"""simple docstring"""


import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
     MODEL_FOR_PRETRAINING_MAPPING,
     FunnelBaseModel,
     FunnelForMaskedLM,
     FunnelForMultipleChoice,
     FunnelForPreTraining,
     FunnelForQuestionAnswering,
     FunnelForSequenceClassification,
     FunnelForTokenClassification,
     FunnelModel,
 )






class  a__     :


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=13,					_UpperCAmelCase=7,					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase=99,					_UpperCAmelCase=[1, 1, 2],					_UpperCAmelCase=1,					_UpperCAmelCase=32,					_UpperCAmelCase=4,					_UpperCAmelCase=8,					_UpperCAmelCase=37,					_UpperCAmelCase="gelu_new",					_UpperCAmelCase=0.1,					_UpperCAmelCase=0.1,					_UpperCAmelCase=0.0,					_UpperCAmelCase=512,					_UpperCAmelCase=3,					_UpperCAmelCase=0.02,					_UpperCAmelCase=3,					_UpperCAmelCase=4,					_UpperCAmelCase=None,					_UpperCAmelCase=False,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				seq_length
        lowercase__								=				is_training
        lowercase__								=				use_input_mask
        lowercase__								=				use_token_type_ids
        lowercase__								=				use_labels
        lowercase__								=				vocab_size
        lowercase__								=				block_sizes
        lowercase__								=				num_decoder_layers
        lowercase__								=				d_model
        lowercase__								=				n_head
        lowercase__								=				d_head
        lowercase__								=				d_inner
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout
        lowercase__								=				attention_dropout
        lowercase__								=				activation_dropout
        lowercase__								=				max_position_embeddings
        lowercase__								=				type_vocab_size
        lowercase__								=				2
        lowercase__								=				num_labels
        lowercase__								=				num_choices
        lowercase__								=				scope
        lowercase__								=				initializer_std

        # Used in the tests to check the size of the first attention layer
        lowercase__								=				n_head
        # Used in the tests to check the size of the first hidden state
        lowercase__								=				self.d_model
        # Used in the tests to check the number of output hidden states/attentions
        lowercase__								=				sum(self.block_sizes	) + (0 if base else self.num_decoder_layers)
        # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
        # the last hidden state of the first block (which is the first hidden state of the decoder).
        if not base:
             lowercase__								=				self.num_hidden_layers + 2


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

        lowercase__								=				None
        if self.use_input_mask:
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					vocab_size=2	)

        lowercase__								=				None
        if self.use_token_type_ids:
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size	)

        lowercase__								=				None
        lowercase__								=				None
        lowercase__								=				None
        if self.use_labels:
             lowercase__								=				ids_tensor([self.batch_size],					self.type_sequence_label_size	)
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.num_labels	)
             lowercase__								=				ids_tensor([self.batch_size],					self.num_choices	)
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					1	)

        lowercase__								=				self.get_config()

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            fake_token_labels,
        )


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return FunnelConfig(
            vocab_size=self.vocab_size,					block_sizes=self.block_sizes,					num_decoder_layers=self.num_decoder_layers,					d_model=self.d_model,					n_head=self.n_head,					d_head=self.d_head,					d_inner=self.d_inner,					hidden_act=self.hidden_act,					hidden_dropout=self.hidden_dropout,					attention_dropout=self.attention_dropout,					activation_dropout=self.activation_dropout,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					initializer_std=self.initializer_std,					)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				FunnelModel(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)
        lowercase__								=				model(_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)
        lowercase__								=				model(_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.d_model)	)

        lowercase__								=				False
        lowercase__								=				model(_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.d_model)	)

        lowercase__								=				False
        lowercase__								=				model(_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.d_model)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				FunnelBaseModel(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)
        lowercase__								=				model(_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)
        lowercase__								=				model(_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, 2, self.d_model)	)

        lowercase__								=				False
        lowercase__								=				model(_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, 3, self.d_model)	)

        lowercase__								=				False
        lowercase__								=				model(_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, 2, self.d_model)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				self.num_labels
        lowercase__								=				FunnelForPreTraining(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				FunnelForMaskedLM(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				self.num_labels
        lowercase__								=				FunnelForSequenceClassification(_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				self.num_choices
        lowercase__								=				FunnelForMultipleChoice(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				input_ids.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
        lowercase__								=				token_type_ids.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
        lowercase__								=				input_mask.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
        lowercase__								=				model(
            _UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase,					)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				self.num_labels
        lowercase__								=				FunnelForTokenClassification(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				FunnelForQuestionAnswering(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(
            _UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					start_positions=_UpperCAmelCase,					end_positions=_UpperCAmelCase,					)
        self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    
        )								=				config_and_inputs
        lowercase__								=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_torch
class  a__     ( _a       ,       _a       ,       unittest.TestCase						):
   snake_case_				=   False
   snake_case_				=   False
   snake_case_				=   (
       (
           FunnelModel,
           FunnelForMaskedLM,
           FunnelForPreTraining,
           FunnelForQuestionAnswering,
           FunnelForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   snake_case_				=   (
       {
           "feature-extraction": (FunnelBaseModel, FunnelModel),
           "fill-mask": FunnelForMaskedLM,
           "question-answering": FunnelForQuestionAnswering,
           "text-classification": FunnelForSequenceClassification,
           "token-classification": FunnelForTokenClassification,
           "zero-shot": FunnelForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase=False	):
        '''simple docstring'''



        lowercase__								=				super()._prepare_for_class(_UpperCAmelCase,					_UpperCAmelCase,					return_labels=_UpperCAmelCase	)

        if return_labels:
             if model_class in get_values(_UpperCAmelCase	):
                  lowercase__								=				torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length),					dtype=torch.long,					device=_UpperCAmelCase	)
        return inputs_dict


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FunnelModelTester(self	)
        lowercase__								=				ConfigTester(self,					config_class=_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        self.config_tester.run_common_tests()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if hasattr(_UpperCAmelCase,					"weight"	) and module.weight is not None:
             module.weight.data.fill_(3	)
        if hasattr(_UpperCAmelCase,					"bias"	) and module.bias is not None:
             module.bias.data.fill_(3	)

        for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
             if hasattr(_UpperCAmelCase,					_UpperCAmelCase	) and getattr(_UpperCAmelCase,					_UpperCAmelCase	) is not None:
                  lowercase__								=				getattr(_UpperCAmelCase,					_UpperCAmelCase	)
                  weight.data.fill_(3	)






@require_torch
class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   False
   snake_case_				=   False
   snake_case_				=   (
       (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
   )


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FunnelModelTester(self,					base=_UpperCAmelCase	)
        lowercase__								=				ConfigTester(self,					config_class=_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        self.config_tester.run_common_tests()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_base_model(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__			,    lowercase__								=				self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__								=				True

        for model_class in self.all_model_classes:
             if model_class.__name__ == "FunnelBaseModel":
                  continue
             lowercase__								=				model_class(_UpperCAmelCase	)
             model.to(_UpperCAmelCase	)
             model.train()
             lowercase__								=				self._prepare_for_class(_UpperCAmelCase,					_UpperCAmelCase,					return_labels=_UpperCAmelCase	)
             lowercase__								=				model(**_UpperCAmelCase	).loss
             loss.backward()


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if hasattr(_UpperCAmelCase,					"weight"	) and module.weight is not None:
             module.weight.data.fill_(3	)
        if hasattr(_UpperCAmelCase,					"bias"	) and module.bias is not None:
             module.bias.data.fill_(3	)

        for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
             if hasattr(_UpperCAmelCase,					_UpperCAmelCase	) and getattr(_UpperCAmelCase,					_UpperCAmelCase	) is not None:
                  lowercase__								=				getattr(_UpperCAmelCase,					_UpperCAmelCase	)
                  weight.data.fill_(3	)






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


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				13
        lowercase__								=				7
        lowercase__								=				torch.arange(0,					batch_size * sequence_length	).long().reshape(_UpperCAmelCase,					_UpperCAmelCase	)
        lowercase__								=				[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
        lowercase__								=				torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]	)

        lowercase__								=				FunnelModel.from_pretrained("sgugger/funnel-random-tiny"	)
        lowercase__								=				model(_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)[0].abs()

        lowercase__								=				torch.tensor(2_344.8_352	)
        lowercase__								=				torch.tensor(0.8_052	)
        self.assertTrue(torch.allclose(output.sum(),					_UpperCAmelCase,					atol=1E-4	)	)
        self.assertTrue(torch.allclose(output.mean(),					_UpperCAmelCase,					atol=1E-4	)	)

        lowercase__								=				torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]	)
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)[0].abs()

        lowercase__								=				torch.tensor(2_343.8_425	)
        lowercase__								=				torch.tensor(0.8_049	)
        self.assertTrue(torch.allclose(output.sum(),					_UpperCAmelCase,					atol=1E-4	)	)
        self.assertTrue(torch.allclose(output.mean(),					_UpperCAmelCase,					atol=1E-4	)	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FunnelTokenizer.from_pretrained("huggingface/funnel-small"	)
        lowercase__								=				FunnelModel.from_pretrained("huggingface/funnel-small"	)
        lowercase__								=				tokenizer("Hello! I am the Funnel Transformer model.",					return_tensors="pt"	)
        lowercase__								=				model(**_UpperCAmelCase	)[0]

        lowercase__								=				torch.tensor(235.7_246	)
        lowercase__								=				torch.tensor(0.0_256	)
        self.assertTrue(torch.allclose(output.sum(),					_UpperCAmelCase,					atol=1E-4	)	)
        self.assertTrue(torch.allclose(output.mean(),					_UpperCAmelCase,					atol=1E-4	)	)





"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     # Encoder
     for layer_index in range(config.num_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

          # Encoder-Decoder-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
              "kernel"
          ]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

          # MLP
          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_decoder_rel_embedding

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          lowercase__								=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]

     flax_model.save_pretrained(A   )
     print("T5X Model was sucessfully converted!"   )


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




