





"""simple docstring"""


import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import GPTaLMHeadModel



@require_torch
@require_sentencepiece
@require_tokenizers
class      lowercase_				(     unittest.TestCase							):

       @slow
       @unittest.skip("Model is not available."     )
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"nvidia/megatron-gpt2-345m/"
        if "MYDIR" in os.environ:
         snake_case__    							=				os.path.join(os.environ["MYDIR"]   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
        model.to(__SCREAMING_SNAKE_CASE     )
        model.half()

        snake_case__    							=				torch.tensor(
            [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]   ,					device=__SCREAMING_SNAKE_CASE   ,					dtype=torch.long   ,					)

        with torch.no_grad():
         snake_case__    							=				model(__SCREAMING_SNAKE_CASE     ).logits

        snake_case__    							=				torch.Size((1, 9, 5_0257)     )
        self.assertEqual(output.shape   ,					__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				torch.tensor(
            [
                4.9414,
                -0.2920,
                -1.2148,
                -4.0273,
                -0.5161,
                -5.2109,
                -1.2412,
                -1.8301,
                -1.7734,
                -4.7148,
                -0.2317,
                -1.0811,
                -2.1777,
                0.4141,
                -3.7969,
                -4.0586,
                -2.5332,
                -3.3809,
                4.3867,
            ]   ,					device=__SCREAMING_SNAKE_CASE   ,					dtype=torch.half   ,					)

        for i in range(19     ):
         snake_case__							,   snake_case__    							=				8 * i // 17, 2792 * i  # along the diagonal
         snake_case__							,   snake_case__    							=				output[0, r, c], expected_diag[i]
         snake_case__    							=				F"row={r} col={c} computed={computed} expected={expected}"
         self.assertAlmostEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					delta=1e-4   ,					msg=__SCREAMING_SNAKE_CASE     )








"""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      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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   ,					__SCREAMING_SNAKE_CASE     )
        with open(self.image_processor_file   ,					"w"   ,					encoding="utf-8"     ) as fp:
         json.dump(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return BertTokenizer.from_pretrained(self.tmpdirname   ,					**__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return BertTokenizerFast.from_pretrained(self.tmpdirname   ,					**__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return FlavaImageProcessor.from_pretrained(self.tmpdirname   ,					**__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

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

        return image_inputs

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_tokenizer()
        snake_case__    							=				self.get_rust_tokenizer()
        snake_case__    							=				self.get_image_processor()

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

        snake_case__    							=				FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					image_processor=__SCREAMING_SNAKE_CASE     )
        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   ,					__SCREAMING_SNAKE_CASE     )
        self.assertIsInstance(processor_fast.tokenizer   ,					__SCREAMING_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   ,					__SCREAMING_SNAKE_CASE     )
        self.assertIsInstance(processor_fast.image_processor   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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=__SCREAMING_SNAKE_CASE   ,					padding_value=1.0     )

        snake_case__    							=				FlavaProcessor.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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

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

        snake_case__    							=				self.prepare_image_inputs()

        snake_case__    							=				image_processor(__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        snake_case__    							=				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     )

        # With rest of the args
        random.seed(1234     )
        snake_case__    							=				image_processor(
            __SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        random.seed(1234     )
        snake_case__    							=				processor(
            images=__SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

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

        snake_case__    							=				"lower newer"

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				tokenizer(__SCREAMING_SNAKE_CASE     )

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

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

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

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

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

        # add extra args
        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE   ,					images=__SCREAMING_SNAKE_CASE   ,					return_codebook_pixels=__SCREAMING_SNAKE_CASE   ,					return_image_mask=__SCREAMING_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(__SCREAMING_SNAKE_CASE     ):
         processor()

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

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

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

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

        self.assertListEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_image_processor()
        snake_case__    							=				self.get_tokenizer()

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

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

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

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

