




'''simple docstring'''
from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel






@require_tf
class        SCREAMING_SNAKE_CASE:
       snake_case_ :     str    =					MarianConfig
       snake_case_ :     Any    =					{}
       snake_case_ :     Union[str, Any]    =					"""gelu"""

       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__=13					,		lowerCamelCase__=7					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=99					,		lowerCamelCase__=32					,		lowerCamelCase__=2					,		lowerCamelCase__=4					,		lowerCamelCase__=37					,		lowerCamelCase__=0.1					,		lowerCamelCase__=0.1					,		lowerCamelCase__=20					,		lowerCamelCase__=2					,		lowerCamelCase__=1					,		lowerCamelCase__=0					,		)    ->					int:

           """simple docstring"""



           __lowercase							=						parent
           __lowercase							=						batch_size
           __lowercase							=						seq_length
           __lowercase							=						is_training
           __lowercase							=						use_labels
           __lowercase							=						vocab_size
           __lowercase							=						hidden_size
           __lowercase							=						num_hidden_layers
           __lowercase							=						num_attention_heads
           __lowercase							=						intermediate_size

           __lowercase							=						hidden_dropout_prob
           __lowercase							=						attention_probs_dropout_prob
           __lowercase							=						max_position_embeddings
           __lowercase							=						eos_token_id
           __lowercase							=						pad_token_id
           __lowercase							=						bos_token_id

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						ids_tensor([self.batch_size, self.seq_length - 1]					,		self.vocab_size     )
           __lowercase							=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )					,		1     )
           __lowercase							=						tf.concat([input_ids, eos_tensor]					,		axis=1     )

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

           __lowercase							=						self.config_cls(
               vocab_size=self.vocab_size					,		d_model=self.hidden_size					,		encoder_layers=self.num_hidden_layers					,		decoder_layers=self.num_hidden_layers					,		encoder_attention_heads=self.num_attention_heads					,		decoder_attention_heads=self.num_attention_heads					,		encoder_ffn_dim=self.intermediate_size					,		decoder_ffn_dim=self.intermediate_size					,		dropout=self.hidden_dropout_prob					,		attention_dropout=self.attention_probs_dropout_prob					,		max_position_embeddings=self.max_position_embeddings					,		eos_token_ids=[2]					,		bos_token_id=self.bos_token_id					,		pad_token_id=self.pad_token_id					,		decoder_start_token_id=self.pad_token_id					,		**self.config_updates					,		)
           __lowercase							=						prepare_marian_inputs_dict(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
           return config, inputs_dict




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

           """simple docstring"""



           __lowercase							=						TFMarianModel(config=lowerCamelCase__     ).get_decoder()
           __lowercase							=						inputs_dict["""input_ids"""]

           __lowercase							=						input_ids[:1, :]
           __lowercase							=						inputs_dict["""attention_mask"""][:1, :]
           __lowercase							=						inputs_dict["""head_mask"""]
           __lowercase							=						1

           # first forward pass
           __lowercase							=						model(lowerCamelCase__					,		attention_mask=lowerCamelCase__					,		head_mask=lowerCamelCase__					,		use_cache=lowerCamelCase__     )

           __lowercase				,__lowercase							=						outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           __lowercase							=						ids_tensor((self.batch_size, 3)					,		config.vocab_size     )
           __lowercase							=						tf.cast(ids_tensor((self.batch_size, 3)					,		2     )					,		tf.inta     )

           # append to next input_ids and
           __lowercase							=						tf.concat([input_ids, next_tokens]					,		axis=-1     )
           __lowercase							=						tf.concat([attention_mask, next_attn_mask]					,		axis=-1     )

           __lowercase							=						model(lowerCamelCase__					,		attention_mask=lowerCamelCase__     )[0]
           __lowercase							=						model(lowerCamelCase__					,		attention_mask=lowerCamelCase__					,		past_key_values=lowerCamelCase__     )[0]

           self.parent.assertEqual(next_tokens.shape[1]					,		output_from_past.shape[1]     )

           # select random slice
           __lowercase							=						int(ids_tensor((1,)					,		output_from_past.shape[-1]     )     )
           __lowercase							=						output_from_no_past[:, -3:, random_slice_idx]
           __lowercase							=						output_from_past[:, :, random_slice_idx]

           # test that outputs are equal for slice
           tf.debugging.assert_near(lowerCamelCase__					,		lowerCamelCase__					,		rtol=1E-3     )

def 				snake_case_  (   a__		:	Dict    ,a__		:	int    ,a__		:	Optional[int]    ,a__		:	Dict=None    ,a__		:	Any=None    ,a__		:	Union[str, Any]=None    ,a__		:	Union[str, Any]=None    ,a__		:	str=None    ,):



    """simple docstring"""




    if attention_mask is None:
        __lowercase							=						tf.cast(tf.math.not_equal(a__    ,config.pad_token_id			)    ,tf.inta			)
    if decoder_attention_mask is None:
        __lowercase							=						tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape    ,dtype=tf.inta			),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,config.pad_token_id			)    ,tf.inta			),
            ]    ,axis=-1    ,)
    if head_mask is None:
        __lowercase							=						tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
    if decoder_head_mask is None:
        __lowercase							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
    if cross_attn_head_mask is None:
        __lowercase							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }






@require_tf
class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      unittest.TestCase     ):
       snake_case_ :     List[str]    =					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
       snake_case_ :     Optional[Any]    =					(TFMarianMTModel,) if is_tf_available() else ()
       snake_case_ :     List[str]    =					(
           {
               """conversational""": TFMarianMTModel,
               """feature-extraction""": TFMarianModel,
               """summarization""": TFMarianMTModel,
               """text2text-generation""": TFMarianMTModel,
               """translation""": TFMarianMTModel,
           }
           if is_tf_available()
           else {}
       )
       snake_case_ :     int    =					True
       snake_case_ :     int    =					False
       snake_case_ :     List[str]    =					False

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						TFMarianModelTester(self     )
           __lowercase							=						ConfigTester(self					,		config_class=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           self.config_tester.run_common_tests()




       def     snake_case__			(    self     )    ->					Tuple:

           """simple docstring"""



           __lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__     )






@require_tf
class        SCREAMING_SNAKE_CASE(				unittest.TestCase     ):
       snake_case_ :     Dict    =					1000  # show more chars for failing integration tests

       @classmethod
       def     snake_case__			(    cls     )    ->					None:

           """simple docstring"""



           __lowercase							=						F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
           return cls

       @cached_property
       def     snake_case__			(    self     )    ->					MarianTokenizer:

           """simple docstring"""



           return AutoTokenizer.from_pretrained(self.model_name     )

       @property
       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           return self.tokenizer.eos_token_id

       @cached_property
       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           warnings.simplefilter("""error"""     )
           __lowercase							=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
           assert isinstance(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						model.config
           self.assertListEqual(c.bad_words_ids					,		[[c.pad_token_id]]     )
           self.assertEqual(c.max_length					,		512     )
           self.assertEqual(c.decoder_start_token_id					,		c.pad_token_id     )
           return model

       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						self.translate_src_text(**lowerCamelCase__     )
           self.assertListEqual(self.expected_text					,		lowerCamelCase__     )




       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					Optional[Any]:

           """simple docstring"""



           __lowercase							=						self.tokenizer(self.src_text					,		**lowerCamelCase__					,		padding=lowerCamelCase__					,		return_tensors="""tf"""     )
           __lowercase							=						self.model.generate(
               model_inputs.input_ids					,		attention_mask=model_inputs.attention_mask					,		num_beams=2					,		max_length=128     )
           __lowercase							=						self.tokenizer.batch_decode(generated_ids.numpy()					,		skip_special_tokens=lowerCamelCase__     )
           return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Union[str, Any]    =					"""mt"""
       snake_case_ :     Tuple    =					"""en"""
       snake_case_ :     Dict    =					["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
       snake_case_ :     int    =					["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

       @unittest.skip("""Skipping until #12647 is resolved."""     )
       @slow
       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					"""en"""
       snake_case_ :     Dict    =					"""zh"""
       snake_case_ :     Union[str, Any]    =					["""My name is Wolfgang and I live in Berlin"""]
       snake_case_ :     Dict    =					["""我叫沃尔夫冈 我住在柏林"""]

       @unittest.skip("""Skipping until #12647 is resolved."""     )
       @slow
       def     snake_case__			(    self     )    ->					Tuple:

           """simple docstring"""



           self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[str]    =					"""en"""
       snake_case_ :     int    =					"""ROMANCE"""
       snake_case_ :     List[Any]    =					[
           """>>fr<< Don't spend so much time watching TV.""",
           """>>pt<< Your message has been sent.""",
           """>>es<< He's two years older than me.""",
       ]
       snake_case_ :     Union[str, Any]    =					[
           """Ne passez pas autant de temps à regarder la télé.""",
           """A sua mensagem foi enviada.""",
           """Es dos años más viejo que yo.""",
       ]

       @unittest.skip("""Skipping until #12647 is resolved."""     )
       @slow
       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           self._assert_generated_batch_equal_expected()




       @unittest.skip("""Skipping until #12647 is resolved."""     )
       @slow
       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           __lowercase							=						TranslationPipeline(self.model					,		self.tokenizer					,		framework="""tf"""     )
           __lowercase							=						pipeline(self.src_text     )
           self.assertEqual(self.expected_text					,		[x["""translation_text"""] for x in output]     )










'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 				snake_case_  (   a__		:	Optional[int]    ,a__		:	str    ,a__		:	int			):



    """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__":
  A							:      Optional[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."""
  )
  A							:      Dict 	=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




