

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 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







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


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return AutoTokenizer.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			)       ->       int:
												shutil.rmtree(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       int   =						self.get_tokenizer()

												_a      :       Optional[Any]   =						BarkProcessor(tokenizer=_a			)

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

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


							@slow
							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						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			,				)

												_a      :       int   =						self.get_tokenizer(bos_token='''(BOS)'''			,				eos_token='''(EOS)'''			)

												_a      :       int   =						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			)       ->       Optional[int]:
												_a      :       List[Any]   =						BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint			,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,				)

												_a      :       Optional[int]   =						3_5
												_a      :       str   =						2
												_a      :       Dict   =						8

												_a      :       Any   =						{
												    '''semantic_prompt''': np.ones(_a			),
												    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)			),
												    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)			),
												}

												# test providing already loaded voice_preset
												_a      :       int   =						processor(text=self.input_string			,				voice_preset=_a			)

												_a      :       Optional[Any]   =						inputs['''history_prompt''']
												for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()			,				processed_voice_preset.get(_a			,				np.array([]			)			).tolist()			)

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

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

												# test loading voice preset from the hub
												_a      :       Dict   =						processor(text=self.input_string			,				voice_preset=self.voice_preset			)




							def 							__lowercase							(     self			)       ->       str:
												_a      :       int   =						self.get_tokenizer()

												_a      :       Any   =						BarkProcessor(tokenizer=_a			)

												_a      :       int   =						processor(text=self.input_string			)

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

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






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "mobilenet_v1"


							def __init__(     self			,				_a=3			,				_a=2_2_4			,				_a=1.0			,				_a=8			,				_a="relu6"			,				_a=True			,				_a=0.999			,				_a=0.02			,				_a=0.001			,				**_a			,				)       ->       List[Any]:
												super().__init__(**_a			)

												if depth_multiplier <= 0:
																	raise ValueError('''depth_multiplier must be greater than zero.'''			)

												_a      :       Tuple   =						num_channels
												_a      :       str   =						image_size
												_a      :       Tuple   =						depth_multiplier
												_a      :       Any   =						min_depth
												_a      :       int   =						hidden_act
												_a      :       Optional[Any]   =						tf_padding
												_a      :       str   =						classifier_dropout_prob
												_a      :       Optional[int]   =						initializer_range
												_a      :       Any   =						layer_norm_eps



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict([('''pixel_values''', {0: '''batch'''})]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																	return OrderedDict([('''logits''', {0: '''batch'''})]			)
												else:
																	return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4



