
"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       UpperCamelCase					 =		AutoConfig.from_pretrained(UpperCAmelCase_				)
       UpperCamelCase					 =		FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCAmelCase_				)
       UpperCamelCase					 =		checkpoints.load_tax_checkpoint(UpperCAmelCase_				)

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

       if config.model_type == "t5":
              UpperCamelCase					 =		"SelfAttention"
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              UpperCamelCase					 =		"LocalSelfAttention"
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              UpperCamelCase					 =		"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				):
              UpperCamelCase					 =		F"layers_{str(UpperCAmelCase_				)}"

              # 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"]

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

              # 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					 =		flax_model.params["encoder"]["block"][str(UpperCAmelCase_				)]["layer"]
              UpperCamelCase					 =		tax_attention_key
              UpperCamelCase					 =		tax_attention_out
              UpperCamelCase					 =		tax_attention_query
              UpperCamelCase					 =		tax_attention_value

              UpperCamelCase					 =		tax_attention_layer_norm

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     UpperCamelCase					 =		tax_global_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

              UpperCamelCase					 =		flax_model_encoder_layer_block

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

       # Side/global relative position_bias + layer norm
       if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              UpperCamelCase					 =		tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
              UpperCamelCase					 =		tax_encoder_global_rel_embedding

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

       # Decoder
       for layer_index in range(config.num_layers				):
              UpperCamelCase					 =		F"layers_{str(UpperCAmelCase_				)}"

              # 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"]
              UpperCamelCase					 =		tax_enc_dec_attention_module["key"]["kernel"]
              UpperCamelCase					 =		tax_enc_dec_attention_module["out"]["kernel"]
              UpperCamelCase					 =		tax_enc_dec_attention_module["query"]["kernel"]
              UpperCamelCase					 =		tax_enc_dec_attention_module["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					 =		flax_model.params["decoder"]["block"][str(UpperCAmelCase_				)]["layer"]
              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

              UpperCamelCase					 =		flax_model_decoder_layer_block

       # 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 and LongT5 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              UpperCamelCase					 =		tax_model["target"]["decoder"]["logits_dense"]["kernel"]

       flax_model.save_pretrained(UpperCAmelCase_				)
       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 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."""
      )
      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"""


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Any   =			BarthezTokenizer
       UpperCamelCase_		:					Tuple   =			BarthezTokenizerFast
       UpperCamelCase_		:					str   =			True
       UpperCamelCase_		:					Dict   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"			)
              tokenizer.save_pretrained(self.tmpdirname			)
              tokenizer.save_pretrained(self.tmpdirname       ,      legacy_format=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"<pad>"
              UpperCamelCase					 =		1

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_			)       ,      UpperCAmelCase_			)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		list(self.get_tokenizer().get_vocab().keys()			)

              self.assertEqual(vocab_keys[0]       ,      "<s>"			)
              self.assertEqual(vocab_keys[1]       ,      "<pad>"			)
              self.assertEqual(vocab_keys[-1]       ,      "<mask>"			)
              self.assertEqual(len(UpperCAmelCase_			)       ,      101_122			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  List[str]:







              """simple docstring"""
              self.assertEqual(self.get_tokenizer().vocab_size       ,      101_122			)






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["A long paragraph for summarization.", "Another paragraph for summarization."]
              UpperCamelCase					 =		[0, 57, 3_018, 70_307, 91, 2]

              UpperCamelCase					 =		self.tokenizer(
                  UpperCAmelCase_       ,      max_length=len(UpperCAmelCase_			)       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)
              self.assertIsInstance(UpperCAmelCase_       ,      UpperCAmelCase_			)

              self.assertEqual((2, 6)       ,      batch.input_ids.shape			)
              self.assertEqual((2, 6)       ,      batch.attention_mask.shape			)
              UpperCamelCase					 =		batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Optional[int]:







              """simple docstring"""
              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer()

              UpperCamelCase					 =		"I was born in 92000, and this is falsé."

              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self.get_rust_tokenizer()
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # fmt: off
              UpperCamelCase					 =		{"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
              # fmt: on

              # moussaKam/mbarthez is a french model. So we also use french texts.
              UpperCamelCase					 =		[
                  "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                  "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                  "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                  "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                  "telles que la traduction et la synthèse de texte.",
              ]

              self.tokenizer_integration_test_util(
                  expected_encoding=UpperCAmelCase_       ,      model_name="moussaKam/mbarthez"       ,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"       ,      sequences=UpperCAmelCase_       ,      )





