

"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast





@require_vision
class 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE__									= CLIPImageProcessor()
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained(
						    """hf-internal-testing/tiny-random-BertModel"""      ,			model_input_names=["""input_ids""", """attention_mask"""]    )

						SCREAMING_SNAKE_CASE__									= GitProcessor(lowercase__      ,			lowercase__    )

						processor.save_pretrained(self.tmpdirname    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			**__UpperCAmelCase			:  int    )				->      Union[str, Any]:
						return AutoProcessor.from_pretrained(self.tmpdirname      ,			**lowercase__    ).tokenizer

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			**__UpperCAmelCase			:  str    )				->      int:
						return AutoProcessor.from_pretrained(self.tmpdirname      ,			**lowercase__    ).image_processor

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Any:
						shutil.rmtree(self.tmpdirname    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= [np.random.randint(2_5_5      ,			size=(3, 3_0, 4_0_0)      ,			dtype=np.uinta    )]

						SCREAMING_SNAKE_CASE__									= [Image.fromarray(np.moveaxis(lowercase__      ,			0      ,			-1    )    ) for x in image_inputs]

						return image_inputs

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=self.get_tokenizer()      ,			image_processor=self.get_image_processor()    )
						processor.save_pretrained(self.tmpdirname    )

						SCREAMING_SNAKE_CASE__									= self.get_tokenizer(bos_token="""(BOS)"""      ,			eos_token="""(EOS)"""    )
						SCREAMING_SNAKE_CASE__									= self.get_image_processor(do_normalize=lowercase__      ,			padding_value=1.0    )

						SCREAMING_SNAKE_CASE__									= GitProcessor.from_pretrained(
						    self.tmpdirname      ,			bos_token="""(BOS)"""      ,			eos_token="""(EOS)"""      ,			do_normalize=lowercase__      ,			padding_value=1.0    )

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

						self.assertEqual(processor.image_processor.to_json_string()      ,			image_processor_add_kwargs.to_json_string()    )
						self.assertIsInstance(processor.image_processor      ,			lowercase__    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=lowercase__      ,			image_processor=lowercase__    )

						SCREAMING_SNAKE_CASE__									= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__									= image_processor(lowercase__      ,			return_tensors="""np"""    )
						SCREAMING_SNAKE_CASE__									= processor(images=lowercase__      ,			return_tensors="""np"""    )

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,			input_processor[key].sum()      ,			delta=1e-2    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Any:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=lowercase__      ,			image_processor=lowercase__    )

						SCREAMING_SNAKE_CASE__									= "lower newer"

						SCREAMING_SNAKE_CASE__									= processor(text=lowercase__    )

						SCREAMING_SNAKE_CASE__									= tokenizer(lowercase__      ,			return_token_type_ids=lowercase__    )

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

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=lowercase__      ,			image_processor=lowercase__    )

						SCREAMING_SNAKE_CASE__									= "lower newer"
						SCREAMING_SNAKE_CASE__									= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__									= processor(text=lowercase__      ,			images=lowercase__    )

						self.assertListEqual(list(inputs.keys()    )      ,			["""input_ids""", """attention_mask""", """pixel_values"""]    )

						# test if it raises when no input is passed
						with pytest.raises(lowercase__    ):
									processor()

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=lowercase__      ,			image_processor=lowercase__    )

						SCREAMING_SNAKE_CASE__									= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						SCREAMING_SNAKE_CASE__									= processor.batch_decode(lowercase__    )
						SCREAMING_SNAKE_CASE__									= tokenizer.batch_decode(lowercase__    )

						self.assertListEqual(lowercase__      ,			lowercase__    )





			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=lowercase__      ,			image_processor=lowercase__    )

						SCREAMING_SNAKE_CASE__									= "lower newer"
						SCREAMING_SNAKE_CASE__									= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__									= processor(text=lowercase__      ,			images=lowercase__    )

						# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
						self.assertListEqual(list(inputs.keys()    )      ,			["""input_ids""", """attention_mask""", """pixel_values"""]    )





"""simple docstring"""




import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


A_							:     str     =	logging.get_logger(__name__)





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Any   						=							'semantic'

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Tuple=7_6_8      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Optional[int]=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Any=0.7      ,			__UpperCAmelCase			:  Optional[int]=True      ,			__UpperCAmelCase			:  str=1_0_0_4_8      ,			__UpperCAmelCase			:  Tuple=1_2_9_5_9_5      ,			__UpperCAmelCase			:  List[Any]=1_2_9_5_9_9      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  Dict=2_5_6      ,			__UpperCAmelCase			:  int=49.9      ,			**__UpperCAmelCase			:  Any      ,			)				->      List[Any]:
						super().__init__(
						    temperature=__UpperCAmelCase      ,			do_sample=__UpperCAmelCase      ,			eos_token_id=__UpperCAmelCase      ,			renormalize_logits=__UpperCAmelCase      ,			max_new_tokens=__UpperCAmelCase      ,			output_scores=__UpperCAmelCase      ,			return_dict_in_generate=__UpperCAmelCase      ,			output_hidden_states=__UpperCAmelCase      ,			output_attentions=__UpperCAmelCase      ,			**__UpperCAmelCase      ,			)

						SCREAMING_SNAKE_CASE__									= text_encoding_offset
						SCREAMING_SNAKE_CASE__									= text_pad_token
						SCREAMING_SNAKE_CASE__									= eos_token_id
						SCREAMING_SNAKE_CASE__									= semantic_infer_token
						SCREAMING_SNAKE_CASE__									= semantic_vocab_size
						SCREAMING_SNAKE_CASE__									= max_input_semantic_length
						SCREAMING_SNAKE_CASE__									= semantic_rate_hz





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'coarse_acoustics'

			def __init__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  List[Any]=False      ,			__UpperCAmelCase			:  Tuple=False      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  Dict=0.7      ,			__UpperCAmelCase			:  Dict=True      ,			__UpperCAmelCase			:  List[str]=1_2_0_4_8      ,			__UpperCAmelCase			:  Optional[Any]=7_5      ,			__UpperCAmelCase			:  List[str]=2      ,			__UpperCAmelCase			:  List[str]=1_2_0_5_0      ,			__UpperCAmelCase			:  Optional[Any]=2_5_6      ,			__UpperCAmelCase			:  int = 6_3_0      ,			__UpperCAmelCase			:  int = 6_0      ,			**__UpperCAmelCase			:  Any      ,			)				->      Dict:
						super().__init__(
						    temperature=__UpperCAmelCase      ,			do_sample=__UpperCAmelCase      ,			renormalize_logits=__UpperCAmelCase      ,			output_scores=__UpperCAmelCase      ,			return_dict_in_generate=__UpperCAmelCase      ,			output_hidden_states=__UpperCAmelCase      ,			output_attentions=__UpperCAmelCase      ,			**__UpperCAmelCase      ,			)

						SCREAMING_SNAKE_CASE__									= coarse_semantic_pad_token
						SCREAMING_SNAKE_CASE__									= coarse_rate_hz
						SCREAMING_SNAKE_CASE__									= n_coarse_codebooks
						SCREAMING_SNAKE_CASE__									= coarse_infer_token
						SCREAMING_SNAKE_CASE__									= max_coarse_input_length
						SCREAMING_SNAKE_CASE__									= max_coarse_history
						SCREAMING_SNAKE_CASE__									= sliding_window_len





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'fine_acoustics'

			def __init__(   self			:  List[Any]      ,			__UpperCAmelCase			:  Union[str, Any]=0.5      ,			__UpperCAmelCase			:  List[Any]=5_1_2      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_2_4      ,			__UpperCAmelCase			:  Optional[Any]=8      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      Optional[Any]:
						super().__init__(temperature=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= max_fine_history_length
						SCREAMING_SNAKE_CASE__									= max_fine_input_length
						SCREAMING_SNAKE_CASE__									= n_fine_codebooks





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Tuple   						=							'bark'
			lowerCamelCase__				:						Dict   						=							True

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  List[Any]=2_4_0_0_0      ,			__UpperCAmelCase			:  Any=1_0_2_4      ,			**__UpperCAmelCase			:  int      ,			)				->      Any:
						if semantic_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

						if coarse_acoustics_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""    )

						if fine_acoustics_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""    )

						SCREAMING_SNAKE_CASE__									= BarkSemanticGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkCoarseGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkFineGenerationConfig(**__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= sample_rate
						SCREAMING_SNAKE_CASE__									= codebook_size

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  BarkSemanticGenerationConfig      ,			__UpperCAmelCase			:  BarkCoarseGenerationConfig      ,			__UpperCAmelCase			:  BarkFineGenerationConfig      ,			**__UpperCAmelCase			:  Optional[int]      ,			)				->      Tuple:
						return cls(
						    semantic_config=semantic_config.to_dict()      ,			coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,			fine_acoustics_config=fine_acoustics_config.to_dict()      ,			**__UpperCAmelCase      ,			)





			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= copy.deepcopy(self.__dict__    )

						SCREAMING_SNAKE_CASE__									= self.semantic_config.to_dict()
						SCREAMING_SNAKE_CASE__									= self.coarse_acoustics_config.to_dict()
						SCREAMING_SNAKE_CASE__									= self.fine_acoustics_config.to_dict()

						SCREAMING_SNAKE_CASE__									= self.__class__.model_type
						return output


