




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 		UpperCAmelCase   (				unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   ViTImageProcessor if is_vision_available() else None






						@property
						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							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(__lowercase       ,	range(len(__lowercase						)						)						)						)

							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(__lowercase						) + "\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       ,	__lowercase						)
							with open(self.image_processor_file       ,	"w"       ,	encoding="utf-8"						) as fp:
								json.dump(__lowercase       ,	__lowercase						)






						def        snake_case__						(						self							:     Any       ,	**__lowercase							:     List[Any]						):






							"""simple docstring"""






							return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,	**__lowercase						)






						def        snake_case__						(						self							:     int       ,	**__lowercase							:     int						):






							"""simple docstring"""






							return ViTImageProcessor.from_pretrained(self.tmpdirname       ,	**__lowercase						)






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							shutil.rmtree(self.tmpdirname						)






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							snake_case_		    =     np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta						)

							snake_case_		    =     Image.fromarray(np.moveaxis(__lowercase       ,	0       ,	-1						)						)

							return image_input






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     self.get_tokenizer()
							snake_case_		    =     self.get_image_processor()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)
							processor.save_pretrained(self.tmpdirname						)
							snake_case_		    =     MgpstrProcessor.from_pretrained(self.tmpdirname       ,	use_fast=__lowercase						)

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

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






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     self.get_tokenizer()
							snake_case_		    =     self.get_image_processor()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)
							processor.save_pretrained(self.tmpdirname						)

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

							snake_case_		    =     MgpstrProcessor.from_pretrained(
							    self.tmpdirname       ,	bos_token="(BOS)"       ,	eos_token="(EOS)"       ,	do_normalize=__lowercase       ,	padding_value=1.0						)

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

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






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     self.get_image_processor()
							snake_case_		    =     self.get_tokenizer()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)

							snake_case_		    =     self.prepare_image_inputs()

							snake_case_		    =     image_processor(__lowercase       ,	return_tensors="np"						)
							snake_case_		    =     processor(images=__lowercase       ,	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        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     self.get_image_processor()
							snake_case_		    =     self.get_tokenizer()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)

							snake_case_		    =     'test'

							snake_case_		    =     processor(text=__lowercase						)

							snake_case_		    =     tokenizer(__lowercase						)
							for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]       ,	encoded_processor[key]						)






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     self.get_image_processor()
							snake_case_		    =     self.get_tokenizer()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)

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

							snake_case_		    =     processor(text=__lowercase       ,	images=__lowercase						)

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

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






						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							snake_case_		    =     self.get_image_processor()
							snake_case_		    =     self.get_tokenizer()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)

							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(__lowercase						)
							snake_case_		    =     tokenizer.batch_decode(__lowercase						)
							snake_case_		    =     [seq.replace(" "       ,	""						) for seq in decoded_tok]

							self.assertListEqual(__lowercase       ,	__lowercase						)






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							snake_case_		    =     self.get_image_processor()
							snake_case_		    =     self.get_tokenizer()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)

							snake_case_		    =     None
							snake_case_		    =     self.prepare_image_inputs()

							snake_case_		    =     processor(text=__lowercase       ,	images=__lowercase						)

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






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     self.get_image_processor()
							snake_case_		    =     self.get_tokenizer()

							snake_case_		    =     MgpstrProcessor(tokenizer=__lowercase       ,	image_processor=__lowercase						)

							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"]						)




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE       :      int												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      List[str]												= {
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	Dict     =			"""deformable_detr"""
       lowercase_ :	int     =			{
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }
       def __init__(					self,      lowerCamelCase=True,      lowerCamelCase=None,      lowerCamelCase=3,      lowerCamelCase=3_00,      lowerCamelCase=10_24,      lowerCamelCase=6,      lowerCamelCase=10_24,      lowerCamelCase=8,      lowerCamelCase=6,      lowerCamelCase=10_24,      lowerCamelCase=8,      lowerCamelCase=0.0,      lowerCamelCase=True,      lowerCamelCase="relu",      lowerCamelCase=2_56,      lowerCamelCase=0.1,      lowerCamelCase=0.0,      lowerCamelCase=0.0,      lowerCamelCase=0.0_2,      lowerCamelCase=1.0,      lowerCamelCase=True,      lowerCamelCase=False,      lowerCamelCase="sine",      lowerCamelCase="resnet50",      lowerCamelCase=True,      lowerCamelCase=False,      lowerCamelCase=4,      lowerCamelCase=4,      lowerCamelCase=4,      lowerCamelCase=False,      lowerCamelCase=3_00,      lowerCamelCase=False,      lowerCamelCase=1,      lowerCamelCase=5,      lowerCamelCase=2,      lowerCamelCase=1,      lowerCamelCase=1,      lowerCamelCase=5,      lowerCamelCase=2,      lowerCamelCase=0.1,      lowerCamelCase=0.2_5,      lowerCamelCase=False,      **lowerCamelCase,      )       ->   Optional[int]:




           """simple docstring"""

           if backbone_config is not None and use_timm_backbone:
               raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')

           if not use_timm_backbone:
               if backbone_config is None:
                   logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
                   _lowercase       :							List[str]      		=		CONFIG_MAPPING['resnet'](out_features=['stage4'])
               elif isinstance(lowerCamelCase,      lowerCamelCase):
                   _lowercase       :							List[str]      		=		backbone_config.get('model_type')
                   _lowercase       :							str      		=		CONFIG_MAPPING[backbone_model_type]
                   _lowercase       :							Optional[int]      		=		config_class.from_dict(lowerCamelCase)
           _lowercase       :							Tuple      		=		use_timm_backbone
           _lowercase       :							List[str]      		=		backbone_config
           _lowercase       :							Tuple      		=		num_channels
           _lowercase       :							Optional[Any]      		=		num_queries
           _lowercase       :							Optional[Any]      		=		max_position_embeddings
           _lowercase       :							Optional[int]      		=		d_model
           _lowercase       :							int      		=		encoder_ffn_dim
           _lowercase       :							List[Any]      		=		encoder_layers
           _lowercase       :							str      		=		encoder_attention_heads
           _lowercase       :							str      		=		decoder_ffn_dim
           _lowercase       :							Optional[Any]      		=		decoder_layers
           _lowercase       :							List[str]      		=		decoder_attention_heads
           _lowercase       :							Optional[int]      		=		dropout
           _lowercase       :							Optional[Any]      		=		attention_dropout
           _lowercase       :							int      		=		activation_dropout
           _lowercase       :							Any      		=		activation_function
           _lowercase       :							Optional[int]      		=		init_std
           _lowercase       :							int      		=		init_xavier_std
           _lowercase       :							Union[str, Any]      		=		encoder_layerdrop
           _lowercase       :							Tuple      		=		auxiliary_loss
           _lowercase       :							Union[str, Any]      		=		position_embedding_type
           _lowercase       :							str      		=		backbone
           _lowercase       :							List[Any]      		=		use_pretrained_backbone
           _lowercase       :							Any      		=		dilation
           # deformable attributes
           _lowercase       :							Any      		=		num_feature_levels
           _lowercase       :							Dict      		=		encoder_n_points
           _lowercase       :							Dict      		=		decoder_n_points
           _lowercase       :							Dict      		=		two_stage
           _lowercase       :							Union[str, Any]      		=		two_stage_num_proposals
           _lowercase       :							str      		=		with_box_refine
           if two_stage is True and with_box_refine is False:
               raise ValueError('If two_stage is True, with_box_refine must be True.')
           # Hungarian matcher
           _lowercase       :							Tuple      		=		class_cost
           _lowercase       :							int      		=		bbox_cost
           _lowercase       :							Optional[int]      		=		giou_cost
           # Loss coefficients
           _lowercase       :							Optional[Any]      		=		mask_loss_coefficient
           _lowercase       :							Dict      		=		dice_loss_coefficient
           _lowercase       :							Tuple      		=		bbox_loss_coefficient
           _lowercase       :							Optional[int]      		=		giou_loss_coefficient
           _lowercase       :							Union[str, Any]      		=		eos_coefficient
           _lowercase       :							Union[str, Any]      		=		focal_alpha
           _lowercase       :							Dict      		=		disable_custom_kernels
           super().__init__(is_encoder_decoder=lowerCamelCase,      **lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return self.encoder_attention_heads
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return self.d_model




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




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		copy.deepcopy(self.__dict__)
           if self.backbone_config is not None:
               _lowercase       :							Union[str, Any]      		=		self.backbone_config.to_dict()
           _lowercase       :							Tuple      		=		self.__class__.model_type
           return output


