





import copy
import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
							from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  ):
					if attention_mask is None:
										_snake_case       :					Any				       =   input_ids.ne(config.pad_token_id				)
					if decoder_attention_mask is None:
										_snake_case       :					List[Any]				       =   decoder_input_ids.ne(config.pad_token_id				)
					if head_mask is None:
										_snake_case       :					str				       =   torch.ones(config.encoder_layers  ,  config.encoder_attention_heads  ,  device=snake_case_				)
					if decoder_head_mask is None:
										_snake_case       :					Optional[int]				       =   torch.ones(config.decoder_layers  ,  config.decoder_attention_heads  ,  device=snake_case_				)
					if cross_attn_head_mask is None:
										_snake_case       :					Union[str, Any]				       =   torch.ones(config.decoder_layers  ,  config.decoder_attention_heads  ,  device=snake_case_				)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}






class        _a:







		def __init__(    self ,						__snake_case ,						__snake_case=1_3 ,						__snake_case=7 ,						__snake_case=True ,						__snake_case=False ,						__snake_case=9_9 ,						__snake_case=1_6 ,						__snake_case=2 ,						__snake_case=4 ,						__snake_case=4 ,						__snake_case="relu" ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=0.0 ,						__snake_case=0.0 ,						__snake_case=2_0 ,						__snake_case=2 ,						__snake_case=1 ,						__snake_case=0 ,						)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   parent
							_snake_case       :					List[str]				       =   batch_size
							_snake_case       :					Union[str, Any]				       =   seq_length
							_snake_case       :					Optional[Any]				       =   is_training
							_snake_case       :					List[str]				       =   use_labels
							_snake_case       :					Tuple				       =   vocab_size
							_snake_case       :					Optional[Any]				       =   hidden_size
							_snake_case       :					Union[str, Any]				       =   num_hidden_layers
							_snake_case       :					List[Any]				       =   num_attention_heads
							_snake_case       :					Tuple				       =   intermediate_size
							_snake_case       :					str				       =   hidden_act
							_snake_case       :					Optional[Any]				       =   hidden_dropout_prob
							_snake_case       :					int				       =   attention_probs_dropout_prob
							_snake_case       :					int				       =   encoder_layerdrop
							_snake_case       :					Tuple				       =   decoder_layerdrop
							_snake_case       :					List[str]				       =   max_position_embeddings
							_snake_case       :					Tuple				       =   eos_token_id
							_snake_case       :					Dict				       =   pad_token_id
							_snake_case       :					str				       =   bos_token_id







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)
							_snake_case       :					Union[str, Any]				       =   self.eos_token_id  # Eos Token
							_snake_case       :					str				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for M2M100 the position_ids are prepared such that
							# all pad tokens have pos id = 2 and rest are between 2..seq_length
							# and the seq_length here is seq_length - num_pad_tokens
							# but when using past, there is no way of knowing if the past input ids had
							# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
							# position_ids being off by num_pad_tokens in past input
							_snake_case       :					int				       =   input_ids.clamp(self.pad_token_id + 1				)
							_snake_case       :					Optional[Any]				       =   decoder_input_ids.clamp(self.pad_token_id + 1				)

							_snake_case       :					Union[str, Any]				       =   self.get_config()
							_snake_case       :					Union[str, Any]				       =   prepare_mam_aaa_inputs_dict(__A ,						__A ,						__A				)
							return config, inputs_dict







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							return MaMaaaConfig(
							    vocab_size=self.vocab_size ,						d_model=self.hidden_size ,						encoder_layers=self.num_hidden_layers ,						decoder_layers=self.num_hidden_layers ,						encoder_attention_heads=self.num_attention_heads ,						decoder_attention_heads=self.num_attention_heads ,						encoder_ffn_dim=self.intermediate_size ,						decoder_ffn_dim=self.intermediate_size ,						dropout=self.hidden_dropout_prob ,						attention_dropout=self.attention_probs_dropout_prob ,						encoder_layerdrop=self.encoder_layerdrop ,						decoder_layerdrop=self.decoder_layerdrop ,						max_position_embeddings=self.max_position_embeddings ,						eos_token_id=self.eos_token_id ,						bos_token_id=self.bos_token_id ,						pad_token_id=self.pad_token_id ,						)







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


							'''simple docstring'''

							_snake_case       :					Any				       =   self.prepare_config_and_inputs()
							return config, inputs_dict







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


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   MaMaaaModel(config=__A				).get_decoder().to(__A				).eval()
							_snake_case       :					List[Any]				       =   inputs_dict["input_ids"]
							_snake_case       :					Optional[Any]				       =   inputs_dict["attention_mask"]
							_snake_case       :					Union[str, Any]				       =   inputs_dict["head_mask"]

							# first forward pass
							_snake_case       :					Dict				       =   model(__A ,						attention_mask=__A ,						head_mask=__A ,						use_cache=__A				)

							_snake_case       :					Dict				       =   outputs.to_tuple()

							# create hypothetical multiple next token and extent to next_input_ids
							_snake_case       :					int				       =   ids_tensor((self.batch_size, 3) ,						config.vocab_size				)
							_snake_case       :					List[str]				       =   ids_tensor((self.batch_size, 3) ,						2				)

							# append to next input_ids and
							_snake_case       :					Union[str, Any]				       =   torch.cat([input_ids, next_tokens] ,						dim=-1				)
							_snake_case       :					List[Any]				       =   torch.cat([attention_mask, next_attn_mask] ,						dim=-1				)

							_snake_case       :					Tuple				       =   model(__A ,						attention_mask=__A				)["last_hidden_state"]
							_snake_case       :					Tuple				       =   model(__A ,						attention_mask=__A ,						past_key_values=__A				)[
							    "last_hidden_state"
							]

							# select random slice
							_snake_case       :					Optional[Any]				       =   ids_tensor((1,) ,						output_from_past.shape[-1]				).item()
							_snake_case       :					Optional[Any]				       =   output_from_no_past[:, -3:, random_slice_idx].detach()
							_snake_case       :					Any				       =   output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(__A ,						__A ,						atol=1E-2				)				)







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


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   MaMaaaModel(config=__A				).to(__A				).eval()
							_snake_case       :					Union[str, Any]				       =   model(**__A				)

							_snake_case       :					Tuple				       =   outputs.encoder_last_hidden_state
							_snake_case       :					Union[str, Any]				       =   outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
												_snake_case       :					Dict				       =   model.get_encoder()
												encoder.save_pretrained(__A				)
												_snake_case       :					Any				       =   MaMaaaEncoder.from_pretrained(__A				).to(__A				)

							_snake_case       :					List[str]				       =   encoder(inputs_dict["input_ids"] ,						attention_mask=inputs_dict["attention_mask"]				)[
							    0
							]

							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												_snake_case       :					Dict				       =   model.get_decoder()
												decoder.save_pretrained(__A				)
												_snake_case       :					Optional[Any]				       =   MaMaaaDecoder.from_pretrained(__A				).to(__A				)

							_snake_case       :					List[str]				       =   decoder(
							    input_ids=inputs_dict["decoder_input_ids"] ,						attention_mask=inputs_dict["decoder_attention_mask"] ,						encoder_hidden_states=__A ,						encoder_attention_mask=inputs_dict["attention_mask"] ,						)[0]

							self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3				)






@require_torch
class        _a(    UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_	,   unittest.TestCase    ):
		lowerCamelCase__     :List[str]   				=		(
		    (
		        MaMaaaModel,
		        MaMaaaForConditionalGeneration,
		    )
		    if is_torch_available()
		    else ()
		)
		lowerCamelCase__     :Any   				=		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
		lowerCamelCase__     :Tuple   				=		(
		    {
		        'conversational': MaMaaaForConditionalGeneration,
		        'feature-extraction': MaMaaaModel,
		        'summarization': MaMaaaForConditionalGeneration,
		        'text2text-generation': MaMaaaForConditionalGeneration,
		        'translation': MaMaaaForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		lowerCamelCase__     :Optional[int]   				=		True
		lowerCamelCase__     :str   				=		True
		lowerCamelCase__     :str   				=		False
		lowerCamelCase__     :Union[str, Any]   				=		False







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


							'''simple docstring'''

							if pipeline_test_casse_name == "TranslationPipelineTests":
												# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
												# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
												return True

							return False







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Any				       =   MaMaaaModelTester(self				)
							_snake_case       :					Dict				       =   ConfigTester(self ,						config_class=__A				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							self.config_tester.run_common_tests()







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


							'''simple docstring'''

							_snake_case       :					int				       =   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												_snake_case       :					int				       =   model_class(__A				)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(__A				)
																	_snake_case       :					Optional[int]				       =   model_class.from_pretrained(__A ,						output_loading_info=__A				)
												self.assertEqual(info["missing_keys"] ,						[]				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A				)







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


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*__A				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
												_snake_case       :					str				       =   model_class(__A				)
												model.to(__A				)
												model.eval()

												_snake_case       :					str				       =   copy.deepcopy(self._prepare_for_class(__A ,						__A				)				)

												if not self.is_encoder_decoder:
																	_snake_case       :					Optional[Any]				       =   inputs["input_ids"]
																	del inputs["input_ids"]
												else:
																	_snake_case       :					Union[str, Any]				       =   inputs["input_ids"]
																	_snake_case       :					List[str]				       =   inputs.get("decoder_input_ids" ,						__A				)
																	del inputs["input_ids"]
																	inputs.pop("decoder_input_ids" ,						__A				)

												_snake_case       :					Tuple				       =   model.get_input_embeddings()
												if not self.is_encoder_decoder:
																	_snake_case       :					List[Any]				       =   wte(__A				)
												else:
																	_snake_case       :					Any				       =   wte(__A				)
																	_snake_case       :					Optional[int]				       =   wte(__A				)

												with torch.no_grad():
																	model(**__A				)[0]







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   self.model_tester.prepare_config_and_inputs()
							_snake_case       :					Any				       =   input_dict["input_ids"]
							_snake_case       :					int				       =   input_ids.ne(1				).to(__A				)
							_snake_case       :					List[Any]				       =   MaMaaaForConditionalGeneration(__A				).eval().to(__A				)
							if torch_device == "cuda":
												model.half()
							model.generate(__A ,						attention_mask=__A				)
							model.generate(num_beams=4 ,						do_sample=__A ,						early_stopping=__A ,						num_return_sequences=3				)







def    A				(							UpperCAmelCase				):
					return torch.tensor(snake_case_  ,  dtype=torch.long  ,  device=snake_case_				)


__lowerCAmelCase :Optional[Any]     =					1E-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class        _a(    unittest.TestCase    ):







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


							'''simple docstring'''

							return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"				)







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


							'''simple docstring'''

							_snake_case       :					List[str]				       =   MaMaaaModel.from_pretrained("facebook/m2m100_418M"				).to(__A				)
							_snake_case       :					Optional[Any]				       =   _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]				)
							_snake_case       :					str				       =   _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]				)
							_snake_case       :					int				       =   prepare_mam_aaa_inputs_dict(model.config ,						__A ,						__A				)
							with torch.no_grad():
												_snake_case       :					str				       =   model(**__A				)[0]
							_snake_case       :					Tuple				       =   torch.Size((1, 1_1, 1_0_2_4)				)
							self.assertEqual(output.shape ,						__A				)
							# change to expected output here
							_snake_case       :					Optional[Any]				       =   torch.tensor(
							    [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]] ,						device=__A				)
							self.assertTrue(torch.allclose(output[:, :3, :3] ,						__A ,						atol=__A				)				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"				).to(__A				)

							# change to intended input
							_snake_case       :					Union[str, Any]				       =   _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]				)
							_snake_case       :					List[str]				       =   _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]				)
							_snake_case       :					int				       =   prepare_mam_aaa_inputs_dict(model.config ,						__A ,						__A				)
							with torch.no_grad():
												_snake_case       :					Union[str, Any]				       =   model(**__A				)[0]
							_snake_case       :					Tuple				       =   torch.Size((1, 1_1, model.config.vocab_size)				)
							self.assertEqual(output.shape ,						__A				)
							# change to expected output here
							_snake_case       :					List[str]				       =   torch.tensor(
							    [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]] ,						device=__A				)
							self.assertTrue(torch.allclose(output[:, :3, :3] ,						__A ,						atol=__A				)				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"				).to(__A				)
							_snake_case       :					List[str]				       =   MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" ,						src_lang="fr" ,						tgt_lang="en"				)

							_snake_case       :					List[Any]				       =   [
							    "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
							    "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
							    "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
							    " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
							    " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
							]

							# The below article tests that we don't add any hypotheses outside of the top n_beams
							_snake_case       :					str				       =   tokenizer(__A ,						padding=__A ,						return_tensors="pt"				)

							_snake_case       :					Tuple				       =   model.generate(
							    input_ids=dct["input_ids"].to(__A				) ,						attention_mask=dct["attention_mask"].to(__A				) ,						num_beams=5 ,						forced_bos_token_id=tokenizer.get_lang_id("en"				) ,						)

							_snake_case       :					List[str]				       =   [
							    "The NSA case highlights the total absence of intelligence debate",
							    "I think there are two levels of response from the French government.",
							    "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
							    " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
							    " communications in France.",
							]

							_snake_case       :					Dict				       =   tokenizer.batch_decode(
							    hypotheses_batch.tolist() ,						clean_up_tokenization_spaces=__A ,						skip_special_tokens=__A				)
							assert generated == expected_en






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
							)