
import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[Any]									=			"""conditional_detr"""
	_UpperCamelCase	:	Any									=			["""past_key_values"""]
	_UpperCamelCase	:	Optional[Any]									=			{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(			self						,       snake_case=True						,       snake_case=None						,       snake_case=3						,       snake_case=300						,       snake_case=6						,       snake_case=2048						,       snake_case=8						,       snake_case=6						,       snake_case=2048						,       snake_case=8						,       snake_case=0.0						,       snake_case=0.0						,       snake_case=True						,       snake_case="relu"						,       snake_case=256						,       snake_case=0.1						,       snake_case=0.0						,       snake_case=0.0						,       snake_case=0.02						,       snake_case=1.0						,       snake_case=False						,       snake_case="sine"						,       snake_case="resnet50"						,       snake_case=True						,       snake_case=False						,       snake_case=2						,       snake_case=5						,       snake_case=2						,       snake_case=1						,       snake_case=1						,       snake_case=2						,       snake_case=5						,       snake_case=2						,       snake_case=0.25						,       **snake_case						,       ):
		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	    =       CONFIG_MAPPING['resnet'](out_features=['stage4']			)
			elif isinstance(snake_case						,       snake_case			):
				lowercase	    =       backbone_config.get('model_type'			)
				lowercase	    =       CONFIG_MAPPING[backbone_model_type]
				lowercase	    =       config_class.from_dict(snake_case			)

		lowercase	    =       use_timm_backbone
		lowercase	    =       backbone_config
		lowercase	    =       num_channels
		lowercase	    =       num_queries
		lowercase	    =       d_model
		lowercase	    =       encoder_ffn_dim
		lowercase	    =       encoder_layers
		lowercase	    =       encoder_attention_heads
		lowercase	    =       decoder_ffn_dim
		lowercase	    =       decoder_layers
		lowercase	    =       decoder_attention_heads
		lowercase	    =       dropout
		lowercase	    =       attention_dropout
		lowercase	    =       activation_dropout
		lowercase	    =       activation_function
		lowercase	    =       init_std
		lowercase	    =       init_xavier_std
		lowercase	    =       encoder_layerdrop
		lowercase	    =       decoder_layerdrop
		lowercase	    =       encoder_layers
		lowercase	    =       auxiliary_loss
		lowercase	    =       position_embedding_type
		lowercase	    =       backbone
		lowercase	    =       use_pretrained_backbone
		lowercase	    =       dilation
		# Hungarian matcher
		lowercase	    =       class_cost
		lowercase	    =       bbox_cost
		lowercase	    =       giou_cost
		# Loss coefficients
		lowercase	    =       mask_loss_coefficient
		lowercase	    =       dice_loss_coefficient
		lowercase	    =       cls_loss_coefficient
		lowercase	    =       bbox_loss_coefficient
		lowercase	    =       giou_loss_coefficient
		lowercase	    =       focal_alpha
		super().__init__(is_encoder_decoder=snake_case						,       **snake_case			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return self.encoder_attention_heads





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return self.d_model





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       copy.deepcopy(self.__dict__			)
		if self.backbone_config is not None:
			lowercase	    =       self.backbone_config.to_dict()
		lowercase	    =       self.__class__.model_type
		return output







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[str]									=			version.parse("""1.11"""  )





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return OrderedDict(
		    [
		        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
		        ('pixel_mask', {0: 'batch'}),
		    ]			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return 1E-5





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return 12



"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


