





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 			__A:
     __A							=							MarianConfig
     __A							=							{}
     __A							=							"gelu"

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





            """simple docstring"""

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

            _UpperCamelCase														=    hidden_dropout_prob
            _UpperCamelCase														=    attention_probs_dropout_prob
            _UpperCamelCase														=    max_position_embeddings
            _UpperCamelCase														=    eos_token_id
            _UpperCamelCase														=    pad_token_id
            _UpperCamelCase														=    bos_token_id

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

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

            _UpperCamelCase														=    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,							)
            _UpperCamelCase														=    prepare_marian_inputs_dict(__UpperCamelCase,							__UpperCamelCase,							__UpperCamelCase   )
            return config, inputs_dict



     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    TFMarianModel(config=__UpperCamelCase   ).get_decoder()
            _UpperCamelCase														=    inputs_dict["input_ids"]

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

            # first forward pass
            _UpperCamelCase														=    model(__UpperCamelCase,							attention_mask=__UpperCamelCase,							head_mask=__UpperCamelCase,							use_cache=__UpperCamelCase   )

            _UpperCamelCase														=    outputs.to_tuple()

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

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

            _UpperCamelCase														=    model(__UpperCamelCase,							attention_mask=__UpperCamelCase   )[0]
            _UpperCamelCase														=    model(__UpperCamelCase,							attention_mask=__UpperCamelCase,							past_key_values=__UpperCamelCase   )[0]

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

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

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






def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=None     ,							lowerCAmelCase=None     ,							lowerCAmelCase=None     ,							lowerCAmelCase=None     ,							lowerCAmelCase=None     ,							):
       if attention_mask is None:
              _UpperCamelCase														=    tf.cast(tf.math.not_equal(lowerCAmelCase     ,							config.pad_token_id       )     ,							tf.inta       )
       if decoder_attention_mask is None:
              _UpperCamelCase														=    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:
              _UpperCamelCase														=    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              _UpperCamelCase														=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              _UpperCamelCase														=    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 			__A(    lowerCAmelCase_    ,      lowerCAmelCase_    ,      unittest.TestCase			):
     __A							=							(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     __A							=							(TFMarianMTModel,) if is_tf_available() else ()
     __A							=							(
         {
             "conversational": TFMarianMTModel,
             "feature-extraction": TFMarianModel,
             "summarization": TFMarianMTModel,
             "text2text-generation": TFMarianMTModel,
             "translation": TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     __A							=							True
     __A							=							False
     __A							=							False

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    TFMarianModelTester(self   )
            _UpperCamelCase														=    ConfigTester(self,							config_class=__UpperCamelCase   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self.config_tester.run_common_tests()



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase   )






@require_tf
class 			__A(    unittest.TestCase			):
     __A							=							10_00  # show more chars for failing integration tests

     @classmethod
     def 						_UpperCamelCase  (   cls   ):





            """simple docstring"""

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

     @cached_property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return AutoTokenizer.from_pretrained(self.model_name   )

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.tokenizer.eos_token_id

     @cached_property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            warnings.simplefilter('''error'''   )
            _UpperCamelCase														=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
            assert isinstance(__UpperCamelCase,							__UpperCamelCase   )
            _UpperCamelCase														=    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 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.translate_src_text(**__UpperCamelCase   )
            self.assertListEqual(self.expected_text,							__UpperCamelCase   )



     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

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






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

     @unittest.skip('''Skipping until #12647 is resolved.'''   )
     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._assert_generated_batch_equal_expected()






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

     @unittest.skip('''Skipping until #12647 is resolved.'''   )
     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 			__A(    lowerCAmelCase_			):
     __A							=							"en"
     __A							=							"ROMANCE"
     __A							=							[
         ">>fr<< Don\'t spend so much time watching TV.",
         ">>pt<< Your message has been sent.",
         ">>es<< He\'s two years older than me.",
     ]
     __A							=							[
         "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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._assert_generated_batch_equal_expected()



     @unittest.skip('''Skipping until #12647 is resolved.'''   )
     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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








"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			int							=    FNetConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    FNetForPreTraining(lowerCAmelCase		)

     UpperCAmelCase__				:			int							=    restore_checkpoint(lowerCAmelCase							,     lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     UpperCAmelCase__				:			Union[str, Any]							=    fnet_pretraining_model.state_dict()

     UpperCAmelCase__				:			Optional[Any]							=    state_dict["fnet.embeddings.position_ids"]
     UpperCAmelCase__				:			Dict							=    {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     UpperCAmelCase__				:			Dict							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]		)
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]		).T
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]		)
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]		)
     UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]		)

     # Encoder Layers
     for layer in range(config.num_hidden_layers		):
          UpperCAmelCase__				:			Any							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["kernel"]		).T
          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["bias"]		)

          UpperCAmelCase__				:			Dict							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["kernel"]		).T
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["bias"]		)

     # Pooler Layers
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]		).T
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]		)

     # Masked LM Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]		).T
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]		)
     UpperCAmelCase__				:			List[str]							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]		)
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]		)

     # Seq Relationship Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]		)
     UpperCAmelCase__				:			str							=    torch.tensor(
         pretrained_model_params["classification"]["output_bias"]		)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase		)

     # Save PreTrained
     print(F"Saving pretrained model to {save_path}"		)
     fnet_pretraining_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--fnet_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained FNet model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
    A__   :						Dict 							=     parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



