

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel





@require_tf
class A__    :



 """simple docstring"""







 _lowercase     :					Dict      =						MBartConfig
 _lowercase     :					Dict      =						{}
 _lowercase     :					List[Any]      =						'''gelu'''

 def __init__(		self :	List[str]     ,						A_ :	Tuple     ,						A_ :	List[str]=1_3     ,						A_ :	Dict=7     ,						A_ :	str=True     ,						A_ :	Optional[int]=False     ,						A_ :	List[Any]=9_9     ,						A_ :	Optional[Any]=3_2     ,						A_ :	List[Any]=2     ,						A_ :	Union[str, Any]=4     ,						A_ :	Optional[int]=3_7     ,						A_ :	Optional[Any]=0.1     ,						A_ :	Dict=0.1     ,						A_ :	Optional[int]=2_0     ,						A_ :	Optional[int]=2     ,						A_ :	Union[str, Any]=1     ,						A_ :	int=0     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    parent
      _lowerCAmelCase   :					Union[str, Any]        =    batch_size
      _lowerCAmelCase   :					Optional[Any]        =    seq_length
      _lowerCAmelCase   :					Union[str, Any]        =    is_training
      _lowerCAmelCase   :					List[str]        =    use_labels
      _lowerCAmelCase   :					Dict        =    vocab_size
      _lowerCAmelCase   :					Optional[Any]        =    hidden_size
      _lowerCAmelCase   :					Tuple        =    num_hidden_layers
      _lowerCAmelCase   :					Any        =    num_attention_heads
      _lowerCAmelCase   :					Optional[Any]        =    intermediate_size
      _lowerCAmelCase   :					Optional[Any]        =    hidden_dropout_prob
      _lowerCAmelCase   :					str        =    attention_probs_dropout_prob
      _lowerCAmelCase   :					Union[str, Any]        =    max_position_embeddings
      _lowerCAmelCase   :					Tuple        =    eos_token_id
      _lowerCAmelCase   :					int        =    pad_token_id
      _lowerCAmelCase   :					List[str]        =    bos_token_id

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size, self.seq_length - 1]     ,						self.vocab_size       )
      _lowerCAmelCase   :					Union[str, Any]        =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )     ,						1       )
      _lowerCAmelCase   :					Dict        =    tf.concat([input_ids, eos_tensor]     ,						axis=1       )

      _lowerCAmelCase   :					Union[str, Any]        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					Optional[int]        =    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     ,						)
      _lowerCAmelCase   :					Optional[int]        =    prepare_mbart_inputs_dict(A_     ,						A_     ,						A_       )
      return config, inputs_dict


 def    __magic_name__       (		self :	Any     ,						A_ :	Optional[Any]     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    TFMBartModel(config=A_       ).get_decoder()
      _lowerCAmelCase   :					int        =    inputs_dict["input_ids"]

      _lowerCAmelCase   :					List[str]        =    input_ids[:1, :]
      _lowerCAmelCase   :					Union[str, Any]        =    inputs_dict["attention_mask"][:1, :]
      _lowerCAmelCase   :					List[str]        =    inputs_dict["head_mask"]
      _lowerCAmelCase   :					Tuple        =    1

      # first forward pass
      _lowerCAmelCase   :					Optional[int]        =    model(A_     ,						attention_mask=A_     ,						head_mask=A_     ,						use_cache=A_       )

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    outputs.to_tuple()
      _lowerCAmelCase   :					str        =    past_key_values[1]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							)       ->      Dict:

     """simple docstring"""







     if attention_mask is None:
          _lowerCAmelCase   :					Tuple        =    tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE		,							config.pad_token_id				)		,							tf.inta				)
     if decoder_attention_mask is None:
          _lowerCAmelCase   :					int        =    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:
          _lowerCAmelCase   :					List[str]        =    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
     if decoder_head_mask is None:
          _lowerCAmelCase   :					List[str]        =    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
     if cross_attn_head_mask is None:
          _lowerCAmelCase   :					Union[str, Any]        =    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__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Optional[Any]      =						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
 _lowercase     :					Dict      =						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
 _lowercase     :					Tuple      =						(
     {
         '''conversational''': TFMBartForConditionalGeneration,
         '''feature-extraction''': TFMBartModel,
         '''summarization''': TFMBartForConditionalGeneration,
         '''text2text-generation''': TFMBartForConditionalGeneration,
         '''translation''': TFMBartForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _lowercase     :					List[Any]      =						True
 _lowercase     :					Dict      =						False
 _lowercase     :					Union[str, Any]      =						False

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Optional[Any]     ,						A_ :	Optional[Any]     ,						A_ :	Any     ,						A_ :	Union[str, Any]     ,						A_ :	Dict       ):






      '''simple docstring'''




      if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
           # Exception encountered when calling layer '...'
           return True

      return False

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    TFMBartModelTester(self       )
      _lowerCAmelCase   :					Tuple        =    ConfigTester(self     ,						config_class=A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()


 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*A_       )





@require_sentencepiece
@require_tokenizers
@require_tf
class A__    ( unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Dict      =						[
     ''' UN Chief Says There Is No Military Solution in Syria''',
 ]
 _lowercase     :					Any      =						[
     '''Şeful ONU declară că nu există o soluţie militară în Siria''',
 ]
 _lowercase     :					Optional[int]      =						'''facebook/mbart-large-en-ro'''

 @cached_property
 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      return AutoTokenizer.from_pretrained(self.model_name       )

 @cached_property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
      return model

 def    __magic_name__       (		self :	Optional[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.translate_src_text(**A_       )
      self.assertListEqual(self.expected_text     ,						A_       )

 def    __magic_name__       (		self :	List[Any]     ,						**A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.tokenizer(self.src_text     ,						**A_     ,						return_tensors="tf"       )
      _lowerCAmelCase   :					int        =    self.model.generate(
          model_inputs.input_ids     ,						attention_mask=model_inputs.attention_mask     ,						num_beams=2       )
      _lowerCAmelCase   :					Tuple        =    self.tokenizer.batch_decode(A_     ,						skip_special_tokens=A_       )
      return generated_words


 @slow
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      self._assert_generated_batch_equal_expected()









import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    {}

     for key, value in state_dict.items():
          if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
               continue

          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.cmd.mim_head.cls.predictions"		,							"mmm_image_head"				)
          _lowerCAmelCase   :					int        =    key.replace("heads.cmd.mlm_head.cls.predictions"		,							"mmm_text_head"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.itm_head.cls"		,							"itm_head"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("heads.cmd.itm_head.pooler"		,							"itm_head.pooler"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.clip_head.logit_scale"		,							"flava.logit_scale"				)
          _lowerCAmelCase   :					List[Any]        =    key.replace("heads.fairseq_mlm.cls.predictions"		,							"mlm_head"				)
          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.imagenet.mim_head.cls.predictions"		,							"mim_head"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("mm_text_projection"		,							"flava.text_to_mm_projection"				)
          _lowerCAmelCase   :					int        =    key.replace("mm_image_projection"		,							"flava.image_to_mm_projection"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("image_encoder.module"		,							"flava.image_model"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("text_encoder.module"		,							"flava.text_model"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("mm_encoder.module.encoder.cls_token"		,							"flava.multimodal_model.cls_token"				)
          _lowerCAmelCase   :					Dict        =    key.replace("mm_encoder.module"		,							"flava.multimodal_model"				)
          _lowerCAmelCase   :					Dict        =    key.replace("text_projection"		,							"flava.text_projection"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("image_projection"		,							"flava.image_projection"				)

          _lowerCAmelCase   :					List[Any]        =    value.float()

     for key, value in codebook_state_dict.items():
          _lowerCAmelCase   :					int        =    value

     return upgrade







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None				)       ->      Tuple:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					Dict        =    FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     else:
          _lowerCAmelCase   :					Dict        =    FlavaConfig()

     _lowerCAmelCase   :					List[Any]        =    FlavaForPreTraining(SCREAMING_SNAKE_CASE				).eval()

     _lowerCAmelCase   :					Optional[int]        =    convert_dalle_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							save_checkpoint=SCREAMING_SNAKE_CASE				)

     if os.path.exists(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Dict        =    torch.load(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)
     else:
          _lowerCAmelCase   :					Tuple        =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)

     _lowerCAmelCase   :					Optional[int]        =    upgrade_state_dict(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     hf_model.load_state_dict(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    hf_model.state_dict()
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				) + count_parameters(SCREAMING_SNAKE_CASE				)

     assert torch.allclose(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							atol=1e-3				)

     hf_model.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
  parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  __UpperCAmelCase    				=     parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






