
"""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:
			__A 		= 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 	lowerCamelCase__      (      unittest.TestCase       ):





			'''simple docstring'''



			def       _lowerCAmelCase	(							self						)				->  Tuple:
				_lowerCAmelCase			=tempfile.mkdtemp()

				_lowerCAmelCase			=PixaStructImageProcessor()
				_lowerCAmelCase			=TaTokenizer.from_pretrained("""t5-small"""						)

				_lowerCAmelCase			=PixaStructProcessor(__UpperCAmelCase  ,  __UpperCAmelCase						)

				processor.save_pretrained(self.tmpdirname						)



			def       _lowerCAmelCase	(							self  ,  **__UpperCAmelCase						)				->  str:
				return AutoProcessor.from_pretrained(self.tmpdirname  ,  **__UpperCAmelCase						).tokenizer



			def       _lowerCAmelCase	(							self  ,  **__UpperCAmelCase						)				->  Optional[int]:
				return AutoProcessor.from_pretrained(self.tmpdirname  ,  **__UpperCAmelCase						).image_processor



			def       _lowerCAmelCase	(							self						)				->  str:
				shutil.rmtree(self.tmpdirname						)



			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=[np.random.randint(2_55  ,  size=(3, 30, 4_00)  ,  dtype=np.uinta						)]

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

				return image_inputs



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				_lowerCAmelCase			=PixaStructProcessor(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			=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       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=self.get_image_processor()
				_lowerCAmelCase			=self.get_tokenizer()

				_lowerCAmelCase			=PixaStructProcessor(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						)



			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				_lowerCAmelCase			=self.get_image_processor()
				_lowerCAmelCase			=self.get_tokenizer()

				_lowerCAmelCase			=PixaStructProcessor(tokenizer=__UpperCAmelCase  ,  image_processor=__UpperCAmelCase						)

				_lowerCAmelCase			="""lower newer"""

				_lowerCAmelCase			=processor(text=__UpperCAmelCase						)

				_lowerCAmelCase			=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       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.get_image_processor()
				_lowerCAmelCase			=self.get_tokenizer()

				_lowerCAmelCase			=PixaStructProcessor(tokenizer=__UpperCAmelCase  ,  image_processor=__UpperCAmelCase						)

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

				_lowerCAmelCase			=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       _lowerCAmelCase	(							self						)				->  str:
				_lowerCAmelCase			=self.get_image_processor()
				_lowerCAmelCase			=self.get_tokenizer()

				_lowerCAmelCase			=PixaStructProcessor(tokenizer=__UpperCAmelCase  ,  image_processor=__UpperCAmelCase						)

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

				_lowerCAmelCase			=processor(text=__UpperCAmelCase  ,  images=__UpperCAmelCase						)

				_lowerCAmelCase			=[5_12, 10_24, 20_48, 40_96]
				_lowerCAmelCase			=[7_70, 7_70, 7_70, 7_70]
				# with text
				for i, max_patch in enumerate(__UpperCAmelCase						):
					_lowerCAmelCase			=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						):
					_lowerCAmelCase			=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       _lowerCAmelCase	(							self						)				->  Optional[int]:
				_lowerCAmelCase			=self.get_image_processor()
				_lowerCAmelCase			=self.get_tokenizer()

				_lowerCAmelCase			=PixaStructProcessor(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       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				_lowerCAmelCase			=self.get_image_processor()
				_lowerCAmelCase			=self.get_tokenizer()

				_lowerCAmelCase			=PixaStructProcessor(tokenizer=__UpperCAmelCase  ,  image_processor=__UpperCAmelCase						)

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

				_lowerCAmelCase			=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"""]						)

				_lowerCAmelCase			=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


__A 		= logging.get_logger(__name__)

__A 		= {
    '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 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''ernie'''



			def __init__(							self  ,  __UpperCAmelCase=3_05_22  ,  __UpperCAmelCase=7_68  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=30_72  ,  __UpperCAmelCase="gelu"  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=5_12  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=0.0_2  ,  __UpperCAmelCase=1e-12  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase="absolute"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase  ,  )				->  str:
				super().__init__(pad_token_id=__UpperCAmelCase  ,  **__UpperCAmelCase						)

				_lowerCAmelCase			=vocab_size
				_lowerCAmelCase			=hidden_size
				_lowerCAmelCase			=num_hidden_layers
				_lowerCAmelCase			=num_attention_heads
				_lowerCAmelCase			=hidden_act
				_lowerCAmelCase			=intermediate_size
				_lowerCAmelCase			=hidden_dropout_prob
				_lowerCAmelCase			=attention_probs_dropout_prob
				_lowerCAmelCase			=max_position_embeddings
				_lowerCAmelCase			=type_vocab_size
				_lowerCAmelCase			=task_type_vocab_size
				_lowerCAmelCase			=use_task_id
				_lowerCAmelCase			=initializer_range
				_lowerCAmelCase			=layer_norm_eps
				_lowerCAmelCase			=position_embedding_type
				_lowerCAmelCase			=use_cache
				_lowerCAmelCase			=classifier_dropout







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							self						)				->  Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					_lowerCAmelCase			={0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
					_lowerCAmelCase			={0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				        ("""token_type_ids""", dynamic_axis),
				        ("""task_type_ids""", dynamic_axis),
				    ]						)



