


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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, TensorType

__lowerCAmelCase	: Any  				=logging.get_logger(__name__)

__lowerCAmelCase	: str  				={
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[Any]						=		'''imagegpt'''
				SCREAMING_SNAKE_CASE__				:			int						=		['''past_key_values''']
				SCREAMING_SNAKE_CASE__				:			str						=		{
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}


				def __init__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Any=512 + 1			,			lowerCAmelCase__			:int=32 * 32			,			lowerCAmelCase__			:Optional[Any]=512			,			lowerCAmelCase__			:Union[str, Any]=24			,			lowerCAmelCase__			:Optional[Any]=8			,			lowerCAmelCase__			:List[str]=None			,			lowerCAmelCase__			:Dict="quick_gelu"			,			lowerCAmelCase__			:Optional[Any]=0.1			,			lowerCAmelCase__			:Any=0.1			,			lowerCAmelCase__			:int=0.1			,			lowerCAmelCase__			:Optional[Any]=1E-5			,			lowerCAmelCase__			:Any=0.02			,			lowerCAmelCase__			:Any=True			,			lowerCAmelCase__			:Optional[Any]=True			,			lowerCAmelCase__			:Optional[Any]=False			,			lowerCAmelCase__			:int=False			,			lowerCAmelCase__			:Tuple=False			,			**lowerCAmelCase__			:Union[str, Any]			,			)		->			str:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     Any					=		n_positions
										__SCREAMING_SNAKE_CASE						:     List[str]					=		n_embd
										__SCREAMING_SNAKE_CASE						:     int					=		n_layer
										__SCREAMING_SNAKE_CASE						:     Any					=		n_head
										__SCREAMING_SNAKE_CASE						:     Any					=		n_inner
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		activation_function
										__SCREAMING_SNAKE_CASE						:     Dict					=		resid_pdrop
										__SCREAMING_SNAKE_CASE						:     int					=		embd_pdrop
										__SCREAMING_SNAKE_CASE						:     Any					=		attn_pdrop
										__SCREAMING_SNAKE_CASE						:     str					=		layer_norm_epsilon
										__SCREAMING_SNAKE_CASE						:     str					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     List[str]					=		scale_attn_weights
										__SCREAMING_SNAKE_CASE						:     str					=		use_cache
										__SCREAMING_SNAKE_CASE						:     int					=		scale_attn_by_inverse_layer_idx
										__SCREAMING_SNAKE_CASE						:     List[str]					=		reorder_and_upcast_attn
										__SCREAMING_SNAKE_CASE						:     Dict					=		tie_word_embeddings

										super().__init__(tie_word_embeddings=lowerCAmelCase__			,			**lowerCAmelCase__	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
										    ]	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"FeatureExtractionMixin"			,			lowerCAmelCase__			:int = 1			,			lowerCAmelCase__			:int = -1			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:Optional["TensorType"] = None			,			lowerCAmelCase__			:int = 3			,			lowerCAmelCase__			:int = 32			,			lowerCAmelCase__			:int = 32			,			)		->			Mapping[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._generate_dummy_images(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		dict(preprocessor(images=lowerCAmelCase__			,			return_tensors=lowerCAmelCase__	)	)

										return inputs




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        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		'''ylacombe/bark-small'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:     str					=		'''en_speaker_1'''
										__SCREAMING_SNAKE_CASE						:     Any					=		'''This is a test string'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings_path.json'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings'''


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			int:
										shutil.rmtree(self.tmpdirname	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										processor.save_pretrained(self.tmpdirname	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BarkProcessor.from_pretrained(self.tmpdirname	)

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


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[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						:     Union[str, 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 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, 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						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		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						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		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						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenizer(
										    self.input_string			,			padding='''max_length'''			,			max_length=256			,			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()	)
