





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""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     )

