




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class 							__SCREAMING_SNAKE_CASE				(						unittest.TestCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				tempfile.mkdtemp()

						lowerCamelCase_				:						List[Any]				  =				CLIPImageProcessor()
						lowerCamelCase_				:						Union[str, Any]				  =				BertTokenizer.from_pretrained(
						    'hf-internal-testing/tiny-random-BertModel'					,			model_input_names=['input_ids', 'attention_mask']   )

						lowerCamelCase_				:						Tuple				  =				GitProcessor(__lowercase					,			__lowercase   )

						processor.save_pretrained(self.tmpdirname   )

					def       lowercase_				(							self					,			**__lowercase   ):







						'''simple docstring'''




						return AutoProcessor.from_pretrained(self.tmpdirname					,			**__lowercase   ).tokenizer

					def       lowercase_				(							self					,			**__lowercase   ):







						'''simple docstring'''




						return AutoProcessor.from_pretrained(self.tmpdirname					,			**__lowercase   ).image_processor

					def       lowercase_				(							self   ):







						'''simple docstring'''




						shutil.rmtree(self.tmpdirname   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				[np.random.randint(255					,			size=(3, 30, 400)					,			dtype=np.uinta   )]

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

						return image_inputs

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				GitProcessor(tokenizer=self.get_tokenizer()					,			image_processor=self.get_image_processor()   )
						processor.save_pretrained(self.tmpdirname   )

						lowerCamelCase_				:						str				  =				self.get_tokenizer(bos_token='(BOS)'					,			eos_token='(EOS)'   )
						lowerCamelCase_				:						Any				  =				self.get_image_processor(do_normalize=__lowercase					,			padding_value=1.0   )

						lowerCamelCase_				:						Optional[Any]				  =				GitProcessor.from_pretrained(
						    self.tmpdirname					,			bos_token='(BOS)'					,			eos_token='(EOS)'					,			do_normalize=__lowercase					,			padding_value=1.0   )

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

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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				self.get_image_processor()
						lowerCamelCase_				:						Any				  =				self.get_tokenizer()

						lowerCamelCase_				:						str				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						Tuple				  =				self.prepare_image_inputs()

						lowerCamelCase_				:						Union[str, Any]				  =				image_processor(__lowercase					,			return_tensors='np'   )
						lowerCamelCase_				:						Optional[int]				  =				processor(images=__lowercase					,			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   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				self.get_image_processor()
						lowerCamelCase_				:						List[Any]				  =				self.get_tokenizer()

						lowerCamelCase_				:						Optional[int]				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						Optional[int]				  =				'lower newer'

						lowerCamelCase_				:						Optional[Any]				  =				processor(text=__lowercase   )

						lowerCamelCase_				:						int				  =				tokenizer(__lowercase					,			return_token_type_ids=__lowercase   )

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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.get_image_processor()
						lowerCamelCase_				:						str				  =				self.get_tokenizer()

						lowerCamelCase_				:						str				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				'lower newer'
						lowerCamelCase_				:						Tuple				  =				self.prepare_image_inputs()

						lowerCamelCase_				:						List[str]				  =				processor(text=__lowercase					,			images=__lowercase   )

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

						# test if it raises when no input is passed
						with pytest.raises(__lowercase   ):
							processor()

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.get_image_processor()
						lowerCamelCase_				:						str				  =				self.get_tokenizer()

						lowerCamelCase_				:						Dict				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						str				  =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCamelCase_				:						Any				  =				processor.batch_decode(__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				tokenizer.batch_decode(__lowercase   )

						self.assertListEqual(__lowercase					,			__lowercase   )





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				self.get_image_processor()
						lowerCamelCase_				:						List[str]				  =				self.get_tokenizer()

						lowerCamelCase_				:						Optional[Any]				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

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

						lowerCamelCase_				:						List[str]				  =				processor(text=__lowercase					,			images=__lowercase   )

						# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
						self.assertListEqual(list(inputs.keys()   )					,			['input_ids', 'attention_mask', 'pixel_values']   )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__						=		{
    'configuration_prophetnet': ['PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ProphetNetConfig'],
    'tokenization_prophetnet': ['ProphetNetTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ProphetNetDecoder',
							    'ProphetNetEncoder',
							    'ProphetNetForCausalLM',
							    'ProphetNetForConditionalGeneration',
							    'ProphetNetModel',
							    'ProphetNetPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
							from .tokenization_prophetnet import ProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_prophetnet import (
														    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ProphetNetDecoder,
														    ProphetNetEncoder,
														    ProphetNetForCausalLM,
														    ProphetNetForConditionalGeneration,
														    ProphetNetModel,
														    ProphetNetPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
