




'''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,
						    BertTokenizerFast,
						    BlipImageProcessor,
						    GPTaTokenizer,
						    InstructBlipProcessor,
						    PreTrainedTokenizerFast,
						)



@require_vision
class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						__snake_case			=		tempfile.mkdtemp()

						__snake_case			=		BlipImageProcessor()
						__snake_case			=		GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"							)
						__snake_case			=		BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"							)

						__snake_case			=		InstructBlipProcessor(a_      ,		a_      ,		a_							)

						processor.save_pretrained(self.tmpdirname							)





			def 							A (      self						:    Optional[Any]      ,		**a_						:    List[Any]							):



						"""simple docstring"""




						return AutoProcessor.from_pretrained(self.tmpdirname      ,		**a_							).tokenizer





			def 							A (      self						:    Any      ,		**a_						:    int							):



						"""simple docstring"""




						return AutoProcessor.from_pretrained(self.tmpdirname      ,		**a_							).image_processor





			def 							A (      self						:    Any      ,		**a_						:    List[str]							):



						"""simple docstring"""




						return AutoProcessor.from_pretrained(self.tmpdirname      ,		**a_							).qformer_tokenizer





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						shutil.rmtree(self.tmpdirname							)





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		[np.random.randint(255      ,		size=(3, 30, 400)      ,		dtype=np.uinta							)]

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

						return image_inputs





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		InstructBlipProcessor(
						    tokenizer=self.get_tokenizer()      ,		image_processor=self.get_image_processor()      ,		qformer_tokenizer=self.get_qformer_tokenizer()      ,		)
						processor.save_pretrained(self.tmpdirname							)

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

						__snake_case			=		InstructBlipProcessor.from_pretrained(
						    self.tmpdirname      ,		bos_token="(BOS)"      ,		eos_token="(EOS)"      ,		do_normalize=a_      ,		padding_value=1.0							)

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

						self.assertEqual(processor.image_processor.to_json_string()      ,		image_processor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.image_processor      ,		a_							)
						self.assertIsInstance(processor.qformer_tokenizer      ,		a_							)





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		self.get_image_processor()
						__snake_case			=		self.get_tokenizer()
						__snake_case			=		self.get_qformer_tokenizer()

						__snake_case			=		InstructBlipProcessor(
						    tokenizer=a_      ,		image_processor=a_      ,		qformer_tokenizer=a_							)

						__snake_case			=		self.prepare_image_inputs()

						__snake_case			=		image_processor(a_      ,		return_tensors="np"							)
						__snake_case			=		processor(images=a_      ,		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 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		self.get_image_processor()
						__snake_case			=		self.get_tokenizer()
						__snake_case			=		self.get_qformer_tokenizer()

						__snake_case			=		InstructBlipProcessor(
						    tokenizer=a_      ,		image_processor=a_      ,		qformer_tokenizer=a_							)

						__snake_case			=		"lower newer"

						__snake_case			=		processor(text=a_							)

						__snake_case			=		tokenizer(a_      ,		return_token_type_ids=a_							)
						__snake_case			=		qformer_tokenizer(a_      ,		return_token_type_ids=a_							)

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

						for key in encoded_tokens_qformer.keys():
									self.assertListEqual(encoded_tokens_qformer[key]      ,		encoded_processor["qformer_" + key]							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		self.get_image_processor()
						__snake_case			=		self.get_tokenizer()
						__snake_case			=		self.get_qformer_tokenizer()

						__snake_case			=		InstructBlipProcessor(
						    tokenizer=a_      ,		image_processor=a_      ,		qformer_tokenizer=a_							)

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

						__snake_case			=		processor(text=a_      ,		images=a_							)

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

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





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		self.get_image_processor()
						__snake_case			=		self.get_tokenizer()
						__snake_case			=		self.get_qformer_tokenizer()

						__snake_case			=		InstructBlipProcessor(
						    tokenizer=a_      ,		image_processor=a_      ,		qformer_tokenizer=a_							)

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

						__snake_case			=		processor.batch_decode(a_							)
						__snake_case			=		tokenizer.batch_decode(a_							)

						self.assertListEqual(a_      ,		a_							)






			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		self.get_image_processor()
						__snake_case			=		self.get_tokenizer()
						__snake_case			=		self.get_qformer_tokenizer()

						__snake_case			=		InstructBlipProcessor(
						    tokenizer=a_      ,		image_processor=a_      ,		qformer_tokenizer=a_							)

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

						__snake_case			=		processor(text=a_      ,		images=a_							)

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











'''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()      )

