       =   jax.jit(model.generate				)

							_snake_case       :					Any				       =   jit_generate(
							    inputs["input_ids"] ,						attention_mask=inputs["attention_mask"] ,						pad_token_id=tokenizer.pad_token_id				).sequences

							_snake_case       :					str				       =   tokenizer.batch_decode(__snake_case ,						skip_special_tokens=__snake_case				)

							_snake_case       :					List[Any]				       =   [
							    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
							    "Hey, I'm a little late to the party. I'm going to",
							]

							self.assertListEqual(__snake_case ,						__snake_case				)







		@is_pt_flax_cross_test
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       , _snake_case       :					Optional[int]				       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__				):
																	# prepare inputs
																	_snake_case       :					List[Any]				       =   self._prepare_for_class(__snake_case ,						__snake_case				)
																	_snake_case       :					Union[str, Any]				       =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																	# load corresponding PyTorch class
																	_snake_case       :					List[str]				       =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	_snake_case       :					List[Any]				       =   getattr(__snake_case ,						__snake_case				)

																	_snake_case       , _snake_case       :					int				       =   pt_inputs["input_ids"].shape
																	_snake_case       :					Optional[int]				       =   np.random.randint(0 ,						seq_length - 1 ,						size=(batch_size,)				)
																	for batch_idx, start_index in enumerate(__snake_case				):
																						_snake_case       :					Union[str, Any]				       =   0
																						_snake_case       :					List[Any]				       =   1
																						_snake_case       :					List[str]				       =   0
																						_snake_case       :					Tuple				       =   1
																	_snake_case       :					Tuple				       =   pt_model_class(__snake_case				).eval()
																	_snake_case       :					List[Any]				       =   model_class(__snake_case ,						dtype=jnp.floataa				)

																	_snake_case       :					Optional[Any]				       =   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,						__snake_case				)
																	_snake_case       :					Optional[int]				       =   fx_state

																	with torch.no_grad():
																						_snake_case       :					str				       =   pt_model(**__snake_case				).to_tuple()

																	_snake_case       :					Any				       =   fx_model(**__snake_case				).to_tuple()
																	self.assertEqual(len(__snake_case				) ,						len(__snake_case				) ,						"Output lengths differ between Flax and PyTorch"				)
																	for fx_output, pt_output in zip(__snake_case ,						__snake_case				):
																						self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4E-2				)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						pt_model.save_pretrained(__snake_case				)
																						_snake_case       :					List[Any]				       =   model_class.from_pretrained(__snake_case ,						from_pt=__snake_case				)

																	_snake_case       :					Union[str, Any]				       =   fx_model_loaded(**__snake_case				).to_tuple()
																	self.assertEqual(
																	    len(__snake_case				) ,						len(__snake_case				) ,						"Output lengths differ between Flax and PyTorch"				)
																	for fx_output_loaded, pt_output in zip(__snake_case ,						__snake_case				):
																						self.assert_almost_equals(fx_output_loaded[:, -1] ,						pt_output[:, -1].numpy() ,						4E-2				)







		@is_pt_flax_cross_test
		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       , _snake_case       :					List[Any]				       =   self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__				):
																	# prepare inputs
																	_snake_case       :					Optional[Any]				       =   self._prepare_for_class(__snake_case ,						__snake_case				)
																	_snake_case       :					Optional[int]				       =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																	# load corresponding PyTorch class
																	_snake_case       :					List[str]				       =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	_snake_case       :					Any				       =   getattr(__snake_case ,						__snake_case				)

																	_snake_case       :					List[str]				       =   pt_model_class(__snake_case				).eval()
																	_snake_case       :					int				       =   model_class(__snake_case ,						dtype=jnp.floataa				)

																	_snake_case       :					Union[str, Any]				       =   load_flax_weights_in_pytorch_model(__snake_case ,						fx_model.params				)
																	_snake_case       , _snake_case       :					Dict				       =   pt_inputs["input_ids"].shape
																	_snake_case       :					Optional[int]				       =   np.random.randint(0 ,						seq_length - 1 ,						size=(batch_size,)				)
																	for batch_idx, start_index in enumerate(__snake_case				):
																						_snake_case       :					List[str]				       =   0
																						_snake_case       :					str				       =   1
																						_snake_case       :					Union[str, Any]				       =   0
																						_snake_case       :					Dict				       =   1

																	# make sure weights are tied in PyTorch
																	pt_model.tie_weights()

																	with torch.no_grad():
																						_snake_case       :					Tuple				       =   pt_model(**__snake_case				).to_tuple()

																	_snake_case       :					int				       =   fx_model(**__snake_case				).to_tuple()
																	self.assertEqual(len(__snake_case				) ,						len(__snake_case				) ,						"Output lengths differ between Flax and PyTorch"				)
																	for fx_output, pt_output in zip(__snake_case ,						__snake_case				):
																						self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4E-2				)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						fx_model.save_pretrained(__snake_case				)
																						_snake_case       :					Optional[int]				       =   pt_model_class.from_pretrained(__snake_case ,						from_flax=__snake_case				)

																	with torch.no_grad():
																						_snake_case       :					Optional[int]				       =   pt_model_loaded(**__snake_case				).to_tuple()

																	self.assertEqual(
																	    len(__snake_case				) ,						len(__snake_case				) ,						"Output lengths differ between Flax and PyTorch"				)
																	for fx_output, pt_output in zip(__snake_case ,						__snake_case				):
																						self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4E-2				)







		@tooslow
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							for model_class_name in self.all_model_classes:
												_snake_case       :					Any				       =   model_class_name.from_pretrained("EleutherAI/gpt-j-6B"				)
												_snake_case       :					List[str]				       =   model(np.ones((1, 1)				)				)
												self.assertIsNotNone(__snake_case				)






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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="attention"				):
					_snake_case       :					Any				       =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					_snake_case       :					str				       =   params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					if split_mlp_wi:
										_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										_snake_case       :					Union[str, Any]				       =   (wi_a, wi_a)
					else:
										_snake_case       :					Optional[int]				       =   params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

					_snake_case       :					List[str]				       =   params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def    A				(							UpperCAmelCase  ,  *, UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   traverse_util.flatten_dict(variables["target"]				)
					_snake_case       :					str				       =   {"/".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
					_snake_case       :					Tuple				       =   "encoder/layers_0/mlp/wi_0/kernel" in old
					print("Split MLP:"  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   collections.OrderedDict()

					# Shared embeddings.
					_snake_case       :					Tuple				       =   old["token_embedder/embedding"]

					# Encoder.
					for i in range(UpperCAmelCase				):
										# Block i, layer 0 (Self Attention).
										_snake_case       :					Optional[Any]				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_attention_layer_norm"				)
										_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "attention"				)
										_snake_case       :					Optional[Any]				       =   layer_norm
										_snake_case       :					int				       =   k.T
										_snake_case       :					Optional[int]				       =   o.T
										_snake_case       :					Dict				       =   q.T
										_snake_case       :					Any				       =   v.T

										# Block i, layer 1 (MLP).
										_snake_case       :					Any				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_mlp_layer_norm"				)
										_snake_case       , _snake_case       :					Tuple				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  UpperCAmelCase				)
										_snake_case       :					Any				       =   layer_norm
										if split_mlp_wi:
															_snake_case       :					Union[str, Any]				       =   wi[0].T
															_snake_case       :					List[str]				       =   wi[1].T
										else:
															_snake_case       :					List[Any]				       =   wi.T
										_snake_case       :					Dict				       =   wo.T

					_snake_case       :					Dict				       =   old[
					    "encoder/relpos_bias/rel_embedding"
					].T
					_snake_case       :					Dict				       =   old["encoder/encoder_norm/scale"]

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

															# Block i, layer 1 (Cross Attention).
															_snake_case       :					Tuple				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_cross_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "encoder_decoder_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					Union[str, Any]				       =   k.T
															_snake_case       :					str				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 2 (MLP).
															_snake_case       :					Dict				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_mlp_layer_norm"				)
															_snake_case       , _snake_case       :					Union[str, Any]				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  UpperCAmelCase				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															if split_mlp_wi:
																				_snake_case       :					str				       =   wi[0].T
																				_snake_case       :					Union[str, Any]				       =   wi[1].T
															else:
																				_snake_case       :					Optional[Any]				       =   wi.T
															_snake_case       :					int				       =   wo.T

										_snake_case       :					Optional[int]				       =   old["decoder/decoder_norm/scale"]
										_snake_case       :					int				       =   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:
															_snake_case       :					Tuple				       =   old["decoder/logits_dense/kernel"].T

					return new







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   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:
										_snake_case       :					Tuple				       =   state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_snake_case       :					List[Any]				       =   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."				)
															_snake_case       :					Optional[int]				       =   state_dict["shared.weight"]

					return state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   checkpoints.load_tax_checkpoint(UpperCAmelCase				)
					_snake_case       :					Dict				       =   convert_tax_to_pytorch(UpperCAmelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=UpperCAmelCase				)
					_snake_case       :					Dict				       =   make_state_dict(UpperCAmelCase  ,  UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase  ,  strict=UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False				):
					_snake_case       :					str				       =   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:
										_snake_case       :					List[Any]				       =   TaEncoderModel(UpperCAmelCase				)
					else:
										_snake_case       :					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__":
							__lowerCAmelCase :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
							)
							__lowerCAmelCase :Optional[int]     =    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)