





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 				__snake_case  :


    '''simple docstring'''







    lowerCamelCase__						:		Any							=  MarianConfig
    lowerCamelCase__						:		Optional[Any]							=  {}
    lowerCamelCase__						:		int							=  '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'''

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

        SCREAMING_SNAKE_CASE__       							= hidden_dropout_prob
        SCREAMING_SNAKE_CASE__       							= attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__       							= max_position_embeddings
        SCREAMING_SNAKE_CASE__       							= eos_token_id
        SCREAMING_SNAKE_CASE__       							= pad_token_id
        SCREAMING_SNAKE_CASE__       							= bos_token_id



    def    lowercase_				(					self    ):





        '''simple docstring'''

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

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

        SCREAMING_SNAKE_CASE__       							= 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      ,							)
        SCREAMING_SNAKE_CASE__       							= prepare_marian_inputs_dict(A_      ,							A_      ,							A_    )
        return config, inputs_dict




    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= TFMarianModel(config=A_    ).get_decoder()
        SCREAMING_SNAKE_CASE__       							= inputs_dict['input_ids']

        SCREAMING_SNAKE_CASE__       							= input_ids[:1, :]
        SCREAMING_SNAKE_CASE__       							= inputs_dict['attention_mask'][:1, :]
        SCREAMING_SNAKE_CASE__       							= inputs_dict['head_mask']
        SCREAMING_SNAKE_CASE__       							= 1

        # first forward pass
        SCREAMING_SNAKE_CASE__       							= model(A_      ,							attention_mask=A_      ,							head_mask=A_      ,							use_cache=A_    )

        SCREAMING_SNAKE_CASE__       							= outputs.to_tuple()

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

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

        SCREAMING_SNAKE_CASE__       							= model(A_      ,							attention_mask=A_    )[0]
        SCREAMING_SNAKE_CASE__       							= model(A_      ,							attention_mask=A_      ,							past_key_values=A_    )[0]

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

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

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





def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=None   ,       )							->	int:
    if attention_mask is None:
        SCREAMING_SNAKE_CASE__       							= tf.cast(tf.math.not_equal(lowerCAmelCase__   ,       config.pad_token_id  )   ,       tf.inta  )
    if decoder_attention_mask is None:
        SCREAMING_SNAKE_CASE__       							= 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:
        SCREAMING_SNAKE_CASE__       							= tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
    if decoder_head_mask is None:
        SCREAMING_SNAKE_CASE__       							= tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
    if cross_attn_head_mask is None:
        SCREAMING_SNAKE_CASE__       							= 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 				__snake_case  (     snake_case__ ,    snake_case__ ,    unittest.TestCase						):


    '''simple docstring'''







    lowerCamelCase__						:		Tuple							=  (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
    lowerCamelCase__						:		Optional[int]							=  (TFMarianMTModel,) if is_tf_available() else ()
    lowerCamelCase__						:		List[str]							=  (
        {
            'conversational': TFMarianMTModel,
            'feature-extraction': TFMarianModel,
            'summarization': TFMarianMTModel,
            'text2text-generation': TFMarianMTModel,
            'translation': TFMarianMTModel,
        }
        if is_tf_available()
        else {}
    )
    lowerCamelCase__						:		str							=  True
    lowerCamelCase__						:		Any							=  False
    lowerCamelCase__						:		Optional[Any]							=  False



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= TFMarianModelTester(self    )
        SCREAMING_SNAKE_CASE__       							= ConfigTester(self      ,							config_class=A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.config_tester.run_common_tests()




    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*A_    )





@require_tf
class 				__snake_case  (     unittest.TestCase						):


    '''simple docstring'''







    lowerCamelCase__						:		List[str]							=  1_0_0_0  # show more chars for failing integration tests



    @classmethod
    def    lowercase_				(					cls    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
        return cls



    @cached_property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        return AutoTokenizer.from_pretrained(self.model_name    )



    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        return self.tokenizer.eos_token_id



    @cached_property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        warnings.simplefilter('''error'''    )
        SCREAMING_SNAKE_CASE__       							= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
        assert isinstance(A_      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= model.config
        self.assertListEqual(c.bad_words_ids      ,							[[c.pad_token_id]]    )
        self.assertEqual(c.max_length      ,							5_12    )
        self.assertEqual(c.decoder_start_token_id      ,							c.pad_token_id    )
        return model



    def    lowercase_				(					self      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.translate_src_text(**A_    )
        self.assertListEqual(self.expected_text      ,							A_    )




    def    lowercase_				(					self      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.tokenizer(self.src_text      ,							**A_      ,							padding=A_      ,							return_tensors='''tf'''    )
        SCREAMING_SNAKE_CASE__       							= self.model.generate(
            model_inputs.input_ids      ,							attention_mask=model_inputs.attention_mask      ,							num_beams=2      ,							max_length=1_28    )
        SCREAMING_SNAKE_CASE__       							= self.tokenizer.batch_decode(generated_ids.numpy()      ,							skip_special_tokens=A_    )
        return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 				__snake_case  (     snake_case__						):


    '''simple docstring'''







    lowerCamelCase__						:		Dict							=  'mt'
    lowerCamelCase__						:		Optional[Any]							=  'en'
    lowerCamelCase__						:		int							=  ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
    lowerCamelCase__						:		Optional[Any]							=  ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']



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





        '''simple docstring'''

        self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 				__snake_case  (     snake_case__						):


    '''simple docstring'''







    lowerCamelCase__						:		Optional[Any]							=  'en'
    lowerCamelCase__						:		Optional[Any]							=  'zh'
    lowerCamelCase__						:		Union[str, Any]							=  ['My name is Wolfgang and I live in Berlin']
    lowerCamelCase__						:		Dict							=  ['我叫沃尔夫冈 我住在柏林']



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





        '''simple docstring'''

        self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 				__snake_case  (     snake_case__						):


    '''simple docstring'''







    lowerCamelCase__						:		str							=  'en'
    lowerCamelCase__						:		Optional[int]							=  'ROMANCE'
    lowerCamelCase__						:		Dict							=  [
        '>>fr<< Don\'t spend so much time watching TV.',
        '>>pt<< Your message has been sent.',
        '>>es<< He\'s two years older than me.',
    ]
    lowerCamelCase__						:		List[str]							=  [
        '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    lowercase_				(					self    ):





        '''simple docstring'''

        self._assert_generated_batch_equal_expected()




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





        '''simple docstring'''

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







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3   ,       dim=0  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3   ,       dim=0  )

    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.weight''']
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.bias''']

    SCREAMING_SNAKE_CASE__       							= q_proj
    SCREAMING_SNAKE_CASE__       							= q_proj_bias

    SCREAMING_SNAKE_CASE__       							= k_proj
    SCREAMING_SNAKE_CASE__       							= k_proj_bias

    SCREAMING_SNAKE_CASE__       							= v_proj
    SCREAMING_SNAKE_CASE__       							= v_proj_bias

    SCREAMING_SNAKE_CASE__       							= out_proj_weights
    SCREAMING_SNAKE_CASE__       							= out_proj_bias


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_fc'''  )
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_proj'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.bias'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    # copy layer norms
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_1'''  )
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_2'''  )

    # copy MLP
    copy_mlp(hf_layer.mlp   ,       lowerCAmelCase_   ,       f'''{prefix}.mlp'''  )

    # copy attn
    copy_attn_layer(hf_layer.self_attn   ,       lowerCAmelCase_   ,       f'''{prefix}.attn'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[int]:
    for layer_id, hf_layer in enumerate(lowerCAmelCase_  ):
        copy_layer(lowerCAmelCase_   ,       lowerCAmelCase_   ,       f'''{prefix}.{layer_id}'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Any:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''text_projection'''].data.T

    # copy text encoder
    for name, param in hf_model.text_model.named_parameters():
        SCREAMING_SNAKE_CASE__       							= pt_weights[f'''bert.{name}'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.proj'''].data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm   ,       lowerCAmelCase_   ,       '''visual.ln_pre'''  )
    copy_linear(hf_model.vision_model.post_layernorm   ,       lowerCAmelCase_   ,       '''visual.ln_post'''  )

    # copy embeddings
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.conv1.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.class_embedding'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.positional_embedding'''].data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers   ,       lowerCAmelCase_   ,       '''visual.transformer.resblocks'''  )


@torch.no_grad()
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None  )							->	str:
    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    SCREAMING_SNAKE_CASE__       							= ChineseCLIPConfig.from_pretrained(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= ChineseCLIPModel(lowerCAmelCase_  ).eval()

    SCREAMING_SNAKE_CASE__       							= torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']
    SCREAMING_SNAKE_CASE__       							= {(name[7:] if name.startswith('''module.'''  ) else name): value for name, value in pt_weights.items()}

    copy_text_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    copy_vision_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
  _A				:							Union[str, Any]							= argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      help="""Path to the output folder storing converted hf PyTorch model.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to original github format ChineseCLIP checkpoint."""
  )
  parser.add_argument(
      """--config_path""", default=None, required=True, type=str, help="""Path to hf config.json of model to convert."""
  )
  _A				:							Union[str, Any]							= parser.parse_args()

  convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
  print("""The conversion is finished!""")
