



"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class    lowercase__     :







       '''simple docstring'''


       UpperCamelCase		=  LEDConfig
       UpperCamelCase		=  {}
       UpperCamelCase		=  '''gelu'''



       def __init__(							self    :						Union[str, Any]	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						str=13	,       _UpperCAmelCase    :						Any=7	,       _UpperCAmelCase    :						Union[str, Any]=True	,       _UpperCAmelCase    :						Dict=False	,       _UpperCAmelCase    :						Any=99	,       _UpperCAmelCase    :						str=32	,       _UpperCAmelCase    :						Union[str, Any]=2	,       _UpperCAmelCase    :						Optional[Any]=4	,       _UpperCAmelCase    :						List[str]=37	,       _UpperCAmelCase    :						Any=0.1	,       _UpperCAmelCase    :						Dict=0.1	,       _UpperCAmelCase    :						Any=20	,       _UpperCAmelCase    :						List[str]=2	,       _UpperCAmelCase    :						Any=1	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Optional[int]=4	,       )				->		Optional[int]:


             '''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
             UpperCAmelCase_    				=							attention_window

             # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
             # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
             # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
             # because its local attention only attends to `self.attention_window` and one before and one after
             UpperCAmelCase_    				=							self.attention_window + 2

             # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
             # the `test_attention_outputs` and `test_hidden_states_output` tests
             UpperCAmelCase_    				=							(
                 self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
             )



       def   lowercase__       (							self    :						Union[str, Any]					)				->		Tuple:


             '''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	,       attention_window=self.attention_window	,       **self.config_updates	,       )
             UpperCAmelCase_    				=							prepare_led_inputs_dict(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							tf.concat(
                 [tf.zeros_like(_UpperCAmelCase					)[:, :-1], tf.ones_like(_UpperCAmelCase					)[:, -1:]]	,       axis=-1	,       )
             UpperCAmelCase_    				=							global_attention_mask
             return config, inputs_dict







       def   lowercase__       (							self    :						Optional[int]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Any					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							TFLEDModel(config=_UpperCAmelCase					).get_decoder()
             UpperCAmelCase_    				=							inputs_dict["input_ids"]

             UpperCAmelCase_    				=							input_ids[:1, :]
             UpperCAmelCase_    				=							inputs_dict["attention_mask"][:1, :]
             UpperCAmelCase_    				=							1

             # first forward pass
             UpperCAmelCase_    				=							model(_UpperCAmelCase	,       attention_mask=_UpperCAmelCase	,       use_cache=_UpperCAmelCase					)

             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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    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)   )
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "decoder_input_ids": decoder_input_ids,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
      }
@require_tf
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
       UpperCamelCase		=  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
       UpperCamelCase		=  (
           {
               '''conversational''': TFLEDForConditionalGeneration,
               '''feature-extraction''': TFLEDModel,
               '''summarization''': TFLEDForConditionalGeneration,
               '''text2text-generation''': TFLEDForConditionalGeneration,
               '''translation''': TFLEDForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       UpperCamelCase		=  True
       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False



       def   lowercase__       (							self    :						str					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							TFLEDModelTester(self					)
             UpperCAmelCase_    				=							ConfigTester(self	,       config_class=_UpperCAmelCase					)



       def   lowercase__       (							self    :						Tuple					)				->		str:


             '''simple docstring'''




             self.config_tester.run_common_tests()



       def   lowercase__       (							self    :						Union[str, Any]					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase					)



       def   lowercase__       (							self    :						Any					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_    				=							tf.zeros_like(inputs_dict["attention_mask"]					)
             UpperCAmelCase_    				=							2
             UpperCAmelCase_    				=							tf.where(
                 tf.range(self.model_tester.seq_length					)[None, :] < num_global_attn_indices	,       1	,       inputs_dict["global_attention_mask"]	,       )

             UpperCAmelCase_    				=							True
             UpperCAmelCase_    				=							self.model_tester.seq_length
             UpperCAmelCase_    				=							self.model_tester.encoder_seq_length

             def check_decoder_attentions_output(_UpperCAmelCase    :						Optional[int]					):
                   UpperCAmelCase_    				=							outputs.decoder_attentions
                   self.assertEqual(len(_UpperCAmelCase					)	,       self.model_tester.num_hidden_layers					)
                   self.assertListEqual(
                       list(decoder_attentions[0].shape[-3:]					)	,       [self.model_tester.num_attention_heads, seq_length, seq_length]	,       )

             def check_encoder_attentions_output(_UpperCAmelCase    :						List[str]					):
                   UpperCAmelCase_    				=							[t.numpy() for t in outputs.encoder_attentions]
                   UpperCAmelCase_    				=							[t.numpy() for t in outputs.encoder_global_attentions]
                   self.assertEqual(len(_UpperCAmelCase					)	,       self.model_tester.num_hidden_layers					)
                   self.assertEqual(len(_UpperCAmelCase					)	,       self.model_tester.num_hidden_layers					)
                   self.assertListEqual(
                       list(attentions[0].shape[-3:]					)	,       [self.model_tester.num_attention_heads, seq_length, seq_length]	,       )
                   self.assertListEqual(
                       list(global_attentions[0].shape[-3:]					)	,       [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]	,       )

             for model_class in self.all_model_classes:
                   UpperCAmelCase_    				=							True
                   UpperCAmelCase_    				=							False
                   UpperCAmelCase_    				=							False
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							model(self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase					)					)
                   UpperCAmelCase_    				=							len(_UpperCAmelCase					)
                   self.assertEqual(config.output_hidden_states	,       _UpperCAmelCase					)
                   check_encoder_attentions_output(_UpperCAmelCase					)

                   if self.is_encoder_decoder:
                         UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                         UpperCAmelCase_    				=							model(self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase					)					)
                         self.assertEqual(config.output_hidden_states	,       _UpperCAmelCase					)
                         check_decoder_attentions_output(_UpperCAmelCase					)

                   # Check that output attentions can also be changed via the config
                   del inputs_dict["output_attentions"]
                   UpperCAmelCase_    				=							True
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							model(self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase					)					)
                   self.assertEqual(config.output_hidden_states	,       _UpperCAmelCase					)
                   check_encoder_attentions_output(_UpperCAmelCase					)

                   # Check attention is always last and order is fine
                   UpperCAmelCase_    				=							True
                   UpperCAmelCase_    				=							True
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							model(self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase					)					)

                   self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)	,       len(_UpperCAmelCase					)					)
                   self.assertEqual(model.config.output_hidden_states	,       _UpperCAmelCase					)
                   check_encoder_attentions_output(_UpperCAmelCase					)



       @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."					)
       def   lowercase__       (							self    :						Dict					)				->		Union[str, Any]:


             '''simple docstring'''




             pass







       def   lowercase__       (							self    :						Optional[int]					)				->		Union[str, Any]:


             '''simple docstring'''




             pass



def      a__			(   lowerCAmelCase__   ):
      return tf.constant(lowerCAmelCase__			,    dtype=tf.intaa   )


lowerCamelCase      =		1e-4


@slow
@require_tf
class    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						Union[str, Any]					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"					).led

             # change to intended input here
             UpperCAmelCase_    				=							_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
             UpperCAmelCase_    				=							_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
             UpperCAmelCase_    				=							prepare_led_inputs_dict(model.config	,       _UpperCAmelCase	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							model(**_UpperCAmelCase					)[0]
             UpperCAmelCase_    				=							(1, 1024, 768)
             self.assertEqual(output.shape	,       _UpperCAmelCase					)
             # change to expected output here
             UpperCAmelCase_    				=							tf.convert_to_tensor(
                 [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]	,       )
             tf.debugging.assert_near(output[:, :3, :3]	,       _UpperCAmelCase	,       atol=1e-3					)







       def   lowercase__       (							self    :						str					)				->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"					)

             # change to intended input here
             UpperCAmelCase_    				=							_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
             UpperCAmelCase_    				=							_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
             UpperCAmelCase_    				=							prepare_led_inputs_dict(model.config	,       _UpperCAmelCase	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							model(**_UpperCAmelCase					)[0]
             UpperCAmelCase_    				=							(1, 1024, model.config.vocab_size)
             self.assertEqual(output.shape	,       _UpperCAmelCase					)
             # change to expected output here
             UpperCAmelCase_    				=							tf.convert_to_tensor(
                 [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]	,       )
             tf.debugging.assert_near(output[:, :3, :3]	,       _UpperCAmelCase	,       atol=1e-3	,       rtol=1e-3					)









"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase      =		{
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

lowerCamelCase      =		AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							create_model(
          "HTSAT-tiny"			,    "roberta"			,    lowerCAmelCase__			,    precision="fp32"			,    device="cuda:0" if torch.cuda.is_available() else "cpu"			,    enable_fusion=lowerCAmelCase__			,    fusion_type="aff_2d" if enable_fusion else None			,    )
      return model, model_cfg
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							{}

      UpperCAmelCase_    				=							r".*sequential.(\d+).*"
      UpperCAmelCase_    				=							r".*_projection.(\d+).*"

      for key, value in state_dict.items():
            # check if any key needs to be modified
            for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
                  if key_to_modify in key:
                        UpperCAmelCase_    				=							key.replace(lowerCAmelCase__			,    lowerCAmelCase__   )

            if re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  # replace sequential layers with list
                  UpperCAmelCase_    				=							re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).group(1   )

                  UpperCAmelCase_    				=							key.replace(f"""sequential.{sequential_layer}."""			,    f"""layers.{int(lowerCAmelCase__   )//3}.linear."""   )
            elif re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							int(re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).group(1   )   )

                  # Because in CLAP they use `nn.Sequential`...
                  UpperCAmelCase_    				=							1 if projecton_layer == 0 else 2

                  UpperCAmelCase_    				=							key.replace(f"""_projection.{projecton_layer}."""			,    f"""_projection.linear{transformers_projection_layer}."""   )

            if "audio" and "qkv" in key:
                  # split qkv into query key and value
                  UpperCAmelCase_    				=							value
                  UpperCAmelCase_    				=							mixed_qkv.size(0   ) // 3

                  UpperCAmelCase_    				=							mixed_qkv[:qkv_dim]
                  UpperCAmelCase_    				=							mixed_qkv[qkv_dim : qkv_dim * 2]
                  UpperCAmelCase_    				=							mixed_qkv[qkv_dim * 2 :]

                  UpperCAmelCase_    				=							query_layer
                  UpperCAmelCase_    				=							key_layer
                  UpperCAmelCase_    				=							value_layer
            else:
                  UpperCAmelCase_    				=							value

      return model_state_dict
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							init_clap(lowerCAmelCase__			,    enable_fusion=lowerCAmelCase__   )

      clap_model.eval()
      UpperCAmelCase_    				=							clap_model.state_dict()
      UpperCAmelCase_    				=							rename_state_dict(lowerCAmelCase__   )

      UpperCAmelCase_    				=							ClapConfig()
      UpperCAmelCase_    				=							enable_fusion
      UpperCAmelCase_    				=							ClapModel(lowerCAmelCase__   )

      # ignore the spectrogram embedding layer
      model.load_state_dict(lowerCAmelCase__			,    strict=lowerCAmelCase__   )

      model.save_pretrained(lowerCAmelCase__   )
      transformers_config.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
  lowerCamelCase      =		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 fairseq checkpoint""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
  lowerCamelCase      =		parser.parse_args()

  convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)




