


'''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				):



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     List[str]   				=			tempfile.mkdtemp()

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

		lowerCAmelCase       :     Union[str, Any]   				=			{
		    '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,
		}

		lowerCAmelCase       :     Union[str, Any]   				=			os.path.join(self.tmpdirname			,       snake_case__     )
		with open(self.image_processor_file			,       'w'			,       encoding='utf-8'     ) as fp:
			json.dump(snake_case__			,       snake_case__     )



	def 					lowercase  (       self			,       **snake_case__     ):
		return BertTokenizer.from_pretrained(self.tmpdirname			,       **snake_case__     )



	def 					lowercase  (       self			,       **snake_case__     ):
		return BertTokenizerFast.from_pretrained(self.tmpdirname			,       **snake_case__     )



	def 					lowercase  (       self			,       **snake_case__     ):
		return FlavaImageProcessor.from_pretrained(self.tmpdirname			,       **snake_case__     )



	def 					lowercase  (       self     ):
		shutil.rmtree(self.tmpdirname     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     int   				=			[np.random.randint(255			,       size=(3, 30, 400)			,       dtype=np.uinta     )]

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

		return image_inputs



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Dict   				=			self.get_tokenizer()
		lowerCAmelCase       :     Dict   				=			self.get_rust_tokenizer()
		lowerCAmelCase       :     Optional[Any]   				=			self.get_image_processor()

		lowerCAmelCase       :     Dict   				=			FlavaProcessor(tokenizer=snake_case__			,       image_processor=snake_case__     )
		processor_slow.save_pretrained(self.tmpdirname     )
		lowerCAmelCase       :     Union[str, Any]   				=			FlavaProcessor.from_pretrained(self.tmpdirname			,       use_fast=snake_case__     )

		lowerCAmelCase       :     Optional[Any]   				=			FlavaProcessor(tokenizer=snake_case__			,       image_processor=snake_case__     )
		processor_fast.save_pretrained(self.tmpdirname     )
		lowerCAmelCase       :     str   				=			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			,       snake_case__     )
		self.assertIsInstance(processor_fast.tokenizer			,       snake_case__     )

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Optional[int]   				=			FlavaProcessor(tokenizer=self.get_tokenizer()			,       image_processor=self.get_image_processor()     )
		processor.save_pretrained(self.tmpdirname     )

		lowerCAmelCase       :     Optional[int]   				=			self.get_tokenizer(bos_token='(BOS)'			,       eos_token='(EOS)'     )
		lowerCAmelCase       :     Optional[Any]   				=			self.get_image_processor(do_normalize=snake_case__			,       padding_value=1.0     )

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

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

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Dict   				=			self.get_image_processor()
		lowerCAmelCase       :     Optional[int]   				=			self.get_tokenizer()

		lowerCAmelCase       :     Any   				=			FlavaProcessor(tokenizer=snake_case__			,       image_processor=snake_case__     )

		lowerCAmelCase       :     str   				=			self.prepare_image_inputs()

		lowerCAmelCase       :     Union[str, Any]   				=			image_processor(snake_case__			,       return_tensors='np'     )
		lowerCAmelCase       :     Dict   				=			processor(images=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     )

		# With rest of the args
		random.seed(1234     )
		lowerCAmelCase       :     List[Any]   				=			image_processor(
		    snake_case__			,       return_image_mask=snake_case__			,       return_codebook_pixels=snake_case__			,       return_tensors='np'     )
		random.seed(1234     )
		lowerCAmelCase       :     Any   				=			processor(
		    images=snake_case__			,       return_image_mask=snake_case__			,       return_codebook_pixels=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 					lowercase  (       self     ):
		lowerCAmelCase       :     Dict   				=			self.get_image_processor()
		lowerCAmelCase       :     Tuple   				=			self.get_tokenizer()

		lowerCAmelCase       :     Tuple   				=			FlavaProcessor(tokenizer=snake_case__			,       image_processor=snake_case__     )

		lowerCAmelCase       :     int   				=			'lower newer'

		lowerCAmelCase       :     Dict   				=			processor(text=snake_case__     )

		lowerCAmelCase       :     List[str]   				=			tokenizer(snake_case__     )

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Any   				=			self.get_image_processor()
		lowerCAmelCase       :     Union[str, Any]   				=			self.get_tokenizer()

		lowerCAmelCase       :     str   				=			FlavaProcessor(tokenizer=snake_case__			,       image_processor=snake_case__     )

		lowerCAmelCase       :     List[Any]   				=			'lower newer'
		lowerCAmelCase       :     Dict   				=			self.prepare_image_inputs()

		lowerCAmelCase       :     int   				=			processor(text=snake_case__			,       images=snake_case__     )

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

		# add extra args
		lowerCAmelCase       :     Dict   				=			processor(text=snake_case__			,       images=snake_case__			,       return_codebook_pixels=snake_case__			,       return_image_mask=snake_case__     )

		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(snake_case__     ):
			processor()



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Tuple   				=			self.get_image_processor()
		lowerCAmelCase       :     Union[str, Any]   				=			self.get_tokenizer()

		lowerCAmelCase       :     int   				=			FlavaProcessor(tokenizer=snake_case__			,       image_processor=snake_case__     )

		lowerCAmelCase       :     Union[str, Any]   				=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		lowerCAmelCase       :     Union[str, Any]   				=			processor.batch_decode(snake_case__     )
		lowerCAmelCase       :     Optional[Any]   				=			tokenizer.batch_decode(snake_case__     )

		self.assertListEqual(snake_case__			,       snake_case__     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     List[Any]   				=			self.get_image_processor()
		lowerCAmelCase       :     Dict   				=			self.get_tokenizer()

		lowerCAmelCase       :     List[Any]   				=			FlavaProcessor(tokenizer=snake_case__			,       image_processor=snake_case__     )

		lowerCAmelCase       :     Dict   				=			'lower newer'
		lowerCAmelCase       :     Optional[Any]   				=			self.prepare_image_inputs()

		lowerCAmelCase       :     Union[str, Any]   				=			processor(text=snake_case__			,       images=snake_case__     )

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




'''simple docstring'''




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


_lowerCAmelCase	:		List[str]		  =						logging.get_logger(__name__)

_lowerCAmelCase	:		int		  =						{
    'facebook/opt-125m': 'https://huggingface.co/facebook/opt-125m/blob/main/config.json',
    'facebook/opt-350m': 'https://huggingface.co/facebook/opt-350m/blob/main/config.json',
    'facebook/opt-1.3b': 'https://huggingface.co/facebook/opt-1.3b/blob/main/config.json',
    'facebook/opt-2.7b': 'https://huggingface.co/facebook/opt-2.7b/blob/main/config.json',
    'facebook/opt-6.7b': 'https://huggingface.co/facebook/opt-6.7b/blob/main/config.json',
    'facebook/opt-13b': 'https://huggingface.co/facebook/opt-13b/blob/main/config.json',
}



class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       Dict         =			"""opt"""
	_lowerCamelCase :       Optional[Any]         =			["""past_key_values"""]



	def __init__(       self			,       snake_case__=5_0272			,       snake_case__=768			,       snake_case__=12			,       snake_case__=3072			,       snake_case__=2048			,       snake_case__=True			,       snake_case__=False			,       snake_case__=None			,       snake_case__=0.1			,       snake_case__=0.0			,       snake_case__=12			,       snake_case__="relu"			,       snake_case__=0.0			,       snake_case__=0.0_2			,       snake_case__=True			,       snake_case__=1			,       snake_case__=2			,       snake_case__=2			,       snake_case__=True			,       snake_case__=True			,       **snake_case__			,       ):
		super().__init__(
		    pad_token_id=snake_case__			,       bos_token_id=snake_case__			,       eos_token_id=snake_case__			,       **snake_case__			,       )
		lowerCAmelCase       :     Union[str, Any]   				=			vocab_size
		lowerCAmelCase       :     List[Any]   				=			max_position_embeddings
		lowerCAmelCase       :     List[str]   				=			num_attention_heads
		lowerCAmelCase       :     Dict   				=			word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
		lowerCAmelCase       :     Optional[Any]   				=			ffn_dim
		lowerCAmelCase       :     Optional[int]   				=			hidden_size
		lowerCAmelCase       :     List[str]   				=			num_hidden_layers
		lowerCAmelCase       :     Union[str, Any]   				=			dropout
		lowerCAmelCase       :     List[str]   				=			attention_dropout
		lowerCAmelCase       :     Union[str, Any]   				=			activation_function
		lowerCAmelCase       :     Optional[Any]   				=			init_std
		lowerCAmelCase       :     List[str]   				=			layerdrop
		lowerCAmelCase       :     Dict   				=			use_cache
		lowerCAmelCase       :     List[Any]   				=			do_layer_norm_before
		# We keep these variables at `True` for backward compatibility.
		lowerCAmelCase       :     List[str]   				=			enable_bias
		lowerCAmelCase       :     Dict   				=			layer_norm_elementwise_affine

		# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
		# with checkpoints that have been fine-tuned before transformers v4.20.1
		# see https://github.com/facebookresearch/metaseq/pull/164
		lowerCAmelCase       :     Tuple   				=			_remove_final_layer_norm
