






'''simple docstring'''
import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


a_					=	1E-4

if is_torch_available():
  import torch

  from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
  from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder






@require_torch
class        __SCREAMING_SNAKE_CASE :




 def __init__( self		:							List[str]   , __lowercase		:							Optional[Any]   , __lowercase		:							Optional[Any]=16   , __lowercase		:							Optional[Any]=13   , __lowercase		:							Union[str, Any]=7   , __lowercase		:							List[str]=14   , __lowercase		:							Dict=10   , __lowercase		:							Tuple=19   , __lowercase		:							Tuple=5   , __lowercase		:							Optional[Any]=4   , __lowercase		:							Any=True   , __lowercase		:							Optional[int]=16   , __lowercase		:							Tuple=2   , __lowercase		:							Optional[Any]=4   , __lowercase		:							Union[str, Any]=4   , __lowercase		:							Dict="gelu"   , __lowercase		:							Optional[int]=0.1   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							str=[1, 2, 3, 4, 5]   , __lowercase		:							Tuple=25   , __lowercase		:							Union[str, Any]=5   , )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=d_model
       SCREAMING_SNAKE_CASE__       :			List[str]													=parent
       SCREAMING_SNAKE_CASE__       :			Dict													=batch_size
       SCREAMING_SNAKE_CASE__       :			str													=prediction_length
       SCREAMING_SNAKE_CASE__       :			Tuple													=context_length
       SCREAMING_SNAKE_CASE__       :			Dict													=cardinality
       SCREAMING_SNAKE_CASE__       :			str													=num_time_features
       SCREAMING_SNAKE_CASE__       :			List[Any]													=lags_sequence
       SCREAMING_SNAKE_CASE__       :			int													=embedding_dimension
       SCREAMING_SNAKE_CASE__       :			Any													=is_training
       SCREAMING_SNAKE_CASE__       :			Dict													=hidden_size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			List[Any]													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			List[str]													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			Tuple													=hidden_act
       SCREAMING_SNAKE_CASE__       :			int													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			List[str]													=attention_probs_dropout_prob

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=context_length
       SCREAMING_SNAKE_CASE__       :			int													=prediction_length + label_length
       SCREAMING_SNAKE_CASE__       :			List[Any]													=label_length

       SCREAMING_SNAKE_CASE__       :			Any													=moving_average
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=autocorrelation_factor




 def        __magic_name__		( self		:							Optional[Any] )		->  Any:
       return AutoformerConfig(
           d_model=self.d_model   , 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   , prediction_length=self.prediction_length   , context_length=self.context_length   , label_length=self.label_length   , lags_sequence=self.lags_sequence   , num_time_features=self.num_time_features   , num_static_categorical_features=1   , cardinality=[self.cardinality]   , embedding_dimension=[self.embedding_dimension]   , moving_average=self.moving_average   , )




 def        __magic_name__		( self		:							Any   , __lowercase		:							Dict )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			str													=config.context_length + max(config.lags_sequence )

       SCREAMING_SNAKE_CASE__       :			List[str]													=ids_tensor([self.batch_size, 1]   , config.cardinality[0] )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=floats_tensor([self.batch_size, _past_length, config.num_time_features] )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=floats_tensor([self.batch_size, _past_length] )
       SCREAMING_SNAKE_CASE__       :			Tuple													=floats_tensor([self.batch_size, _past_length] ) > 0.5

       # decoder inputs
       SCREAMING_SNAKE_CASE__       :			Tuple													=floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
       SCREAMING_SNAKE_CASE__       :			str													=floats_tensor([self.batch_size, config.prediction_length] )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={
           '''past_values''': past_values,
           '''static_categorical_features''': static_categorical_features,
           '''past_time_features''': past_time_features,
           '''past_observed_mask''': past_observed_mask,
           '''future_time_features''': future_time_features,
           '''future_values''': future_values,
       }
       return inputs_dict




 def        __magic_name__		( self		:							int )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.get_config()
       SCREAMING_SNAKE_CASE__       :			List[str]													=self.prepare_autoformer_inputs_dict(__lowercase )
       return config, inputs_dict




 def        __magic_name__		( self		:							int )		->  Any:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[str]													=self.prepare_config_and_inputs()
       return config, inputs_dict




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							Union[str, Any]   , __lowercase		:							Optional[Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[str]													=AutoformerModel(config=__lowercase ).to(__lowercase ).eval()
       SCREAMING_SNAKE_CASE__       :			Any													=model(**__lowercase )

       SCREAMING_SNAKE_CASE__       :			Any													=outputs.encoder_last_hidden_state
       SCREAMING_SNAKE_CASE__       :			Tuple													=outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__       :			List[str]													=model.get_encoder()
             encoder.save_pretrained(__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[str]													=AutoformerEncoder.from_pretrained(__lowercase ).to(__lowercase )

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=model.create_network_inputs(**__lowercase )
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )

       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.cat(
           (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])   , dim=-1   , )
       SCREAMING_SNAKE_CASE__       :			int													=encoder(inputs_embeds=__lowercase )[0]
       self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )

       SCREAMING_SNAKE_CASE__       :			Tuple													=(
           torch.mean(transformer_inputs[:, : config.context_length, ...]   , dim=1 )
           .unsqueeze(1 )
           .repeat(1   , config.prediction_length   , 1 )
       )
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.zeros(
           [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]   , device=enc_input.device   , )

       SCREAMING_SNAKE_CASE__       :			Any													=torch.cat(
           (
               torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)   , dim=1 ),
               feature[:, config.context_length - config.label_length :, ...],
           )   , dim=-1   , )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.cat(
           (
               torch.cat((trend_input[:, -config.label_length :, ...], mean)   , dim=1 ),
               feature[:, config.context_length - config.label_length :, ...],
           )   , dim=-1   , )

       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=model.get_decoder()
             decoder.save_pretrained(__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=AutoformerDecoder.from_pretrained(__lowercase ).to(__lowercase )

       SCREAMING_SNAKE_CASE__       :			int													=decoder(
           trend=__lowercase   , inputs_embeds=__lowercase   , encoder_hidden_states=__lowercase   , )[0]

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






@require_torch
class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 snake_case_												=							(AutoformerForPrediction,) if is_torch_available() else ()
 snake_case_												=							{"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
 snake_case_												=							False
 snake_case_												=							False
 snake_case_												=							False
 snake_case_												=							False
 snake_case_												=							False
 snake_case_												=							False




 def        __magic_name__		( self		:							Any )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=AutoformerModelTester(self )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=ConfigTester(self   , config_class=__lowercase   , has_text_modality=__lowercase )




 def        __magic_name__		( self		:							Tuple )		->  str:
       self.config_tester.run_common_tests()




 def        __magic_name__		( self		:							Any )		->  Dict:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_class(__lowercase )

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(__lowercase )
                   SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Tuple													=model_class.from_pretrained(__lowercase   , output_loading_info=__lowercase )
             self.assertEqual(info['''missing_keys''']   , [] )




 def        __magic_name__		( self		:							Dict )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*__lowercase )




 @unittest.skip(reason='''Model has no tokens embeddings''' )
 def        __magic_name__		( self		:							Dict )		->  Union[str, Any]:
       pass




 def        __magic_name__		( self		:							Union[str, Any] )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=inspect.signature(getattr(__lowercase   , '''forward''' ) )
       # The main input is the name of the argument after `self`
       SCREAMING_SNAKE_CASE__       :			str													=list(model_signature.parameters.keys() )[1]
       self.assertEqual(AutoformerModel.main_input_name   , __lowercase )




 def        __magic_name__		( self		:							str )		->  str:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[str]													=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=model_class(__lowercase )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=inspect.signature(model.forward )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             SCREAMING_SNAKE_CASE__       :			Any													=[*signature.parameters.keys()]

             SCREAMING_SNAKE_CASE__       :			Any													=[
                 '''past_values''',
                 '''past_time_features''',
                 '''past_observed_mask''',
                 '''static_categorical_features''',
                 '''static_real_features''',
                 '''future_values''',
                 '''future_time_features''',
             ]

             if model.__class__.__name__ in ["AutoformerForPrediction"]:
                   expected_arg_names.append('''future_observed_mask''' )

             expected_arg_names.extend(
                 [
                     '''decoder_attention_mask''',
                     '''head_mask''',
                     '''decoder_head_mask''',
                     '''cross_attn_head_mask''',
                     '''encoder_outputs''',
                     '''past_key_values''',
                     '''output_hidden_states''',
                     '''output_attentions''',
                     '''use_cache''',
                     '''return_dict''',
                 ] )

             self.assertListEqual(arg_names[: len(__lowercase )]   , __lowercase )




 def        __magic_name__		( self		:							Tuple )		->  List[str]:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			int													=self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=True

       SCREAMING_SNAKE_CASE__       :			str													=getattr(self.model_tester   , '''seq_length'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			Tuple													=getattr(self.model_tester   , '''decoder_seq_length'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			str													=getattr(self.model_tester   , '''encoder_seq_length'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=getattr(self.model_tester   , '''d_model'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=getattr(self.model_tester   , '''num_attention_heads'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=d_model // num_attention_heads

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=True
             SCREAMING_SNAKE_CASE__       :			Any													=False
             SCREAMING_SNAKE_CASE__       :			List[Any]													=True
             SCREAMING_SNAKE_CASE__       :			Dict													=model_class(__lowercase )
             model.to(__lowercase )
             model.eval()
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE__       :			Dict													=model(**self._prepare_for_class(__lowercase   , __lowercase ) )
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(__lowercase )   , self.model_tester.num_hidden_layers )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             SCREAMING_SNAKE_CASE__       :			int													=True
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_class(__lowercase )
             model.to(__lowercase )
             model.eval()
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE__       :			str													=model(**self._prepare_for_class(__lowercase   , __lowercase ) )
             SCREAMING_SNAKE_CASE__       :			Dict													=outputs.encoder_attentions
             self.assertEqual(len(__lowercase )   , self.model_tester.num_hidden_layers )

             self.assertListEqual(
                 list(attentions[0].shape[-3:] )   , [self.model_tester.num_attention_heads, encoder_seq_length, dim]   , )
             SCREAMING_SNAKE_CASE__       :			Any													=len(__lowercase )

             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=7

             if "last_hidden_state" in outputs:
                   correct_outlen += 1

             if "trend" in outputs:
                   correct_outlen += 1

             if "past_key_values" in outputs:
                   correct_outlen += 1  # past_key_values have been returned

             if "loss" in outputs:
                   correct_outlen += 1

             if "params" in outputs:
                   correct_outlen += 1

             self.assertEqual(__lowercase   , __lowercase )

             # decoder attentions
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=outputs.decoder_attentions
             self.assertIsInstance(__lowercase   , (list, tuple) )
             self.assertEqual(len(__lowercase )   , self.model_tester.num_hidden_layers )
             self.assertListEqual(
                 list(decoder_attentions[0].shape[-3:] )   , [self.model_tester.num_attention_heads, decoder_seq_length, dim]   , )

             # cross attentions
             SCREAMING_SNAKE_CASE__       :			int													=outputs.cross_attentions
             self.assertIsInstance(__lowercase   , (list, tuple) )
             self.assertEqual(len(__lowercase )   , self.model_tester.num_hidden_layers )
             self.assertListEqual(
                 list(cross_attentions[0].shape[-3:] )   , [self.model_tester.num_attention_heads, decoder_seq_length, dim]   , )

       # Check attention is always last and order is fine
       SCREAMING_SNAKE_CASE__       :			List[Any]													=True
       SCREAMING_SNAKE_CASE__       :			Tuple													=True
       SCREAMING_SNAKE_CASE__       :			Tuple													=model_class(__lowercase )
       model.to(__lowercase )
       model.eval()
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__       :			Any													=model(**self._prepare_for_class(__lowercase   , __lowercase ) )

       self.assertEqual(out_len + 2   , len(__lowercase ) )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

       self.assertEqual(len(__lowercase )   , self.model_tester.num_hidden_layers )
       self.assertListEqual(
           list(self_attentions[0].shape[-3:] )   , [self.model_tester.num_attention_heads, encoder_seq_length, dim]   , )




 @is_flaky()
 def        __magic_name__		( self		:							Optional[int] )		->  List[str]:
       super().test_retain_grad_hidden_states_attentions()





def 	_a(					UpperCamelCase__			:  List[Any]="train-batch.pt"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													=hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''',		filename=UpperCamelCase__,		repo_type='''dataset'''							)
      SCREAMING_SNAKE_CASE__       :			int													=torch.load(UpperCamelCase__,		map_location=UpperCamelCase__							)
      return batch







@require_torch
@slow
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							Tuple )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			int													=AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Tuple													=prepare_batch()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model(
                 past_values=batch['''past_values''']   , past_time_features=batch['''past_time_features''']   , past_observed_mask=batch['''past_observed_mask''']   , static_categorical_features=batch['''static_categorical_features''']   , future_values=batch['''future_values''']   , future_time_features=batch['''future_time_features''']   , )[0]

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.Size(
           (64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
       self.assertEqual(output.shape   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.tensor(
           [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]   , device=__lowercase )
       self.assertTrue(torch.allclose(output[0, :3, :3]   , __lowercase   , atol=__lowercase ) )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Tuple													=prepare_batch('''val-batch.pt''' )
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__       :			Tuple													=model(
                 past_values=batch['''past_values''']   , past_time_features=batch['''past_time_features''']   , past_observed_mask=batch['''past_observed_mask''']   , static_categorical_features=batch['''static_categorical_features''']   , ).encoder_last_hidden_state
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.Size((64, model.config.context_length, model.config.d_model) )
       self.assertEqual(output.shape   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			str													=torch.tensor(
           [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]   , device=__lowercase )
       self.assertTrue(torch.allclose(output[0, :3, :3]   , __lowercase   , atol=__lowercase ) )




 def        __magic_name__		( self		:							Any )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Dict													=AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=prepare_batch('''val-batch.pt''' )
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=model.generate(
                 static_categorical_features=batch['''static_categorical_features''']   , past_time_features=batch['''past_time_features''']   , past_values=batch['''past_values''']   , future_time_features=batch['''future_time_features''']   , past_observed_mask=batch['''past_observed_mask''']   , )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
       self.assertEqual(outputs.sequences.shape   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.tensor([3130.6763, 4056.5293, 7053.0786]   , device=__lowercase )
       SCREAMING_SNAKE_CASE__       :			str													=outputs.sequences.mean(dim=1 )
       self.assertTrue(torch.allclose(mean_prediction[0, -3:]   , __lowercase   , rtol=1e-1 ) )







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''TransientGlobalSelfAttention'''
      else:
            raise ValueError(
                '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                ''' attribute with a value from [\'local\', \'transient-global].'''							)

      # Encoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

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

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

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

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

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

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_encoder_global_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

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

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

      # Decoder Normalization
      SCREAMING_SNAKE_CASE__       :			Any													=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=txa_decoder_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      SCREAMING_SNAKE_CASE__       :			Tuple													=tax_decoder_rel_embedding

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

      # LM Head (only in v1.1 and LongT5 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
  )
  parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
  parser.add_argument(
      '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  )
  a_					=	parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)