




"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  TaConfig.from_pretrained(_lowercase						)
    UpperCamelCase										=  FlaxTaForConditionalGeneration(config=_lowercase						)
    UpperCamelCase										=  checkpoints.load_tax_checkpoint(_lowercase						)

    UpperCamelCase										=  '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

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

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

        # Layer Normalization
        UpperCamelCase										=  tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

        UpperCamelCase										=  tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCamelCase										=  tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCamelCase										=  tax_attention_key
        UpperCamelCase										=  tax_attention_out
        UpperCamelCase										=  tax_attention_query
        UpperCamelCase										=  tax_attention_value

        UpperCamelCase										=  tax_attention_layer_norm

        if split_mlp_wi:
            UpperCamelCase										=  tax_mlp_wi_a
            UpperCamelCase										=  tax_mlp_wi_a
        else:
            UpperCamelCase										=  tax_mlp_wi

        UpperCamelCase										=  tax_mlp_wo
        UpperCamelCase										=  tax_mlp_layer_norm

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

    # Assigning
    UpperCamelCase										=  tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    UpperCamelCase										=  tax_encoder_norm

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

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

        # Layer Normalization
        UpperCamelCase										=  tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

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

        # Layer Normalization
        UpperCamelCase										=  tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

        UpperCamelCase										=  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCamelCase										=  tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCamelCase										=  tax_attention_key
        UpperCamelCase										=  tax_attention_out
        UpperCamelCase										=  tax_attention_query
        UpperCamelCase										=  tax_attention_value

        UpperCamelCase										=  tax_pre_attention_layer_norm

        UpperCamelCase										=  tax_enc_dec_attention_key
        UpperCamelCase										=  tax_enc_dec_attention_out
        UpperCamelCase										=  tax_enc_dec_attention_query
        UpperCamelCase										=  tax_enc_dec_attention_value

        UpperCamelCase										=  tax_cross_layer_norm

        if split_mlp_wi:
            UpperCamelCase										=  tax_mlp_wi_a
            UpperCamelCase										=  tax_mlp_wi_a
        else:
            UpperCamelCase										=  tax_mlp_wi

        UpperCamelCase										=  tax_mlp_wo

        UpperCamelCase										=  txa_mlp_layer_norm

    # Decoder Normalization
    UpperCamelCase										=  tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    UpperCamelCase										=  txa_decoder_norm

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

    # Token Embeddings
    UpperCamelCase										=  tax_model['''target''']['''token_embedder''']['''embedding''']
    UpperCamelCase										=  txa_token_embeddings

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

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


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





"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.ctrl.modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
  )

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				)							->      Any:
       UpperCamelCase										=  parent
       UpperCamelCase										=  1_3
       UpperCamelCase										=  7
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  9_9
       UpperCamelCase										=  3_2
       UpperCamelCase										=  2
       UpperCamelCase										=  4
       UpperCamelCase										=  3_7
       UpperCamelCase										=  '''gelu'''
       UpperCamelCase										=  0.1
       UpperCamelCase										=  0.1
       UpperCamelCase										=  5_1_2
       UpperCamelCase										=  1_6
       UpperCamelCase										=  2
       UpperCamelCase										=  0.02
       UpperCamelCase										=  3
       UpperCamelCase										=  4
       UpperCamelCase										=  None
       UpperCamelCase										=  self.vocab_size - 1







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size)

       UpperCamelCase										=  None
       if self.use_input_mask:
           UpperCamelCase										=  random_attention_mask([self.batch_size, self.seq_length])

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

       UpperCamelCase										=  None
       if self.use_mc_token_ids:
           UpperCamelCase										=  ids_tensor([self.batch_size, self.num_choices]					,				self.seq_length)

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

       UpperCamelCase										=  CTRLConfig(
           vocab_size=self.vocab_size					,				n_embd=self.hidden_size					,				n_layer=self.num_hidden_layers					,				n_head=self.num_attention_heads					,				n_positions=self.max_position_embeddings					,				pad_token_id=self.pad_token_id					,				)

       UpperCamelCase										=  ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,				2)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				*lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  TFCTRLModel(config=lowerCamelCase_)
       UpperCamelCase										=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       UpperCamelCase										=  model(lowerCamelCase_)

       UpperCamelCase										=  [input_ids, None, input_mask]  # None is the input for 'past'
       UpperCamelCase										=  model(lowerCamelCase_)

       UpperCamelCase										=  model(lowerCamelCase_)

       self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				*lowerCamelCase_)							->      int:
       UpperCamelCase										=  TFCTRLLMHeadModel(config=lowerCamelCase_)
       UpperCamelCase										=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       UpperCamelCase										=  model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				*lowerCamelCase_)							->      List[str]:
       UpperCamelCase										=  self.num_labels
       UpperCamelCase										=  ids_tensor([self.batch_size]					,				self.type_sequence_label_size)
       UpperCamelCase										=  {
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''labels''': sequence_labels,
       }
       UpperCamelCase										=  TFCTRLForSequenceClassification(lowerCamelCase_)
       UpperCamelCase										=  model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  self.prepare_config_and_inputs()

       (
           (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  
       )										=  config_and_inputs

       UpperCamelCase										=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict

@require_tf
class 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   A_										=					(TFCTRLLMHeadModel,) if is_tf_available() else ()
   A_										=					(
       {
           '''feature-extraction''': TFCTRLModel,
           '''text-classification''': TFCTRLForSequenceClassification,
           '''text-generation''': TFCTRLLMHeadModel,
           '''zero-shot''': TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      int:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
           # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
           # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
           # config could not be created.
           return True

       return False







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase										=  TFCTRLModelTester(self)
       UpperCamelCase										=  ConfigTester(self					,				config_class=lowerCamelCase_					,				n_embd=3_7)







   def      UpperCAmelCase__ (		self)							->      int:
       self.config_tester.run_common_tests()







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_model(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_lm_head(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[str]:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase										=  [TFCTRLLMHeadModel]
       UpperCamelCase										=  [TFCTRLForSequenceClassification]

       for model_class in self.all_model_classes:
           UpperCamelCase										=  model_class(lowerCamelCase_)
           model.build()  # may be needed for the get_bias() call below
           assert isinstance(model.get_input_embeddings()					,				tf.keras.layers.Layer)

           if model_class in list_lm_models:
               UpperCamelCase										=  model.get_output_embeddings()
               assert isinstance(lowerCamelCase_					,				tf.keras.layers.Layer)
               UpperCamelCase										=  model.get_bias()
               assert isinstance(lowerCamelCase_					,				lowerCamelCase_)
               for k, v in name.items():
                   assert isinstance(lowerCamelCase_					,				tf.Variable)
           elif model_class in list_other_models_with_output_ebd:
               UpperCamelCase										=  model.get_output_embeddings()
               assert isinstance(lowerCamelCase_					,				tf.keras.layers.Layer)
               UpperCamelCase										=  model.get_bias()
               assert name is None
           else:
               UpperCamelCase										=  model.get_output_embeddings()
               assert x is None
               UpperCamelCase										=  model.get_bias()
               assert name is None







   @slow
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase										=  TFCTRLModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)

@require_tf
class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   @slow
   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  TFCTRLLMHeadModel.from_pretrained('''ctrl''')
       UpperCamelCase										=  tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]					,				dtype=tf.intaa)  # Legal the president is
       UpperCamelCase										=  [
           1_1_8_5_9,
           0,
           1_6_1_1,
           8,
           5,
           1_5_0,
           2_6_4_4_9,
           2,
           1_9,
           3_4_8,
           4_6_9,
           3,
           2_5_9_5,
           4_8,
           2_0_7_4_0,
           2_4_6_5_3_3,
           2_4_6_5_3_3,
           1_9,
           3_0,
           5,
       ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

       UpperCamelCase										=  model.generate(lowerCamelCase_					,				do_sample=lowerCamelCase_)
       self.assertListEqual(output_ids[0].numpy().tolist()					,				lowerCamelCase_)