


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        a	:




	"""simple docstring"""


	__UpperCAmelCase					:							str  						=						LEDConfig
	__UpperCAmelCase					:							List[Any]  						=						{}
	__UpperCAmelCase					:							List[Any]  						=						"gelu"

	def __init__(							self :						Union[str, Any]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						str=13					,			lowerCamelCase :						Any=7					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Dict=False					,			lowerCamelCase :						Any=99					,			lowerCamelCase :						str=32					,			lowerCamelCase :						Union[str, Any]=2					,			lowerCamelCase :						Optional[Any]=4					,			lowerCamelCase :						List[str]=37					,			lowerCamelCase :						Any=0.1					,			lowerCamelCase :						Dict=0.1					,			lowerCamelCase :						Any=20					,			lowerCamelCase :						List[str]=2					,			lowerCamelCase :						Any=1					,			lowerCamelCase :						int=0					,			lowerCamelCase :						Optional[int]=4					,			)			->					Optional[int]:
								__snake_case	:    int											=     parent
								__snake_case	:    str											=     batch_size
								__snake_case	:    Any											=     seq_length
								__snake_case	:    Tuple											=     is_training
								__snake_case	:    List[str]											=     use_labels
								__snake_case	:    Optional[Any]											=     vocab_size
								__snake_case	:    int											=     hidden_size
								__snake_case	:    Optional[Any]											=     num_hidden_layers
								__snake_case	:    int											=     num_attention_heads
								__snake_case	:    Optional[Any]											=     intermediate_size

								__snake_case	:    Any											=     hidden_dropout_prob
								__snake_case	:    Any											=     attention_probs_dropout_prob
								__snake_case	:    str											=     max_position_embeddings
								__snake_case	:    Union[str, Any]											=     eos_token_id
								__snake_case	:    List[Any]											=     pad_token_id
								__snake_case	:    Union[str, Any]											=     bos_token_id
								__snake_case	:    str											=     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
								__snake_case	:    Optional[int]											=     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
								__snake_case	:    Tuple											=     (
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)

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

								__snake_case	:    Tuple											=     ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size       )

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






	def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						int					,			lowerCamelCase :						Any       )			->					str:
								__snake_case	:    List[str]											=     TFLEDModel(config=_UpperCAmelCase       ).get_decoder()
								__snake_case	:    List[str]											=     inputs_dict["input_ids"]

								__snake_case	:    Union[str, Any]											=     input_ids[:1, :]
								__snake_case	:    Optional[Any]											=     inputs_dict["attention_mask"][:1, :]
								__snake_case	:    int											=     1

								# first forward pass
								__snake_case	:    List[str]											=     model(_UpperCAmelCase					,			attention_mask=_UpperCAmelCase					,			use_cache=_UpperCAmelCase       )

								__snake_case				,    __snake_case	:    List[str]											=     outputs.to_tuple()

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

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

								__snake_case	:    int											=     model(_UpperCAmelCase					,			attention_mask=_UpperCAmelCase       )[0]
								__snake_case	:    int											=     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
								__snake_case	:    int											=     int(ids_tensor((1,)					,			output_from_past.shape[-1]       )       )
								__snake_case	:    List[Any]											=     output_from_no_past[:, -3:, random_slice_idx]
								__snake_case	:    Optional[Any]											=     output_from_past[:, :, random_slice_idx]

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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			):
							if attention_mask is None:
														__snake_case	:    Tuple											=     tf.cast(tf.math.not_equal(lowerCAmelCase__							,			config.pad_token_id			)							,			tf.inta			)
							if decoder_attention_mask is None:
														__snake_case	:    Optional[Any]											=     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:
														__snake_case	:    Union[str, Any]											=     tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
							if decoder_head_mask is None:
														__snake_case	:    List[Any]											=     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        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




	"""simple docstring"""


	__UpperCAmelCase					:							List[str]  						=						(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
	__UpperCAmelCase					:							Tuple  						=						(TFLEDForConditionalGeneration,) if is_tf_available() else ()
	__UpperCAmelCase					:							Optional[int]  						=						(
	    {
	        "conversational": TFLEDForConditionalGeneration,
	        "feature-extraction": TFLEDModel,
	        "summarization": TFLEDForConditionalGeneration,
	        "text2text-generation": TFLEDForConditionalGeneration,
	        "translation": TFLEDForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__UpperCAmelCase					:							Dict  						=						True
	__UpperCAmelCase					:							Dict  						=						False
	__UpperCAmelCase					:							Optional[int]  						=						False
	__UpperCAmelCase					:							Tuple  						=						False

	def 					__snake_case      (							self :						str       )			->					Optional[Any]:
								__snake_case	:    Tuple											=     TFLEDModelTester(self       )
								__snake_case	:    Optional[Any]											=     ConfigTester(self					,			config_class=_UpperCAmelCase       )

	def 					__snake_case      (							self :						Tuple       )			->					str:
								self.config_tester.run_common_tests()

	def 					__snake_case      (							self :						Union[str, Any]       )			->					Any:
								__snake_case	:    Any											=     self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase       )

	def 					__snake_case      (							self :						Any       )			->					Union[str, Any]:
								__snake_case				,    __snake_case	:    Optional[Any]											=     self.model_tester.prepare_config_and_inputs_for_common()
								__snake_case	:    List[str]											=     tf.zeros_like(inputs_dict["attention_mask"]       )
								__snake_case	:    List[str]											=     2
								__snake_case	:    Any											=     tf.where(
								    tf.range(self.model_tester.seq_length       )[None, :] < num_global_attn_indices					,			1					,			inputs_dict["global_attention_mask"]					,			)

								__snake_case	:    Tuple											=     True
								__snake_case	:    Optional[int]											=     self.model_tester.seq_length
								__snake_case	:    List[Any]											=     self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(lowerCamelCase :						Optional[int]       ):
															__snake_case	:    int											=     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(lowerCamelCase :						List[str]       ):
															__snake_case	:    List[Any]											=     [t.numpy() for t in outputs.encoder_attentions]
															__snake_case	:    Union[str, Any]											=     [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:
															__snake_case	:    Union[str, Any]											=     True
															__snake_case	:    Dict											=     False
															__snake_case	:    Optional[int]											=     False
															__snake_case	:    str											=     model_class(_UpperCAmelCase       )
															__snake_case	:    List[Any]											=     model(self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase       )       )
															__snake_case	:    Union[str, Any]											=     len(_UpperCAmelCase       )
															self.assertEqual(config.output_hidden_states					,			_UpperCAmelCase       )
															check_encoder_attentions_output(_UpperCAmelCase       )

															if self.is_encoder_decoder:
																						__snake_case	:    Union[str, Any]											=     model_class(_UpperCAmelCase       )
																						__snake_case	:    List[Any]											=     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"]
															__snake_case	:    List[str]											=     True
															__snake_case	:    int											=     model_class(_UpperCAmelCase       )
															__snake_case	:    List[Any]											=     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
															__snake_case	:    str											=     True
															__snake_case	:    List[Any]											=     True
															__snake_case	:    List[str]											=     model_class(_UpperCAmelCase       )
															__snake_case	:    List[str]											=     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 					__snake_case      (							self :						Dict       )			->					Union[str, Any]:
								pass






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







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


_snake_case  :	str			      =       1E-4

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




	"""simple docstring"""


	def 					__snake_case      (							self :						Union[str, Any]       )			->					str:
								__snake_case	:    Any											=     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"       ).led

								# change to intended input here
								__snake_case	:    List[str]											=     _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
								__snake_case	:    Tuple											=     _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
								__snake_case	:    int											=     prepare_led_inputs_dict(model.config					,			_UpperCAmelCase					,			_UpperCAmelCase       )
								__snake_case	:    List[str]											=     model(**_UpperCAmelCase       )[0]
								__snake_case	:    List[Any]											=     (1, 1024, 768)
								self.assertEqual(output.shape					,			_UpperCAmelCase       )
								# change to expected output here
								__snake_case	:    str											=     tf.convert_to_tensor(
								    [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]					,			)
								tf.debugging.assert_near(output[:, :3, :3]					,			_UpperCAmelCase					,			atol=1E-3       )






	def 					__snake_case      (							self :						str       )			->					List[Any]:
								__snake_case	:    Any											=     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"       )

								# change to intended input here
								__snake_case	:    str											=     _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
								__snake_case	:    int											=     _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
								__snake_case	:    Union[str, Any]											=     prepare_led_inputs_dict(model.config					,			_UpperCAmelCase					,			_UpperCAmelCase       )
								__snake_case	:    Union[str, Any]											=     model(**_UpperCAmelCase       )[0]
								__snake_case	:    Union[str, Any]											=     (1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape					,			_UpperCAmelCase       )
								# change to expected output here
								__snake_case	:    Optional[Any]											=     tf.convert_to_tensor(
								    [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]					,			)
								tf.debugging.assert_near(output[:, :3, :3]					,			_UpperCAmelCase					,			atol=1E-3					,			rtol=1E-3       )





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	int			      =       {
    "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",
}

_snake_case  :	Dict			      =       AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    Any											=     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        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    int											=     {}

							__snake_case	:    List[Any]											=     R".*sequential.(\d+).*"
							__snake_case	:    Any											=     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:
																												__snake_case	:    Optional[int]											=     key.replace(__lowerCamelCase							,			__lowerCamelCase			)

														if re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					# replace sequential layers with list
																					__snake_case	:    List[Any]											=     re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)

																					__snake_case	:    str											=     key.replace(F'sequential.{sequential_layer}.'							,			F'layers.{int(__lowerCamelCase			)//3}.linear.'			)
														elif re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Any											=     int(re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)			)

																					# Because in CLAP they use `nn.Sequential`...
																					__snake_case	:    List[str]											=     1 if projecton_layer == 0 else 2

																					__snake_case	:    Tuple											=     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
																					__snake_case	:    List[str]											=     value
																					__snake_case	:    Optional[int]											=     mixed_qkv.size(0			) // 3

																					__snake_case	:    List[str]											=     mixed_qkv[:qkv_dim]
																					__snake_case	:    int											=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case	:    str											=     mixed_qkv[qkv_dim * 2 :]

																					__snake_case	:    int											=     query_layer
																					__snake_case	:    Tuple											=     key_layer
																					__snake_case	:    List[str]											=     value_layer
														else:
																					__snake_case	:    Tuple											=     value

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

							clap_model.eval()
							__snake_case	:    Union[str, Any]											=     clap_model.state_dict()
							__snake_case	:    Any											=     rename_state_dict(__lowerCamelCase			)

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[Any]											=     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__":
			_snake_case  :	Union[str, Any]			      =       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")
			_snake_case  :	Tuple			      =       parser.parse_args()

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

