
'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					_UpperCamelCase											=			False

if is_vision_available():
					from PIL import Image

					from transformers import (
					    AutoProcessor,
					    PixaStructImageProcessor,
					    PixaStructProcessor,
					    PreTrainedTokenizerFast,
					    TaTokenizer,
					)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,				reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."	,				)
@require_vision
@require_torch
class _A  (    unittest.TestCase    ):





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     tempfile.mkdtemp()

					__UpperCAmelCase   :      Dict										=     PixaStructImageProcessor()
					__UpperCAmelCase   :      Optional[int]										=     TaTokenizer.from_pretrained("""t5-small"""				)

					__UpperCAmelCase   :      str										=     PixaStructProcessor(__UpperCAmelCase ,       __UpperCAmelCase				)

					processor.save_pretrained(self.tmpdirname				)





		def 							__A       (     self ,       **__UpperCAmelCase				)       ->     Dict:

					'''simple docstring'''
					return AutoProcessor.from_pretrained(self.tmpdirname ,       **__UpperCAmelCase				).tokenizer





		def 							__A       (     self ,       **__UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					return AutoProcessor.from_pretrained(self.tmpdirname ,       **__UpperCAmelCase				).image_processor





		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					shutil.rmtree(self.tmpdirname				)





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     [np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta				)]

					__UpperCAmelCase   :      List[Any]										=     [Image.fromarray(np.moveaxis(__UpperCAmelCase ,       0 ,       -1				)				) for x in image_inputs]

					return image_inputs





		def 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     PixaStructProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()				)
					processor.save_pretrained(self.tmpdirname				)

					__UpperCAmelCase   :      Union[str, Any]										=     self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""				)
					__UpperCAmelCase   :      List[str]										=     self.get_image_processor(do_normalize=__UpperCAmelCase ,       padding_value=1.0				)

					__UpperCAmelCase   :      Optional[int]										=     PixaStructProcessor.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				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.get_image_processor()
					__UpperCAmelCase   :      int										=     self.get_tokenizer()

					__UpperCAmelCase   :      List[str]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Dict										=     self.prepare_image_inputs()

					__UpperCAmelCase   :      Union[str, Any]										=     image_processor(__UpperCAmelCase ,       return_tensors="""np"""				)
					__UpperCAmelCase   :      int										=     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				)





		def 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     self.get_image_processor()
					__UpperCAmelCase   :      int										=     self.get_tokenizer()

					__UpperCAmelCase   :      Optional[int]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[int]										=     """lower newer"""

					__UpperCAmelCase   :      List[Any]										=     processor(text=__UpperCAmelCase				)

					__UpperCAmelCase   :      Dict										=     tokenizer(__UpperCAmelCase ,       return_token_type_ids=__UpperCAmelCase ,       add_special_tokens=__UpperCAmelCase				)

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





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.get_image_processor()
					__UpperCAmelCase   :      Dict										=     self.get_tokenizer()

					__UpperCAmelCase   :      Any										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     """lower newer"""
					__UpperCAmelCase   :      List[str]										=     self.prepare_image_inputs()

					__UpperCAmelCase   :      Tuple										=     processor(text=__UpperCAmelCase ,       images=__UpperCAmelCase				)

					self.assertListEqual(
					    list(inputs.keys()				) ,       ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]				)

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





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     self.get_image_processor()
					__UpperCAmelCase   :      List[str]										=     self.get_tokenizer()

					__UpperCAmelCase   :      Optional[int]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Any										=     """lower newer"""
					__UpperCAmelCase   :      str										=     self.prepare_image_inputs()

					__UpperCAmelCase   :      Dict										=     processor(text=__UpperCAmelCase ,       images=__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     [512, 1_024, 2_048, 4_096]
					__UpperCAmelCase   :      Dict										=     [770, 770, 770, 770]
					# with text
					for i, max_patch in enumerate(__UpperCAmelCase				):
								__UpperCAmelCase   :      List[Any]										=     processor(text=__UpperCAmelCase ,       images=__UpperCAmelCase ,       max_patches=__UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[0] ,       __UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[1] ,       expected_hidden_size[i]				)

					# without text input
					for i, max_patch in enumerate(__UpperCAmelCase				):
								__UpperCAmelCase   :      List[str]										=     processor(images=__UpperCAmelCase ,       max_patches=__UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[0] ,       __UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[1] ,       expected_hidden_size[i]				)





		def 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.get_image_processor()
					__UpperCAmelCase   :      List[str]										=     self.get_tokenizer()

					__UpperCAmelCase   :      Any										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[Any]										=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase   :      Tuple										=     processor.batch_decode(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     tokenizer.batch_decode(__UpperCAmelCase				)

					self.assertListEqual(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     self.get_image_processor()
					__UpperCAmelCase   :      Union[str, Any]										=     self.get_tokenizer()

					__UpperCAmelCase   :      Union[str, Any]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Dict										=     """lower newer"""
					__UpperCAmelCase   :      Tuple										=     self.prepare_image_inputs()

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

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(
					    list(inputs.keys()				) ,       ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]				)

					__UpperCAmelCase   :      List[str]										=     processor(text=__UpperCAmelCase				)

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









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     vocab_size
					__UpperCAmelCase   :      Optional[int]										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     hidden_act
					__UpperCAmelCase   :      List[Any]										=     intermediate_size
					__UpperCAmelCase   :      Optional[Any]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Dict										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      int										=     max_position_embeddings
					__UpperCAmelCase   :      str										=     type_vocab_size
					__UpperCAmelCase   :      int										=     task_type_vocab_size
					__UpperCAmelCase   :      List[str]										=     use_task_id
					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      List[str]										=     position_embedding_type
					__UpperCAmelCase   :      Union[str, Any]										=     use_cache
					__UpperCAmelCase   :      Optional[int]										=     classifier_dropout



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase   :      Union[str, Any]										=     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					        ("""task_type_ids""", dynamic_axis),
					    ]				)




