
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.xglm.modeling_tf_xglm import (
							    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFXGLMForCausalLM,
							    TFXGLMModel,
							)

@require_tf
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Dict    =	XGLMConfig
			__UpperCamelCase :  Optional[Any]    =	{}
			__UpperCamelCase :  Any    =	"""gelu"""

			def __init__(    self	:    str  ,		snake_case_	:    Dict  ,		snake_case_	:    str=14  ,		snake_case_	:    List[str]=7  ,		snake_case_	:    Optional[Any]=True  ,		snake_case_	:    Optional[Any]=True  ,		snake_case_	:    Union[str, Any]=True  ,		snake_case_	:    Optional[Any]=99  ,		snake_case_	:    Union[str, Any]=32  ,		snake_case_	:    List[Any]=2  ,		snake_case_	:    List[str]=4  ,		snake_case_	:    Tuple=37  ,		snake_case_	:    Tuple="gelu"  ,		snake_case_	:    Dict=0.1  ,		snake_case_	:    List[str]=0.1  ,		snake_case_	:    Union[str, Any]=512  ,		snake_case_	:    Optional[Any]=0.02  ,		):
							UpperCamelCase_:							List[str]		=  parent
							UpperCamelCase_:							Dict		=  batch_size
							UpperCamelCase_:							Tuple		=  seq_length
							UpperCamelCase_:							Any		=  is_training
							UpperCamelCase_:							int		=  use_input_mask
							UpperCamelCase_:							int		=  use_labels
							UpperCamelCase_:							Optional[int]		=  vocab_size
							UpperCamelCase_:							Any		=  d_model
							UpperCamelCase_:							Tuple		=  num_hidden_layers
							UpperCamelCase_:							Optional[int]		=  num_attention_heads
							UpperCamelCase_:							Any		=  ffn_dim
							UpperCamelCase_:							List[str]		=  activation_function
							UpperCamelCase_:							List[Any]		=  activation_dropout
							UpperCamelCase_:							Dict		=  attention_dropout
							UpperCamelCase_:							List[Any]		=  max_position_embeddings
							UpperCamelCase_:							int		=  initializer_range
							UpperCamelCase_:							Optional[Any]		=  None
							UpperCamelCase_:							Optional[int]		=  0
							UpperCamelCase_:							Optional[int]		=  2
							UpperCamelCase_:							Union[str, Any]		=  1

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							return XGLMConfig.from_pretrained("""facebook/xglm-564M""" )

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Dict		=  tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size )  ,		clip_value_min=0  ,		clip_value_max=3 )

							UpperCamelCase_:							Union[str, Any]		=  None
							if self.use_input_mask:
											UpperCamelCase_:							int		=  random_attention_mask([self.batch_size, self.seq_length] )

							UpperCamelCase_:							int		=  self.get_config()

							UpperCamelCase_:							Dict		=  floats_tensor([self.num_hidden_layers, self.num_attention_heads]  ,		2 )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)

			def  lowerCAmelCase__		(    self	:    List[str] ):
							return XGLMConfig(
							    vocab_size=self.vocab_size  ,		d_model=self.hidden_size  ,		num_layers=self.num_hidden_layers  ,		attention_heads=self.num_attention_heads  ,		ffn_dim=self.ffn_dim  ,		activation_function=self.activation_function  ,		activation_dropout=self.activation_dropout  ,		attention_dropout=self.attention_dropout  ,		max_position_embeddings=self.max_position_embeddings  ,		initializer_range=self.initializer_range  ,		use_cache=snake_case_  ,		bos_token_id=self.bos_token_id  ,		eos_token_id=self.eos_token_id  ,		pad_token_id=self.pad_token_id  ,		return_dict=snake_case_  ,		)




			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Tuple		=  self.prepare_config_and_inputs()

							(
							    (
							    UpperCamelCase_
							),				(
							    UpperCamelCase_
							),				(
							    UpperCamelCase_
							),				(
							    UpperCamelCase_
							),				
							):							str		=  config_and_inputs

							UpperCamelCase_:							int		=  {
							    """input_ids""": input_ids,
							    """head_mask""": head_mask,
							}

							return config, inputs_dict




@require_tf
class     _UpperCamelCase			(   _A       ,    _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			__UpperCamelCase :  Any    =	(TFXGLMForCausalLM,) if is_tf_available() else ()
			__UpperCamelCase :  int    =	(
			    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
			)
			__UpperCamelCase :  str    =	False
			__UpperCamelCase :  Union[str, Any]    =	False
			__UpperCamelCase :  List[str]    =	False

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							List[str]		=  TFXGLMModelTester(self )
							UpperCamelCase_:							Union[str, Any]		=  ConfigTester(self  ,		config_class=snake_case_  ,		n_embd=37 )

			def  lowerCAmelCase__		(    self	:    Dict ):
							self.config_tester.run_common_tests()

			@slow
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCamelCase_:							str		=  TFXGLMModel.from_pretrained(snake_case_ )
											self.assertIsNotNone(snake_case_ )




			@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor.""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							super().test_resize_token_embeddings()




@require_tf
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Union[str, Any]=True ):
							UpperCamelCase_:							Optional[Any]		=  TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
							UpperCamelCase_:							List[Any]		=  tf.convert_to_tensor([[2, 268, 9865]]  ,		dtype=tf.intaa )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							UpperCamelCase_:							Dict		=  [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
							# fmt: on
							UpperCamelCase_:							str		=  model.generate(snake_case_  ,		do_sample=snake_case_  ,		num_beams=1 )
							if verify_outputs:
											self.assertListEqual(output_ids[0].numpy().tolist()  ,		snake_case_ )

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Tuple		=  XGLMTokenizer.from_pretrained("""facebook/xglm-564M""" )
							UpperCamelCase_:							Any		=  TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )

							tf.random.set_seed(0 )
							UpperCamelCase_:							List[Any]		=  tokenizer("""Today is a nice day and"""  ,		return_tensors="""tf""" )
							UpperCamelCase_:							Optional[Any]		=  tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(""":/CPU:0""" ):
											UpperCamelCase_:							str		=  model.generate(snake_case_  ,		do_sample=snake_case_  ,		seed=[7, 0] )
							UpperCamelCase_:							Dict		=  tokenizer.decode(output_ids[0]  ,		skip_special_tokens=snake_case_ )

							UpperCamelCase_:							Any		=  (
							    """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
							)
							self.assertEqual(snake_case_  ,		snake_case_ )




			@slow
			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Tuple		=  TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
							UpperCamelCase_:							List[Any]		=  XGLMTokenizer.from_pretrained("""facebook/xglm-564M""" )

							UpperCamelCase_:							Optional[Any]		=  """left"""

							# use different length sentences to test batching
							UpperCamelCase_:							str		=  [
							    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
							    """left-padding, such as in batched generation. The output for the sequence below should be the same """
							    """regardless of whether left padding is applied or not. When""",
							    """Hello, my dog is a little""",
							]

							UpperCamelCase_:							Optional[int]		=  tokenizer(snake_case_  ,		return_tensors="""tf"""  ,		padding=snake_case_ )
							UpperCamelCase_:							Tuple		=  inputs["""input_ids"""]

							UpperCamelCase_:							str		=  model.generate(input_ids=snake_case_  ,		attention_mask=inputs["""attention_mask"""]  ,		max_new_tokens=12 )

							UpperCamelCase_:							Dict		=  tokenizer(sentences[0]  ,		return_tensors="""tf""" ).input_ids
							UpperCamelCase_:							str		=  model.generate(input_ids=snake_case_  ,		max_new_tokens=12 )

							UpperCamelCase_:							Optional[Any]		=  tokenizer(sentences[1]  ,		return_tensors="""tf""" ).input_ids
							UpperCamelCase_:							List[Any]		=  model.generate(input_ids=snake_case_  ,		max_new_tokens=12 )

							UpperCamelCase_:							Optional[int]		=  tokenizer.batch_decode(snake_case_  ,		skip_special_tokens=snake_case_ )
							UpperCamelCase_:							List[Any]		=  tokenizer.decode(output_non_padded[0]  ,		skip_special_tokens=snake_case_ )
							UpperCamelCase_:							Tuple		=  tokenizer.decode(output_padded[0]  ,		skip_special_tokens=snake_case_ )

							UpperCamelCase_:							int		=  [
							    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
							    """left-padding, such as in batched generation. The output for the sequence below should be the same """
							    """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
							    """a single""",
							    """Hello, my dog is a little bit of a shy one, but he is very friendly""",
							]
							self.assertListEqual(snake_case_  ,		snake_case_ )
							self.assertListEqual(snake_case_  ,		[non_padded_sentence, padded_sentence] )








import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_  :	Union[str, Any] 				=					logging.get_logger(__name__)

set_seed(7_70)


lowerCamelCase_  :	str 				=					{
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


lowerCamelCase_  :	Any 				=					{
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

lowerCamelCase_  :	str 				=					os.path.dirname(os.path.abspath(__file__))
lowerCamelCase_  :	Any 				=					os.path.join(os.path.expanduser("""~"""), """.cache""")
lowerCamelCase_  :	Dict 				=					os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False  )					->		int:
				UpperCamelCase_:							Union[str, Any]		=  model_type
				if use_small:
								key += "_small"
				return os.path.join(lowerCamelCase       ,      REMOTE_MODEL_PATHS[key]["""file_name"""]  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				os.makedirs(lowerCamelCase       ,      exist_ok=lowerCamelCase  )
				hf_hub_download(repo_id=lowerCamelCase       ,      filename=lowerCamelCase       ,      local_dir=lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Optional[int]:
				if model_type == "text":
								UpperCamelCase_:							str		=  BarkSemanticModel
								UpperCamelCase_:							Dict		=  BarkSemanticConfig
								UpperCamelCase_:							int		=  BarkSemanticGenerationConfig
				elif model_type == "coarse":
								UpperCamelCase_:							str		=  BarkCoarseModel
								UpperCamelCase_:							int		=  BarkCoarseConfig
								UpperCamelCase_:							Any		=  BarkCoarseGenerationConfig
				elif model_type == "fine":
								UpperCamelCase_:							Optional[Any]		=  BarkFineModel
								UpperCamelCase_:							int		=  BarkFineConfig
								UpperCamelCase_:							Dict		=  BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				UpperCamelCase_:							str		=  F'''{model_type}_small''' if use_small else model_type
				UpperCamelCase_:							List[Any]		=  REMOTE_MODEL_PATHS[model_key]
				if not os.path.exists(lowerCamelCase  ):
								logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''  )
								_download(model_info["""repo_id"""]       ,      model_info["""file_name"""]  )
				UpperCamelCase_:							int		=  torch.load(lowerCamelCase       ,      map_location=lowerCamelCase  )
				# this is a hack
				UpperCamelCase_:							Tuple		=  checkpoint["""model_args"""]
				if "input_vocab_size" not in model_args:
								UpperCamelCase_:							int		=  model_args["""vocab_size"""]
								UpperCamelCase_:							Optional[int]		=  model_args["""vocab_size"""]
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				UpperCamelCase_:							Tuple		=  model_args.pop("""n_head"""  )
				UpperCamelCase_:							Dict		=  model_args.pop("""n_embd"""  )
				UpperCamelCase_:							List[str]		=  model_args.pop("""n_layer"""  )

				UpperCamelCase_:							Optional[Any]		=  ConfigClass(**checkpoint["""model_args"""]  )
				UpperCamelCase_:							Optional[Any]		=  ModelClass(config=lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GenerationConfigClass()

				UpperCamelCase_:							Optional[Any]		=  model_generation_config
				UpperCamelCase_:							Optional[int]		=  checkpoint["""model"""]
				# fixup checkpoint
				UpperCamelCase_:							Dict		=  """_orig_mod."""
				for k, v in list(state_dict.items()  ):
								if k.startswith(lowerCamelCase  ):
												# replace part of the key with corresponding layer name in HF implementation
												UpperCamelCase_:							Optional[int]		=  k[len(lowerCamelCase  ) :]
												for old_layer_name in new_layer_name_dict:
																UpperCamelCase_:							Dict		=  new_k.replace(lowerCamelCase       ,      new_layer_name_dict[old_layer_name]  )

												UpperCamelCase_:							List[str]		=  state_dict.pop(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  set(state_dict.keys()  ) - set(model.state_dict().keys()  )
				UpperCamelCase_:							Dict		=  {k for k in extra_keys if not k.endswith(""".attn.bias"""  )}
				UpperCamelCase_:							Optional[Any]		=  set(model.state_dict().keys()  ) - set(state_dict.keys()  )
				UpperCamelCase_:							Union[str, Any]		=  {k for k in missing_keys if not k.endswith(""".attn.bias"""  )}
				if len(lowerCamelCase  ) != 0:
								raise ValueError(F'''extra keys found: {extra_keys}'''  )
				if len(lowerCamelCase  ) != 0:
								raise ValueError(F'''missing keys: {missing_keys}'''  )
				model.load_state_dict(lowerCamelCase       ,      strict=lowerCamelCase  )
				UpperCamelCase_:							str		=  model.num_parameters(exclude_embeddings=lowerCamelCase  )
				UpperCamelCase_:							int		=  checkpoint["""best_val_loss"""].item()
				logger.info(F'''model loaded: {round(n_params/1E6       ,      1  )}M params, {round(lowerCamelCase       ,      3  )} loss'''  )
				model.eval()
				model.to(lowerCamelCase  )
				del checkpoint, state_dict

				return model







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Any:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				UpperCamelCase_:							Union[str, Any]		=  """cpu"""  # do conversion on cpu

				UpperCamelCase_:							int		=  _get_ckpt_path(lowerCamelCase       ,      use_small=lowerCamelCase  )
				UpperCamelCase_:							Dict		=  _load_model(lowerCamelCase       ,      lowerCamelCase       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				# load bark initial model
				UpperCamelCase_:							List[Any]		=  _bark_load_model(lowerCamelCase       ,      """cpu"""       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				if model_type == "text":
								UpperCamelCase_:							Tuple		=  bark_model["""model"""]

				if model.num_parameters(exclude_embeddings=lowerCamelCase  ) != bark_model.get_num_params():
								raise ValueError("""initial and new models don't have the same number of parameters"""  )

				# check if same output as the bark model
				UpperCamelCase_:							Optional[Any]		=  5
				UpperCamelCase_:							List[str]		=  10

				if model_type in ["text", "coarse"]:
								UpperCamelCase_:							int		=  torch.randint(2_56       ,      (batch_size, sequence_length)       ,      dtype=torch.int  )
								UpperCamelCase_:							Tuple		=  bark_model(lowerCamelCase  )[0]

								UpperCamelCase_:							Optional[Any]		=  model(lowerCamelCase  )

								# take last logits
								UpperCamelCase_:							Union[str, Any]		=  output_new_model_total.logits[:, [-1], :]

				else:
								UpperCamelCase_:							Tuple		=  3
								UpperCamelCase_:							List[Any]		=  8
								UpperCamelCase_:							List[str]		=  torch.randint(2_56       ,      (batch_size, sequence_length, n_codes_total)       ,      dtype=torch.int  )

								UpperCamelCase_:							int		=  model(lowerCamelCase       ,      lowerCamelCase  )
								UpperCamelCase_:							Any		=  bark_model(lowerCamelCase       ,      lowerCamelCase  )

								UpperCamelCase_:							Optional[int]		=  output_new_model_total.logits

				# output difference should come from the difference of self-attention implementation design
				if output_new_model.shape != output_old_model.shape:
								raise ValueError("""initial and new outputs don't have the same shape"""  )
				if (output_new_model - output_old_model).abs().max().item() > 1E-3:
								raise ValueError("""initial and new outputs are not equal"""  )

				Path(lowerCamelCase  ).mkdir(exist_ok=lowerCamelCase  )
				model.save_pretrained(lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      )					->		str:
				UpperCamelCase_:							List[str]		=  os.path.join(lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							List[Any]		=  BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Optional[int]		=  BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Any		=  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							Optional[Any]		=  BarkSemanticModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  BarkCoarseModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  BarkFineModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  EncodecModel.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							int		=  BarkConfig.from_sub_model_configs(
				    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config       ,      coarseAcoustic.generation_config       ,      fineAcoustic.generation_config  )

				UpperCamelCase_:							Optional[Any]		=  BarkModel(lowerCamelCase  )

				UpperCamelCase_:							int		=  semantic
				UpperCamelCase_:							Tuple		=  coarseAcoustic
				UpperCamelCase_:							Optional[int]		=  fineAcoustic
				UpperCamelCase_:							Any		=  codec

				UpperCamelCase_:							Dict		=  bark_generation_config

				Path(lowerCamelCase  ).mkdir(exist_ok=lowerCamelCase  )
				bark.save_pretrained(lowerCamelCase       ,      repo_id=lowerCamelCase       ,      push_to_hub=lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :	List[Any] 				=					argparse.ArgumentParser()
							# Required parameters

							parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

							lowerCamelCase_  :	Dict 				=					parser.parse_args()

							load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)






