




'''simple docstring'''


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

_lowercase  =			logging.get_logger(__name__)

_lowercase  =			{
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     Dict			  =      '''imagegpt'''
	lowercase_       :     Union[str, Any]			  =      ['''past_key_values''']
	lowercase_       :     Optional[int]			  =      {
	    '''hidden_size''': '''n_embd''',
	    '''max_position_embeddings''': '''n_positions''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Any=5_1_2 + 1       ,		__lowerCAmelCase     :  Dict=3_2 * 3_2       ,		__lowerCAmelCase     :  str=5_1_2       ,		__lowerCAmelCase     :  str=2_4       ,		__lowerCAmelCase     :  Union[str, Any]=8       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		__lowerCAmelCase     :  List[str]="quick_gelu"       ,		__lowerCAmelCase     :  Any=0.1       ,		__lowerCAmelCase     :  Optional[Any]=0.1       ,		__lowerCAmelCase     :  Dict=0.1       ,		__lowerCAmelCase     :  Union[str, Any]=1E-5       ,		__lowerCAmelCase     :  List[Any]=0.02       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  Tuple=True       ,		__lowerCAmelCase     :  Union[str, Any]=False       ,		__lowerCAmelCase     :  int=False       ,		__lowerCAmelCase     :  List[Any]=False       ,		**__lowerCAmelCase     :  str       ,		):
							__snake_case						 =   vocab_size
							__snake_case						 =   n_positions
							__snake_case						 =   n_embd
							__snake_case						 =   n_layer
							__snake_case						 =   n_head
							__snake_case						 =   n_inner
							__snake_case						 =   activation_function
							__snake_case						 =   resid_pdrop
							__snake_case						 =   embd_pdrop
							__snake_case						 =   attn_pdrop
							__snake_case						 =   layer_norm_epsilon
							__snake_case						 =   initializer_range
							__snake_case						 =   scale_attn_weights
							__snake_case						 =   use_cache
							__snake_case						 =   scale_attn_by_inverse_layer_idx
							__snake_case						 =   reorder_and_upcast_attn
							__snake_case						 =   tie_word_embeddings

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





class 						a_				(			UpperCAmelCase__						):




	@property
	def   lowercase__   (       self     :  Dict      ):
							return OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'sequence'}),
							    ]      )




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  "FeatureExtractionMixin"       ,		__lowerCAmelCase     :  int = 1       ,		__lowerCAmelCase     :  int = -1       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  Optional["TensorType"] = None       ,		__lowerCAmelCase     :  int = 3       ,		__lowerCAmelCase     :  int = 3_2       ,		__lowerCAmelCase     :  int = 3_2       ,		):
							__snake_case						 =   self._generate_dummy_images(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )
							__snake_case						 =   dict(preprocessor(images=__lowerCAmelCase       ,		return_tensors=__lowerCAmelCase      )      )

							return inputs







'''simple docstring'''


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 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'ylacombe/bark-small'
							__snake_case						 =   tempfile.mkdtemp()
							__snake_case						 =   'en_speaker_1'
							__snake_case						 =   'This is a test string'
							__snake_case						 =   'speaker_embeddings_path.json'
							__snake_case						 =   'speaker_embeddings'




	def   lowercase__   (       self     :  int       ,		**__lowerCAmelCase     :  str      ):
							return AutoTokenizer.from_pretrained(self.checkpoint       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							shutil.rmtree(self.tmpdirname      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							processor.save_pretrained(self.tmpdirname      )
							__snake_case						 =   BarkProcessor.from_pretrained(self.tmpdirname      )

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




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   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       ,		)

							__snake_case						 =   self.get_tokenizer(bos_token='(BOS)'       ,		eos_token='(EOS)'      )

							__snake_case						 =   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   lowercase__   (       self     :  str      ):
							__snake_case						 =   BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)

							__snake_case						 =   3_5
							__snake_case						 =   2
							__snake_case						 =   8

							__snake_case						 =   {
							    '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
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )

							__snake_case						 =   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
							__snake_case						 =   os.path.join(self.tmpdirname       ,		'file.npz'      )
							np.savez(__lowerCAmelCase       ,		**__lowerCAmelCase      )
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )
							__snake_case						 =   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
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=self.voice_preset      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							__snake_case						 =   processor(text=self.input_string      )

							__snake_case						 =   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()      )

