




'''simple docstring'''




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


a__				: Tuple									=    logging.get_logger(__name__)

a__				: Union[str, Any]									=    {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}






class 							__snake_case    (				SCREAMING_SNAKE_CASE__       ):
	__lowerCAmelCase						=     'pegasus_x'
	__lowerCAmelCase						=     ['past_key_values']
	__lowerCAmelCase						=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





	def __init__(		self			,					UpperCamelCase_=9_6103			,					UpperCamelCase_=1_6384			,					UpperCamelCase_=16			,					UpperCamelCase_=4096			,					UpperCamelCase_=16			,					UpperCamelCase_=16			,					UpperCamelCase_=4096			,					UpperCamelCase_=16			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_="gelu"			,					UpperCamelCase_=1024			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=0			,					UpperCamelCase_=True			,					UpperCamelCase_=0			,					UpperCamelCase_=1			,					UpperCamelCase_=1			,					UpperCamelCase_=32			,					UpperCamelCase_=512			,					UpperCamelCase_=True			,					**UpperCamelCase_			,					)			->		Optional[int]:
		snake_case__  					=      vocab_size
		snake_case__  					=      max_position_embeddings
		snake_case__  					=      d_model
		snake_case__  					=      encoder_ffn_dim
		snake_case__  					=      encoder_layers
		snake_case__  					=      encoder_attention_heads
		snake_case__  					=      decoder_ffn_dim
		snake_case__  					=      decoder_layers
		snake_case__  					=      decoder_attention_heads
		snake_case__  					=      dropout
		snake_case__  					=      attention_dropout
		snake_case__  					=      activation_dropout
		snake_case__  					=      activation_function
		snake_case__  					=      init_std
		snake_case__  					=      encoder_layerdrop
		snake_case__  					=      decoder_layerdrop
		snake_case__  					=      use_cache
		snake_case__  					=      encoder_layers
		snake_case__  					=      scale_embedding  # scale factor will be sqrt(d_model) if True

		snake_case__  					=      num_global_tokens
		snake_case__  					=      block_size
		snake_case__  					=      stagger_local_blocks

		super().__init__(
		    pad_token_id=a_			,					eos_token_id=a_			,					is_encoder_decoder=a_			,					decoder_start_token_id=a_			,					forced_eos_token_id=a_			,					**a_			,					)





	@property
	def      _snake_case      (		self		)			->		Union[str, Any]:
		return self.encoder_attention_heads





	@property
	def      _snake_case      (		self		)			->		List[str]:
		return self.d_model










'''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 							__snake_case    (				unittest.TestCase       ):





	def      _snake_case      (		self		)			->		Optional[Any]:
		snake_case__  					=      tempfile.mkdtemp()

		# fmt: off
		snake_case__  					=      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
		# fmt: on
		snake_case__  					=      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]		)		)

		snake_case__  					=      {
		    '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,
		}

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





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
		return BertTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[Any]:
		return BertTokenizerFast.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
		return FlavaImageProcessor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





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





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

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

		return image_inputs





	def      _snake_case      (		self		)			->		Optional[int]:
		snake_case__  					=      self.get_tokenizer()
		snake_case__  					=      self.get_rust_tokenizer()
		snake_case__  					=      self.get_image_processor()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
		processor_slow.save_pretrained(self.tmpdirname		)
		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname			,					use_fast=UpperCamelCase_		)

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
		processor_fast.save_pretrained(self.tmpdirname		)
		snake_case__  					=      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			,					UpperCamelCase_		)
		self.assertIsInstance(processor_fast.tokenizer			,					UpperCamelCase_		)

		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			,					UpperCamelCase_		)
		self.assertIsInstance(processor_fast.image_processor			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      FlavaProcessor(tokenizer=self.get_tokenizer()			,					image_processor=self.get_image_processor()		)
		processor.save_pretrained(self.tmpdirname		)

		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
		snake_case__  					=      self.get_image_processor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)

		snake_case__  					=      FlavaProcessor.from_pretrained(
		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)

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

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





	def      _snake_case      (		self		)			->		Union[str, Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      self.prepare_image_inputs()

		snake_case__  					=      image_processor(UpperCamelCase_			,					return_tensors='np'		)
		snake_case__  					=      processor(images=UpperCamelCase_			,					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		)
		snake_case__  					=      image_processor(
		    UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
		random.seed(1234		)
		snake_case__  					=      processor(
		    images=UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					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		)			->		Dict:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      'lower newer'

		snake_case__  					=      processor(text=UpperCamelCase_		)

		snake_case__  					=      tokenizer(UpperCamelCase_		)

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





	def      _snake_case      (		self		)			->		Union[str, Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

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

		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)

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

		# add extra args
		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_image_mask=UpperCamelCase_		)

		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(UpperCamelCase_		):
			processor()





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

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

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

		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

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

		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)

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



