





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCamelCase__      =logging.get_logger(__name__)


UpperCamelCase__      ={
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}





class 			lowerCAmelCase__( _UpperCamelCase    ):






   '''simple docstring'''


   __snake_case 			=    'codegen'
   __snake_case 			=    {
       'max_position_embeddings': 'n_positions',
       'hidden_size': 'n_embd',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }




   def __init__(      self    ,						__lowerCamelCase=5_0_4_0_0    ,						__lowerCamelCase=2_0_4_8    ,						__lowerCamelCase=2_0_4_8    ,						__lowerCamelCase=4_0_9_6    ,						__lowerCamelCase=2_8    ,						__lowerCamelCase=1_6    ,						__lowerCamelCase=6_4    ,						__lowerCamelCase=None    ,						__lowerCamelCase="gelu_new"    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=1E-5    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=True    ,						__lowerCamelCase=5_0_2_5_6    ,						__lowerCamelCase=5_0_2_5_6    ,						__lowerCamelCase=False    ,						**__lowerCamelCase    ,						)			->     str:
        _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							vocab_size
        _SCREAMING_SNAKE_CASE       :	int						    =							n_ctx
        _SCREAMING_SNAKE_CASE       :	Tuple						    =							n_positions
        _SCREAMING_SNAKE_CASE       :	List[Any]						    =							n_embd
        _SCREAMING_SNAKE_CASE       :	List[str]						    =							n_layer
        _SCREAMING_SNAKE_CASE       :	Any						    =							n_head
        _SCREAMING_SNAKE_CASE       :	Any						    =							n_inner
        _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							rotary_dim
        _SCREAMING_SNAKE_CASE       :	List[str]						    =							activation_function
        _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							resid_pdrop
        _SCREAMING_SNAKE_CASE       :	List[str]						    =							embd_pdrop
        _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							attn_pdrop
        _SCREAMING_SNAKE_CASE       :	List[str]						    =							layer_norm_epsilon
        _SCREAMING_SNAKE_CASE       :	List[Any]						    =							initializer_range
        _SCREAMING_SNAKE_CASE       :	int						    =							use_cache

        _SCREAMING_SNAKE_CASE       :	Any						    =							bos_token_id
        _SCREAMING_SNAKE_CASE       :	Dict						    =							eos_token_id

        super().__init__(
            bos_token_id=__lowerCamelCase    ,						eos_token_id=__lowerCamelCase    ,						tie_word_embeddings=__lowerCamelCase    ,						**__lowerCamelCase							)





class 			lowerCAmelCase__( _UpperCamelCase    ):






   '''simple docstring'''


   def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = "default"    ,						__lowerCamelCase = None    ,						__lowerCamelCase = False    ,						)			->     List[Any]:
        super().__init__(__lowerCamelCase    ,						task=__lowerCamelCase    ,						patching_specs=__lowerCamelCase    ,						use_past=__lowerCamelCase							)
        if not getattr(self._config    ,						"pad_token_id"    ,						__lowerCamelCase							):
             # TODO: how to do that better?
             _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0




   @property
   def     UpperCamelCase_			(      self							)			->     Tuple:
        _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}							)
        if self.use_past:
             self.fill_with_past_key_values_(__lowerCamelCase    ,						direction="inputs"							)
             _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{0: "batch", 1: "past_sequence + sequence"}
        else:
             _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{0: "batch", 1: "sequence"}

        return common_inputs




   @property
   def     UpperCamelCase_			(      self							)			->     List[Any]:
        return self._config.n_layer




   @property
   def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
        return self._config.n_head




   def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = -1    ,						__lowerCamelCase = -1    ,						__lowerCamelCase = False    ,						__lowerCamelCase = None    ,						)			->     Optional[Any]:
        _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							super(__lowerCamelCase    ,						self							).generate_dummy_inputs(
            __lowerCamelCase    ,						batch_size=__lowerCamelCase    ,						seq_length=__lowerCamelCase    ,						is_pair=__lowerCamelCase    ,						framework=__lowerCamelCase							)

        # We need to order the input in the way they appears in the forward()
        _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							OrderedDict({"input_ids": common_inputs["input_ids"]}							)

        # Need to add the past_keys
        if self.use_past:
             if not is_torch_available():
                  raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
             else:
                  import torch

                  _SCREAMING_SNAKE_CASE       :	str						    =							common_inputs["input_ids"].shape
                  # Not using the same length for past_key_values
                  _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							seqlen + 2
                  _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							(
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[
                      (torch.zeros(__lowerCamelCase							), torch.zeros(__lowerCamelCase							)) for _ in range(self.num_layers							)
                  ]

        _SCREAMING_SNAKE_CASE       :	Dict						    =							common_inputs["attention_mask"]
        if self.use_past:
             _SCREAMING_SNAKE_CASE       :	str						    =							ordered_inputs["attention_mask"].dtype
             _SCREAMING_SNAKE_CASE       :	Any						    =							torch.cat(
                 [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase    ,						__lowerCamelCase    ,						dtype=__lowerCamelCase							)]    ,						dim=1							)

        return ordered_inputs







   @property
   def     UpperCamelCase_			(      self							)			->     List[str]:
        return 1_3






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							"ylacombe/bark-small"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"en_speaker_1"
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"This is a test string"
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"speaker_embeddings_path.json"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"speaker_embeddings"




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            return AutoTokenizer.from_pretrained(self.checkpoint    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            shutil.rmtree(self.tmpdirname							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            processor.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(self.tmpdirname							)

            self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab()							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)
            processor.save_pretrained(
                self.tmpdirname    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						speaker_embeddings_directory=self.speaker_embeddings_directory    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)"							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BarkProcessor.from_pretrained(
                self.tmpdirname    ,						self.speaker_embeddings_dict_path    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						)

            self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab()							)




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							3_5
            _SCREAMING_SNAKE_CASE       :	Any						    =							2
            _SCREAMING_SNAKE_CASE       :	int						    =							8

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
                "semantic_prompt": np.ones(__lowerCamelCase							),
                "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)							),
                "fine_prompt": np.ones((nb_codebooks_total, seq_len)							),
            }

            # test providing already loaded voice_preset
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							inputs["history_prompt"]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from npz file
            _SCREAMING_SNAKE_CASE       :	Any						    =							os.path.join(self.tmpdirname    ,						"file.npz"							)
            np.savez(__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							inputs["history_prompt"]

            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from the hub
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							processor(text=self.input_string    ,						voice_preset=self.voice_preset							)







       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							processor(text=self.input_string							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer(
                self.input_string    ,						padding="max_length"    ,						max_length=2_5_6    ,						add_special_tokens=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,						encoded_processor[key].squeeze().tolist()							)