



"""simple docstring"""







import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[str]							  =     "data2vec-audio"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 512, 512, 512)						,	SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)						,	SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=19						,	SCREAMING_SNAKE_CASE__=5						,	SCREAMING_SNAKE_CASE__=0.0_5						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__="sum"						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 1500)						,	SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Dict:
				super().__init__(**SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__		)
				A__											=					hidden_size
				A__											=					feat_extract_activation
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					conv_bias
				A__											=					num_conv_pos_embeddings
				A__											=					num_conv_pos_embedding_groups
				A__											=					conv_pos_kernel_size
				A__											=					len(self.conv_dim		)
				A__											=					num_hidden_layers
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					num_attention_heads
				A__											=					hidden_dropout
				A__											=					attention_dropout
				A__											=					activation_dropout
				A__											=					feat_proj_dropout
				A__											=					final_dropout
				A__											=					layerdrop
				A__											=					layer_norm_eps
				A__											=					initializer_range
				A__											=					vocab_size
				A__											=					use_weighted_layer_sum

				if (
				    (len(self.conv_stride		) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
				    or (len(self.conv_dim		) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
					    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
					    f""" {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				A__											=					mask_time_prob
				A__											=					mask_time_length
				A__											=					mask_time_min_masks
				A__											=					mask_feature_prob
				A__											=					mask_feature_length
				A__											=					mask_feature_min_masks

				# ctc loss
				A__											=					ctc_loss_reduction
				A__											=					ctc_zero_infinity

				# adapter
				A__											=					add_adapter
				A__											=					adapter_kernel_size
				A__											=					adapter_stride
				A__											=					num_adapter_layers
				A__											=					output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				A__											=					classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					xvector_output_dim







			@property
			def   snake_case__	(      self		)      ->	List[str]:
				return math.prod(self.conv_stride		)









"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

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

				A__											=					BlipaProcessor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				processor.save_pretrained(self.tmpdirname		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).tokenizer


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).image_processor


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


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]

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

				return image_inputs


			def   snake_case__	(      self		)      ->	str:
				A__											=					BlipaProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

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

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

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

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


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	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   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	return_token_type_ids=SCREAMING_SNAKE_CASE__		)

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


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

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

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

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

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

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

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

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




