






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor


@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							_UpperCAmelCase     =       ViTImageProcessor if is_vision_available() else None
							@property
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Optional[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()
							def lowerCAmelCase_ (     self:      Tuple						)		->		Tuple:
											snake_case__									=     (3, 32, 1_28)
											snake_case__									=     tempfile.mkdtemp()

											# fmt: off
											snake_case__									=     ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
											# fmt: on
											snake_case__									=     dict(zip(UpperCamelCase       ,	range(len(UpperCamelCase						)						)						)						)

											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(json.dumps(UpperCamelCase						) + '\n'						)

											snake_case__									=     {
											    'do_normalize': False,
											    'do_resize': True,
											    'image_processor_type': 'ViTImageProcessor',
											    'resample': 3,
											    'size': {'height': 32, 'width': 1_28},
											}
											snake_case__									=     os.path.join(self.tmpdirname       ,	UpperCamelCase						)
											with open(self.image_processor_file       ,	'w'       ,	encoding='utf-8'						) as fp:
															json.dump(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	**UpperCamelCase:      Tuple						)		->		int:
											return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple       ,	**UpperCamelCase:      int						)		->		int:
											return ViTImageProcessor.from_pretrained(self.tmpdirname       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											shutil.rmtree(self.tmpdirname						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Optional[Any]:
											snake_case__									=     np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta						)

											snake_case__									=     Image.fromarray(np.moveaxis(UpperCamelCase       ,	0       ,	-1						)						)

											return image_input
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Union[str, Any]:
											snake_case__									=     self.get_tokenizer()
											snake_case__									=     self.get_image_processor()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)
											processor.save_pretrained(self.tmpdirname						)
											snake_case__									=     MgpstrProcessor.from_pretrained(self.tmpdirname       ,	use_fast=UpperCamelCase						)

											self.assertEqual(processor.char_tokenizer.get_vocab()       ,	tokenizer.get_vocab()						)
											self.assertIsInstance(processor.char_tokenizer       ,	UpperCamelCase						)

											self.assertEqual(processor.image_processor.to_json_string()       ,	image_processor.to_json_string()						)
											self.assertIsInstance(processor.image_processor       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		str:
											snake_case__									=     self.get_tokenizer()
											snake_case__									=     self.get_image_processor()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)
											processor.save_pretrained(self.tmpdirname						)

											snake_case__									=     self.get_tokenizer(bos_token='(BOS)'       ,	eos_token='(EOS)'						)
											snake_case__									=     self.get_image_processor(do_normalize=UpperCamelCase       ,	padding_value=1.0						)

											snake_case__									=     MgpstrProcessor.from_pretrained(
											    self.tmpdirname       ,	bos_token='(BOS)'       ,	eos_token='(EOS)'       ,	do_normalize=UpperCamelCase       ,	padding_value=1.0						)

											self.assertEqual(processor.char_tokenizer.get_vocab()       ,	tokenizer_add_kwargs.get_vocab()						)
											self.assertIsInstance(processor.char_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:      Union[str, Any]						)		->		Optional[Any]:
											snake_case__									=     self.get_image_processor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

											snake_case__									=     self.prepare_image_inputs()

											snake_case__									=     image_processor(UpperCamelCase       ,	return_tensors='np'						)
											snake_case__									=     processor(images=UpperCamelCase       ,	return_tensors='np'						)

											for key in input_image_proc.keys():
															self.assertAlmostEqual(input_image_proc[key].sum()       ,	input_processor[key].sum()       ,	delta=1e-2						)
							def lowerCAmelCase_ (     self:      str						)		->		Any:
											snake_case__									=     self.get_image_processor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

											snake_case__									=     'test'

											snake_case__									=     processor(text=UpperCamelCase						)

											snake_case__									=     tokenizer(UpperCamelCase						)
											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]       ,	encoded_processor[key]						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											snake_case__									=     self.get_image_processor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

											snake_case__									=     'test'
											snake_case__									=     self.prepare_image_inputs()

											snake_case__									=     processor(text=UpperCamelCase       ,	images=UpperCamelCase						)

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

											# test if it raises when no input is passed
											with pytest.raises(UpperCamelCase						):
															processor()
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:
											snake_case__									=     self.get_image_processor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

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

											snake_case__									=     processor.char_decode(UpperCamelCase						)
											snake_case__									=     tokenizer.batch_decode(UpperCamelCase						)
											snake_case__									=     [seq.replace(' '       ,	''						) for seq in decoded_tok]

											self.assertListEqual(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Any:
											snake_case__									=     self.get_image_processor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

											snake_case__									=     None
											snake_case__									=     self.prepare_image_inputs()

											snake_case__									=     processor(text=UpperCamelCase       ,	images=UpperCamelCase						)

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


							def lowerCAmelCase_ (     self:      Dict						)		->		str:
											snake_case__									=     self.get_image_processor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     MgpstrProcessor(tokenizer=UpperCamelCase       ,	image_processor=UpperCamelCase						)

											snake_case__									=     torch.randn(1       ,	27       ,	38						)
											snake_case__									=     torch.randn(1       ,	27       ,	5_02_57						)
											snake_case__									=     torch.randn(1       ,	27       ,	3_05_22						)

											snake_case__									=     processor.batch_decode([char_input, bpe_input, wp_input]						)

											self.assertListEqual(list(results.keys()						)       ,	['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds']						)












from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


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




