
"""simple docstring"""


import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import FlavaImageProcessor, FlavaProcessor
		from transformers.models.flava.image_processing_flava import (
		    FLAVA_CODEBOOK_MEAN,
		    FLAVA_CODEBOOK_STD,
		    FLAVA_IMAGE_MEAN,
		    FLAVA_IMAGE_STD,
		)







@require_vision
class 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				Dict:
							A  				=				tempfile.mkdtemp()

							# fmt: off
							A  				=				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
							# fmt: on
							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file'] )

							with open(self.vocab_file					,'w'					,encoding='utf-8' ) as fp:
								fp.write(''.join([x + '\n' for x in vocab_tokens] ) )

							A  				=				{
							    'image_mean': FLAVA_IMAGE_MEAN,
							    'image_std': FLAVA_IMAGE_STD,
							    'do_normalize': True,
							    'do_resize': True,
							    'size': 224,
							    'do_center_crop': True,
							    'crop_size': 224,
							    'input_size_patches': 14,
							    'total_mask_patches': 75,
							    'mask_group_max_patches': None,
							    'mask_group_min_patches': 16,
							    'mask_group_min_aspect_ratio': 0.3,
							    'mask_group_max_aspect_ratio': None,
							    'codebook_do_resize': True,
							    'codebook_size': 112,
							    'codebook_do_center_crop': True,
							    'codebook_crop_size': 112,
							    'codebook_do_map_pixels': True,
							    'codebook_do_normalize': True,
							    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
							    'codebook_image_std': FLAVA_CODEBOOK_STD,
							}

							A  				=				os.path.join(self.tmpdirname					,A_ )
							with open(self.image_processor_file					,'w'					,encoding='utf-8' ) as fp:
								json.dump(A_					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,**A_       : int )       ->				Optional[int]:
							return BertTokenizer.from_pretrained(self.tmpdirname					,**A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,**A_       : List[Any] )       ->				Optional[int]:
							return BertTokenizerFast.from_pretrained(self.tmpdirname					,**A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,**A_       : str )       ->				List[Any]:
							return FlavaImageProcessor.from_pretrained(self.tmpdirname					,**A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				Union[str, Any]:
							shutil.rmtree(self.tmpdirname )


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

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

							return image_inputs


						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()
							A  				=				self.get_image_processor()

							A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )
							processor_slow.save_pretrained(self.tmpdirname )
							A  				=				FlavaProcessor.from_pretrained(self.tmpdirname					,use_fast=A_ )

							A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )
							processor_fast.save_pretrained(self.tmpdirname )
							A  				=				FlavaProcessor.from_pretrained(self.tmpdirname )

							self.assertEqual(processor_slow.tokenizer.get_vocab()					,tokenizer_slow.get_vocab() )
							self.assertEqual(processor_fast.tokenizer.get_vocab()					,tokenizer_fast.get_vocab() )
							self.assertEqual(tokenizer_slow.get_vocab()					,tokenizer_fast.get_vocab() )
							self.assertIsInstance(processor_slow.tokenizer					,A_ )
							self.assertIsInstance(processor_fast.tokenizer					,A_ )

							self.assertEqual(processor_slow.image_processor.to_json_string()					,image_processor.to_json_string() )
							self.assertEqual(processor_fast.image_processor.to_json_string()					,image_processor.to_json_string() )
							self.assertIsInstance(processor_slow.image_processor					,A_ )
							self.assertIsInstance(processor_fast.image_processor					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Tuple:
							A  				=				FlavaProcessor(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=A_					,padding_value=1.0 )

							A  				=				FlavaProcessor.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_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Any:
							A  				=				self.get_image_processor()
							A  				=				self.get_tokenizer()

							A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

							A  				=				self.prepare_image_inputs()

							A  				=				image_processor(A_					,return_tensors='np' )
							A  				=				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 )

							# With rest of the args
							random.seed(1234 )
							A  				=				image_processor(
							    A_					,return_image_mask=A_					,return_codebook_pixels=A_					,return_tensors='np' )
							random.seed(1234 )
							A  				=				processor(
							    images=A_					,return_image_mask=A_					,return_codebook_pixels=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   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				str:
							A  				=				self.get_image_processor()
							A  				=				self.get_tokenizer()

							A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

							A  				=				'lower newer'

							A  				=				processor(text=A_ )

							A  				=				tokenizer(A_ )

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


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

							A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

							A  				=				'lower newer'
							A  				=				self.prepare_image_inputs()

							A  				=				processor(text=A_					,images=A_ )

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

							# add extra args
							A  				=				processor(text=A_					,images=A_					,return_codebook_pixels=A_					,return_image_mask=A_ )

							self.assertListEqual(
							    list(inputs.keys() )					,[
							        'input_ids',
							        'token_type_ids',
							        'attention_mask',
							        'pixel_values',
							        'codebook_pixel_values',
							        'bool_masked_pos',
							    ]					,)

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


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Optional[int]:
							A  				=				self.get_image_processor()
							A  				=				self.get_tokenizer()

							A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

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

							A  				=				processor.batch_decode(A_ )
							A  				=				tokenizer.batch_decode(A_ )

							self.assertListEqual(A_					,A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				List[Any]:
							A  				=				self.get_image_processor()
							A  				=				self.get_tokenizer()

							A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

							A  				=				'lower newer'
							A  				=				self.prepare_image_inputs()

							A  				=				processor(text=A_					,images=A_ )

							self.assertListEqual(list(inputs.keys() )					,processor.model_input_names )

"""simple docstring"""


from typing import Callable, Optional, Union

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


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      int =      '''prophetnet'''
						_lowerCamelCase:      Optional[Any] =      ['''past_key_values''']
						_lowerCamelCase:      Dict =      {
						    '''num_attention_heads''': '''num_encoder_attention_heads''',
						}


						def __init__(	self       : Tuple					,A_       : Optional[float] = 0.1					,A_       : Optional[Union[str, Callable]] = "gelu"					,A_       : Optional[int] = 3_0522					,A_       : Optional[int] = 1024					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[float] = 0.1					,A_       : Optional[float] = 0.1					,A_       : Optional[int] = 512					,A_       : Optional[float] = 0.02					,A_       : Optional[bool] = True					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 2					,A_       : Optional[int] = 32					,A_       : Optional[int] = 128					,A_       : Optional[bool] = False					,A_       : Optional[float] = 0.0					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 1					,A_       : Optional[int] = 2					,**A_       : Tuple					,)       ->				Any:
							A  				=				vocab_size
							A  				=				hidden_size
							A  				=				encoder_ffn_dim
							A  				=				num_encoder_layers
							A  				=				num_encoder_attention_heads
							A  				=				decoder_ffn_dim
							A  				=				num_decoder_layers
							A  				=				num_decoder_attention_heads
							A  				=				max_position_embeddings
							A  				=				init_std  # Normal(0, this parameter)
							A  				=				activation_function

							# parameters for prophetnet
							A  				=				ngram
							A  				=				num_buckets
							A  				=				relative_max_distance
							A  				=				disable_ngram_loss
							A  				=				eps

							# 3 Types of Dropout
							A  				=				attention_dropout
							A  				=				activation_dropout
							A  				=				dropout

							A  				=				use_cache

							super().__init__(
							    pad_token_id=A_					,bos_token_id=A_					,eos_token_id=A_					,is_encoder_decoder=A_					,add_cross_attention=A_					,decoder_start_token_id=A_					,**A_					,)


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							return self.num_encoder_layers + self.num_decoder_layers




						@num_hidden_layers.setter
						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Any )       ->				int:
							raise NotImplementedError(
							    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
							    ' `num_decoder_layers`.' )