
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 					__a						( self					)    ->				Optional[int]:
							lowerCAmelCase_      =     tempfile.mkdtemp()

							# fmt: off
							lowerCAmelCase_      =     ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
							# fmt: on
							lowerCAmelCase_      =     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_      =     {
							    "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_      =     os.path.join(self.tmpdirname    ,    _UpperCamelCase					)
							with open(self.image_processor_file    ,    "w"    ,    encoding="utf-8"					) as fp:
										json.dump(_UpperCamelCase    ,    _UpperCamelCase					)




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




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				Optional[int]:
							return BertTokenizerFast.from_pretrained(self.tmpdirname    ,    **_UpperCamelCase					)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				Tuple:
							return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,    **_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							shutil.rmtree(self.tmpdirname					)




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

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

							return image_inputs




				def 					__a						( self					)    ->				Any:
							lowerCAmelCase_      =     self.get_tokenizer()
							lowerCAmelCase_      =     self.get_rust_tokenizer()
							lowerCAmelCase_      =     self.get_image_processor()

							lowerCAmelCase_      =     FlavaProcessor(tokenizer=_UpperCamelCase    ,    image_processor=_UpperCamelCase					)
							processor_slow.save_pretrained(self.tmpdirname					)
							lowerCAmelCase_      =     FlavaProcessor.from_pretrained(self.tmpdirname    ,    use_fast=_UpperCamelCase					)

							lowerCAmelCase_      =     FlavaProcessor(tokenizer=_UpperCamelCase    ,    image_processor=_UpperCamelCase					)
							processor_fast.save_pretrained(self.tmpdirname					)
							lowerCAmelCase_      =     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 					__a						( self					)    ->				List[str]:
							lowerCAmelCase_      =     FlavaProcessor(tokenizer=self.get_tokenizer()    ,    image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

							lowerCAmelCase_      =     self.get_tokenizer(bos_token="(BOS)"    ,    eos_token="(EOS)"					)
							lowerCAmelCase_      =     self.get_image_processor(do_normalize=_UpperCamelCase    ,    padding_value=1.0					)

							lowerCAmelCase_      =     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 					__a						( self					)    ->				Dict:
							lowerCAmelCase_      =     self.get_image_processor()
							lowerCAmelCase_      =     self.get_tokenizer()

							lowerCAmelCase_      =     FlavaProcessor(tokenizer=_UpperCamelCase    ,    image_processor=_UpperCamelCase					)

							lowerCAmelCase_      =     self.prepare_image_inputs()

							lowerCAmelCase_      =     image_processor(_UpperCamelCase    ,    return_tensors="np"					)
							lowerCAmelCase_      =     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(1_234					)
							lowerCAmelCase_      =     image_processor(
							    _UpperCamelCase    ,    return_image_mask=_UpperCamelCase    ,    return_codebook_pixels=_UpperCamelCase    ,    return_tensors="np"					)
							random.seed(1_234					)
							lowerCAmelCase_      =     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 					__a						( self					)    ->				Dict:
							lowerCAmelCase_      =     self.get_image_processor()
							lowerCAmelCase_      =     self.get_tokenizer()

							lowerCAmelCase_      =     FlavaProcessor(tokenizer=_UpperCamelCase    ,    image_processor=_UpperCamelCase					)

							lowerCAmelCase_      =     "lower newer"

							lowerCAmelCase_      =     processor(text=_UpperCamelCase					)

							lowerCAmelCase_      =     tokenizer(_UpperCamelCase					)

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




				def 					__a						( self					)    ->				Any:
							lowerCAmelCase_      =     self.get_image_processor()
							lowerCAmelCase_      =     self.get_tokenizer()

							lowerCAmelCase_      =     FlavaProcessor(tokenizer=_UpperCamelCase    ,    image_processor=_UpperCamelCase					)

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

							lowerCAmelCase_      =     processor(text=_UpperCamelCase    ,    images=_UpperCamelCase					)

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

							# add extra args
							lowerCAmelCase_      =     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 					__a						( self					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     self.get_image_processor()
							lowerCAmelCase_      =     self.get_tokenizer()

							lowerCAmelCase_      =     FlavaProcessor(tokenizer=_UpperCamelCase    ,    image_processor=_UpperCamelCase					)

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

							lowerCAmelCase_      =     processor.batch_decode(_UpperCamelCase					)
							lowerCAmelCase_      =     tokenizer.batch_decode(_UpperCamelCase					)

							self.assertListEqual(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     self.get_image_processor()
							lowerCAmelCase_      =     self.get_tokenizer()

							lowerCAmelCase_      =     FlavaProcessor(tokenizer=_UpperCamelCase    ,    image_processor=_UpperCamelCase					)

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

							lowerCAmelCase_      =     processor(text=_UpperCamelCase    ,    images=_UpperCamelCase					)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_A       =					{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					["XLMProphetNetTokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLMProphetNetDecoder",
			    "XLMProphetNetEncoder",
			    "XLMProphetNetForCausalLM",
			    "XLMProphetNetForConditionalGeneration",
			    "XLMProphetNetModel",
			    "XLMProphetNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_prophetnet import (
						    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMProphetNetDecoder,
						    XLMProphetNetEncoder,
						    XLMProphetNetForCausalLM,
						    XLMProphetNetForConditionalGeneration,
						    XLMProphetNetModel,
						    XLMProphetNetPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



