




'''simple docstring'''


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class     SCREAMING_SNAKE_CASE						(	snake_case_       ,			unittest.TestCase							):
      __magic_name__     :					int             =				BartTokenizer
      __magic_name__     :					Union[str, Any]             =				BartTokenizerFast
      __magic_name__     :					Optional[int]             =				True
      __magic_name__     :					str             =				filter_roberta_detectors






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           super().setUp()
           a_    :       str    	=     [
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           a_    :       Dict    	=     dict(zip(lowercase__	,	range(len(lowercase__					)					)					)					)
           a_    :       List[str]    	=     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           a_    :       Dict    	=     {"""unk_token""": """<unk>"""}

           a_    :       Tuple    	=     os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["""vocab_file"""]					)
           a_    :       Dict    	=     os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["""merges_file"""]					)
           with open(self.vocab_file	,	"""w"""	,	encoding="""utf-8"""					) as fp:
                fp.write(json.dumps(lowercase__					) + """\n"""					)
           with open(self.merges_file	,	"""w"""	,	encoding="""utf-8"""					) as fp:
                fp.write("""\n""".join(lowercase__					)					)






      def        lowercase_	(   self      :     List[str]	,	**lowercase__      :     Any					):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map					)
           return self.tokenizer_class.from_pretrained(self.tmpdirname	,	**lowercase__					)






      def        lowercase_	(   self      :     List[str]	,	**lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map					)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,	**lowercase__					)






      def        lowercase_	(   self      :     int	,	lowercase__      :     List[Any]					):


           '''simple docstring'''

           return "lower newer", "lower newer"






      @cached_property
      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           return BartTokenizer.from_pretrained("""facebook/bart-large"""					)






      @cached_property
      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           return BartTokenizerFast.from_pretrained("""facebook/bart-large"""					)






      @require_torch
      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           a_    :       Tuple    	=     [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                a_    :       Tuple    	=     tokenizer(lowercase__	,	max_length=len(lowercase__					)	,	padding=lowercase__	,	return_tensors="""pt"""					)
                self.assertIsInstance(lowercase__	,	lowercase__					)

                self.assertEqual((2, 9)	,	batch.input_ids.shape					)
                self.assertEqual((2, 9)	,	batch.attention_mask.shape					)
                a_    :       str    	=     batch.input_ids.tolist()[0]
                self.assertListEqual(lowercase__	,	lowercase__					)
                # Test that special tokens are reset






      @require_torch
      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       Dict    	=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                a_    :       Optional[int]    	=     tokenizer(lowercase__	,	padding=lowercase__	,	return_tensors="""pt"""					)
                # check if input_ids are returned and no labels
                self.assertIn("""input_ids"""	,	lowercase__					)
                self.assertIn("""attention_mask"""	,	lowercase__					)
                self.assertNotIn("""labels"""	,	lowercase__					)
                self.assertNotIn("""decoder_attention_mask"""	,	lowercase__					)






      @require_torch
      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           a_    :       Dict    	=     [
               """Summary of the text.""",
               """Another summary.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                a_    :       str    	=     tokenizer(text_target=lowercase__	,	max_length=32	,	padding="""max_length"""	,	return_tensors="""pt"""					)
                self.assertEqual(32	,	targets["""input_ids"""].shape[1]					)






      @require_torch
      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                a_    :       Tuple    	=     tokenizer(
                    ["""I am a small frog""" * 1024, """I am a small frog"""]	,	padding=lowercase__	,	truncation=lowercase__	,	return_tensors="""pt"""					)
                self.assertIsInstance(lowercase__	,	lowercase__					)
                self.assertEqual(batch.input_ids.shape	,	(2, 1024)					)






      @require_torch
      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           a_    :       Dict    	=     ["""A long paragraph for summarization."""]
           a_    :       str    	=     [
               """Summary of the text.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                a_    :       Dict    	=     tokenizer(lowercase__	,	return_tensors="""pt"""					)
                a_    :       Any    	=     tokenizer(text_target=lowercase__	,	return_tensors="""pt"""					)
                a_    :       str    	=     inputs["""input_ids"""]
                a_    :       Tuple    	=     targets["""input_ids"""]
                self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
                self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
                self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
                self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           pass






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                     a_    :       Dict    	=     self.rust_tokenizer_class.from_pretrained(lowercase__	,	**lowercase__					)
                     a_    :       str    	=     self.tokenizer_class.from_pretrained(lowercase__	,	**lowercase__					)
                     a_    :       Optional[Any]    	=     """A, <mask> AllenNLP sentence."""
                     a_    :       str    	=     tokenizer_r.encode_plus(lowercase__	,	add_special_tokens=lowercase__	,	return_token_type_ids=lowercase__					)
                     a_    :       Optional[Any]    	=     tokenizer_p.encode_plus(lowercase__	,	add_special_tokens=lowercase__	,	return_token_type_ids=lowercase__					)

                     # token_type_ids should put 0 everywhere
                     self.assertEqual(sum(tokens_r["""token_type_ids"""]					)	,	sum(tokens_p["""token_type_ids"""]					)					)

                     # attention_mask should put 1 everywhere, so sum over length should be 1
                     self.assertEqual(
                         sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					)	,	sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					)	,	)

                     a_    :       Dict    	=     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
                     a_    :       Dict    	=     tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

                     # Rust correctly handles the space before the mask while python doesnt
                     self.assertSequenceEqual(tokens_p["""input_ids"""]	,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
                     self.assertSequenceEqual(tokens_r["""input_ids"""]	,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

                     self.assertSequenceEqual(
                         lowercase__	,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
                     self.assertSequenceEqual(
                         lowercase__	,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)






'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



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




     """simple docstring"""


     a_    :       Optional[int]    	=     TaConfig.from_pretrained(UpperCamelCase__							)
     a_    :       List[str]    	=     FlaxTaForConditionalGeneration(config=UpperCamelCase__							)
     a_    :       List[Any]    	=     checkpoints.load_tax_checkpoint(UpperCamelCase__							)

     a_    :       List[str]    	=     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
          a_    :       Optional[Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

          if split_mlp_wi:
               a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Union[str, Any]    	=     tax_attention_key
          a_    :       Dict    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       List[str]    	=     tax_attention_value

          a_    :       Union[str, Any]    	=     tax_attention_layer_norm

          if split_mlp_wi:
               a_    :       Tuple    	=     tax_mlp_wi_a
               a_    :       Dict    	=     tax_mlp_wi_a
          else:
               a_    :       Optional[Any]    	=     tax_mlp_wi

          a_    :       Optional[int]    	=     tax_mlp_wo
          a_    :       Tuple    	=     tax_mlp_layer_norm

     # Only for layer 0:
     a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_    :       Union[str, Any]    	=     tax_encoder_rel_embedding

     # Assigning
     a_    :       Dict    	=     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_    :       Optional[Any]    	=     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
          a_    :       int    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
              """scale"""
          ]

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

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

          # MLP
          if split_mlp_wi:
               a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Optional[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Any    	=     tax_attention_key
          a_    :       Any    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       Union[str, Any]    	=     tax_attention_value

          a_    :       Any    	=     tax_pre_attention_layer_norm

          a_    :       Optional[int]    	=     tax_enc_dec_attention_key
          a_    :       List[Any]    	=     tax_enc_dec_attention_out
          a_    :       Any    	=     tax_enc_dec_attention_query
          a_    :       Tuple    	=     tax_enc_dec_attention_value

          a_    :       List[str]    	=     tax_cross_layer_norm

          if split_mlp_wi:
               a_    :       int    	=     tax_mlp_wi_a
               a_    :       Optional[int]    	=     tax_mlp_wi_a
          else:
               a_    :       Tuple    	=     tax_mlp_wi

          a_    :       List[Any]    	=     tax_mlp_wo

          a_    :       Union[str, Any]    	=     txa_mlp_layer_norm

     # Decoder Normalization
     a_    :       str    	=     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_    :       Dict    	=     txa_decoder_norm

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

     # Token Embeddings
     a_    :       Union[str, Any]    	=     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_    :       Optional[Any]    	=     txa_token_embeddings

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

     flax_model.save_pretrained(UpperCamelCase__							)
     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_			:     str							    =     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
