



"""simple docstring"""

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowercase__					:						str												=			{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class  _UpperCAmelCase (    unittest.TestCase):
 _lowerCAmelCase     :    Tuple	      =	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 _lowerCAmelCase     :    Dict	      =	TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  _lowerCAmelCase     :    Any	      =	{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  _lowerCAmelCase     :    List[Any]	      =	{
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: List[Any]						,  lowercase_	: Optional[int]						,  lowercase_	: List[str]       ):
     snake_case_				:  List[Any]       =			ZeroShotClassificationPipeline(
         model=lowercase_						,  tokenizer=lowercase_						,  candidate_labels=['''polics''', '''health''']       )
     return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: Optional[int]       ):
     snake_case_				:  Optional[Any]       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''       )
     self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

     # No kwarg
     snake_case_				:  int       =			classifier('''Who are you voting for in 2020?'''						,  ['''politics''']       )
     self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

     snake_case_				:  Union[str, Any]       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''']       )
     self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

     snake_case_				:  Union[str, Any]       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels='''politics, public health'''       )
     self.assertEqual(
         lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}       )
     self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']       )       )						,  1.0       )

     snake_case_				:  Any       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''']       )
     self.assertEqual(
         lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}       )
     self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']       )       )						,  1.0       )

     snake_case_				:  Union[str, Any]       =			classifier(
         '''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''						,  hypothesis_template='''This text is about {}'''       )
     self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

     # https://github.com/huggingface/transformers/issues/13846
     snake_case_				:  List[Any]       =			classifier(['''I am happy''']						,  ['''positive''', '''negative''']       )
     self.assertEqual(
         lowercase_						,  [
             {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}
             for i in range(1       )
         ]						,  )
     snake_case_				:  Any       =			classifier(['''I am happy''', '''I am sad''']						,  ['''positive''', '''negative''']       )
     self.assertEqual(
         lowercase_						,  [
             {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}
             for i in range(2       )
         ]						,  )

     with self.assertRaises(lowercase_       ):
         classifier(''''''						,  candidate_labels='''politics'''       )

     with self.assertRaises(lowercase_       ):
         classifier(lowercase_						,  candidate_labels='''politics'''       )

     with self.assertRaises(lowercase_       ):
         classifier('''Who are you voting for in 2020?'''						,  candidate_labels=''''''       )

     with self.assertRaises(lowercase_       ):
         classifier('''Who are you voting for in 2020?'''						,  candidate_labels=lowercase_       )

     with self.assertRaises(lowercase_       ):
         classifier(
             '''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''						,  hypothesis_template='''Not formatting template'''						,  )

     with self.assertRaises(lowercase_       ):
         classifier(
             '''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''						,  hypothesis_template=lowercase_						,  )

     self.run_entailment_id(lowercase_       )

 def 		_snake_case      (  self	: Optional[int]						,  lowercase_	: Pipeline       ):
     snake_case_				:  Tuple       =			zero_shot_classifier.model.config
     snake_case_				:  Dict       =			config.labelaid
     snake_case_				:  Union[str, Any]       =			zero_shot_classifier.entailment_id

     snake_case_				:  Optional[int]       =			{'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
     self.assertEqual(zero_shot_classifier.entailment_id						,  -1       )

     snake_case_				:  Any       =			{'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
     self.assertEqual(zero_shot_classifier.entailment_id						,  0       )

     snake_case_				:  Any       =			{'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
     self.assertEqual(zero_shot_classifier.entailment_id						,  0       )

     snake_case_				:  Optional[int]       =			{'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
     self.assertEqual(zero_shot_classifier.entailment_id						,  2       )

     snake_case_				:  Optional[int]       =			original_labelaid
     self.assertEqual(lowercase_						,  zero_shot_classifier.entailment_id       )

 @require_torch
 def 		_snake_case      (  self	: str       ):
     snake_case_				:  str       =			pipeline(
         '''zero-shot-classification'''						,  model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''						,  framework='''pt'''						,  )
     # There was a regression in 4.10 for this
     # Adding a test so we don't make the mistake again.
     # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
     zero_shot_classifier(
         '''Who are you voting for in 2020?''' * 100						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

 @require_torch
 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Union[str, Any]       =			pipeline(
         '''zero-shot-classification'''						,  model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''						,  framework='''pt'''						,  )
     snake_case_				:  int       =			zero_shot_classifier(
         '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

     self.assertEqual(
         nested_simplify(lowercase_       )						,  {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''science''', '''public health''', '''politics'''],
             '''scores''': [0.3_33, 0.3_33, 0.3_33],
         }						,  )

 @require_tf
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  Any       =			pipeline(
         '''zero-shot-classification'''						,  model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''						,  framework='''tf'''						,  )
     snake_case_				:  Optional[int]       =			zero_shot_classifier(
         '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

     self.assertEqual(
         nested_simplify(lowercase_       )						,  {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''science''', '''public health''', '''politics'''],
             '''scores''': [0.3_33, 0.3_33, 0.3_33],
         }						,  )

 @slow
 @require_torch
 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  List[str]       =			pipeline('''zero-shot-classification'''						,  model='''roberta-large-mnli'''						,  framework='''pt'''       )
     snake_case_				:  Dict       =			zero_shot_classifier(
         '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

     self.assertEqual(
         nested_simplify(lowercase_       )						,  {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''politics''', '''public health''', '''science'''],
             '''scores''': [0.9_76, 0.0_15, 0.0_09],
         }						,  )
     snake_case_				:  str       =			zero_shot_classifier(
         '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
         ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
         ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
         ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
         ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
         ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
         ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
         ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
         ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
         ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
         ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
         ''' large and limited training data.'''						,  candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']						,  multi_label=lowercase_						,  )
     self.assertEqual(
         nested_simplify(lowercase_       )						,  {
             '''sequence''': (
                 '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                 ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                 ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                 ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                 ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                 ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                 ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                 ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                 ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                 ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                 ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                 ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                 ''' limited training data.'''
             ),
             '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
             '''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
         }						,  )






 @slow
 @require_tf
 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  List[str]       =			pipeline('''zero-shot-classification'''						,  model='''roberta-large-mnli'''						,  framework='''tf'''       )
     snake_case_				:  Tuple       =			zero_shot_classifier(
         '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

     self.assertEqual(
         nested_simplify(lowercase_       )						,  {
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''politics''', '''public health''', '''science'''],
             '''scores''': [0.9_76, 0.0_15, 0.0_09],
         }						,  )
     snake_case_				:  Union[str, Any]       =			zero_shot_classifier(
         '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
         ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
         ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
         ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
         ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
         ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
         ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
         ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
         ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
         ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
         ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
         ''' large and limited training data.'''						,  candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']						,  multi_label=lowercase_						,  )
     self.assertEqual(
         nested_simplify(lowercase_       )						,  {
             '''sequence''': (
                 '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                 ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                 ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                 ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                 ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                 ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                 ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                 ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                 ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                 ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                 ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                 ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                 ''' limited training data.'''
             ),
             '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
             '''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
         }						,  )










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        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					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			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":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

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

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    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":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

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

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

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

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

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

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
     lowercase__					:						Tuple												=			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.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





