





import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer




class        SCREAMING_SNAKE_CASE__					:



 def __init__(				self,__lowerCamelCase,__lowerCamelCase=13,__lowerCamelCase=7,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=True,__lowerCamelCase=99,__lowerCamelCase=32,__lowerCamelCase=5,__lowerCamelCase=4,__lowerCamelCase=37,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=512,__lowerCamelCase=16,__lowerCamelCase=2,__lowerCamelCase=0.02,__lowerCamelCase=3,__lowerCamelCase=4,__lowerCamelCase=None,):
        A__		   =		parent
        A__		   =		batch_size
        A__		   =		seq_length
        A__		   =		is_training
        A__		   =		use_input_mask
        A__		   =		use_token_type_ids
        A__		   =		use_labels
        A__		   =		vocab_size
        A__		   =		hidden_size
        A__		   =		num_hidden_layers
        A__		   =		num_attention_heads
        A__		   =		intermediate_size
        A__		   =		hidden_act
        A__		   =		hidden_dropout_prob
        A__		   =		attention_probs_dropout_prob
        A__		   =		max_position_embeddings
        A__		   =		type_vocab_size
        A__		   =		type_sequence_label_size
        A__		   =		initializer_range
        A__		   =		num_labels
        A__		   =		num_choices
        A__		   =		scope



 def 							UpperCamelCase     (				self					):
        A__		   =		ids_tensor([self.batch_size, self.seq_length],self.vocab_size					)

        A__		   =		None
        if self.use_input_mask:
               A__		   =		random_attention_mask([self.batch_size, self.seq_length]					)

        A__		   =		None
        if self.use_token_type_ids:
               A__		   =		ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size					)

        A__		   =		None
        A__		   =		None
        A__		   =		None
        if self.use_labels:
               A__		   =		ids_tensor([self.batch_size],self.type_sequence_label_size					)
               A__		   =		ids_tensor([self.batch_size, self.seq_length],self.num_labels					)
               A__		   =		ids_tensor([self.batch_size],self.num_choices					)

        A__		   =		self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def 							UpperCamelCase     (				self					):
        return LlamaConfig(
            vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=__lowerCamelCase,initializer_range=self.initializer_range,)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		LlamaModel(config=__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(__lowerCamelCase,attention_mask=__lowerCamelCase					)
        A__		   =		model(__lowerCamelCase					)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        A__		   =		True
        A__		   =		LlamaModel(__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(
            __lowerCamelCase,attention_mask=__lowerCamelCase,encoder_hidden_states=__lowerCamelCase,encoder_attention_mask=__lowerCamelCase,)
        A__		   =		model(
            __lowerCamelCase,attention_mask=__lowerCamelCase,encoder_hidden_states=__lowerCamelCase,)
        A__		   =		model(__lowerCamelCase,attention_mask=__lowerCamelCase					)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        A__		   =		LlamaForCausalLM(config=__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(__lowerCamelCase,attention_mask=__lowerCamelCase,labels=__lowerCamelCase					)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        A__		   =		True
        A__		   =		True
        A__		   =		LlamaForCausalLM(config=__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()

        # first forward pass
        A__		   =		model(
            __lowerCamelCase,attention_mask=__lowerCamelCase,encoder_hidden_states=__lowerCamelCase,encoder_attention_mask=__lowerCamelCase,use_cache=__lowerCamelCase,)
        A__		   =		outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        A__		   =		ids_tensor((self.batch_size, 3),config.vocab_size					)
        A__		   =		ids_tensor((self.batch_size, 3),vocab_size=2					)

        # append to next input_ids and
        A__		   =		torch.cat([input_ids, next_tokens],dim=-1					)
        A__		   =		torch.cat([input_mask, next_mask],dim=-1					)

        A__		   =		model(
            __lowerCamelCase,attention_mask=__lowerCamelCase,encoder_hidden_states=__lowerCamelCase,encoder_attention_mask=__lowerCamelCase,output_hidden_states=__lowerCamelCase,)['''hidden_states'''][0]
        A__		   =		model(
            __lowerCamelCase,attention_mask=__lowerCamelCase,encoder_hidden_states=__lowerCamelCase,encoder_attention_mask=__lowerCamelCase,past_key_values=__lowerCamelCase,output_hidden_states=__lowerCamelCase,)['''hidden_states'''][0]

        # select random slice
        A__		   =		ids_tensor((1,),output_from_past.shape[-1]					).item()
        A__		   =		output_from_no_past[:, -3:, random_slice_idx].detach()
        A__		   =		output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-3					)					)




 def 							UpperCamelCase     (				self					):
        A__		   =		self.prepare_config_and_inputs()
        (
            (
            A__
        )   ,      (
            A__
        )   ,      (
            A__
        )   ,      (
            A__
        )   ,      (
            A__
        )   ,      (
            A__
        )   ,      (
            A__
        )   ,      
        )		   =		config_and_inputs
        A__		   =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict




@require_torch
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__							,   UpperCamelCase__							,   UpperCamelCase__							,   unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							=      (LlamaForCausalLM,) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							=      (
     {
         '''feature-extraction''': LlamaModel,
         '''text-classification''': LlamaForSequenceClassification,
         '''text-generation''': LlamaForCausalLM,
         '''zero-shot''': LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      False



 def 							UpperCamelCase     (				self					):
        A__		   =		LlamaModelTester(self					)
        A__		   =		ConfigTester(self,config_class=__lowerCamelCase,hidden_size=37					)



 def 							UpperCamelCase     (				self					):
        self.config_tester.run_common_tests()



 def 							UpperCamelCase     (				self					):
        A__		   =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
               A__		   =		type
               self.model_tester.create_and_check_model(*__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()
        A__		   =		3
        A__		   =		input_dict['''input_ids''']
        A__		   =		input_ids.ne(1					).to(__lowerCamelCase					)
        A__		   =		ids_tensor([self.model_tester.batch_size],self.model_tester.type_sequence_label_size					)
        A__		   =		LlamaForSequenceClassification(__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(__lowerCamelCase,attention_mask=__lowerCamelCase,labels=__lowerCamelCase					)
        self.assertEqual(result.logits.shape,(self.model_tester.batch_size, self.model_tester.num_labels)					)



 def 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()
        A__		   =		3
        A__		   =		'''single_label_classification'''
        A__		   =		input_dict['''input_ids''']
        A__		   =		input_ids.ne(1					).to(__lowerCamelCase					)
        A__		   =		ids_tensor([self.model_tester.batch_size],self.model_tester.type_sequence_label_size					)
        A__		   =		LlamaForSequenceClassification(__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(__lowerCamelCase,attention_mask=__lowerCamelCase,labels=__lowerCamelCase					)
        self.assertEqual(result.logits.shape,(self.model_tester.batch_size, self.model_tester.num_labels)					)



 def 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()
        A__		   =		3
        A__		   =		'''multi_label_classification'''
        A__		   =		input_dict['''input_ids''']
        A__		   =		input_ids.ne(1					).to(__lowerCamelCase					)
        A__		   =		ids_tensor(
            [self.model_tester.batch_size, config.num_labels],self.model_tester.type_sequence_label_size					).to(torch.float					)
        A__		   =		LlamaForSequenceClassification(__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        A__		   =		model(__lowerCamelCase,attention_mask=__lowerCamelCase,labels=__lowerCamelCase					)
        self.assertEqual(result.logits.shape,(self.model_tester.batch_size, self.model_tester.num_labels)					)



 @unittest.skip('''LLaMA buffers include complex numbers, which breaks this test'''					)
 def 							UpperCamelCase     (				self					):
        pass




 @parameterized.expand([('''linear''',), ('''dynamic''',)]					)
 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__   ,      A__		   =		self.model_tester.prepare_config_and_inputs_for_common()
        A__		   =		ids_tensor([1, 10],config.vocab_size					)
        A__		   =		ids_tensor([1, int(config.max_position_embeddings * 1.5					)],config.vocab_size					)

        set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
        A__		   =		LlamaModel(__lowerCamelCase					)
        original_model.to(__lowerCamelCase					)
        original_model.eval()
        A__		   =		original_model(__lowerCamelCase					).last_hidden_state
        A__		   =		original_model(__lowerCamelCase					).last_hidden_state

        set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
        A__		   =		{'''type''': scaling_type, '''factor''': 10.0}
        A__		   =		LlamaModel(__lowerCamelCase					)
        scaled_model.to(__lowerCamelCase					)
        scaled_model.eval()
        A__		   =		scaled_model(__lowerCamelCase					).last_hidden_state
        A__		   =		scaled_model(__lowerCamelCase					).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
               self.assertTrue(torch.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-5					)					)
        else:
               self.assertFalse(torch.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-5					)					)

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-5					)					)




@require_torch
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''					)
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
        A__		   =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''',device_map='''auto'''					)
        A__		   =		model(torch.tensor([input_ids]					)					)
        # Expected mean on dim = -1
        A__		   =		torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]					)
        torch.testing.assert_close(out.mean(-1					),__lowerCamelCase,atol=1E-2,rtol=1E-2					)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        A__		   =		torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]					)
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30],__lowerCamelCase,atol=1E-5,rtol=1E-5					)



 @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''					)
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
        A__		   =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''',device_map='''auto'''					)
        A__		   =		model(torch.tensor(__lowerCamelCase					)					)
        # Expected mean on dim = -1
        A__		   =		torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]					)
        torch.testing.assert_close(out.mean(-1					),__lowerCamelCase,atol=1E-2,rtol=1E-2					)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        A__		   =		torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]					)
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30],__lowerCamelCase,atol=1E-5,rtol=1E-5					)



 @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''					)
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
        A__		   =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''',device_map='''auto'''					)
        A__		   =		model(torch.tensor(__lowerCamelCase					)					)
        # Expected mean on dim = -1
        A__		   =		torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]					)
        torch.testing.assert_close(out.mean(-1					),__lowerCamelCase,atol=1E-2,rtol=1E-2					)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        A__		   =		torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]					)
        # fmt: on
        torch.testing.assert_close(out.mean(-1					),__lowerCamelCase,atol=1E-2,rtol=1E-2					)



 @unittest.skip(
     '''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'''					)
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		[1, 306, 4658, 278, 6593, 310, 2834, 338]
        A__		   =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''',device_map='''auto'''					)
        A__		   =		model(torch.tensor(__lowerCamelCase					)					)

        A__		   =		torch.tensor(
            [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]],dtype=torch.floataa					)
        torch.testing.assert_close(out.mean(-1					),__lowerCamelCase,atol=1E-2,rtol=1E-2					)
        # fmt: off
        A__		   =		torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]					)
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30],__lowerCamelCase,atol=1E-5,rtol=1E-5					)




 @unittest.skip('''Model is curently gated'''					)
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		'''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
        A__		   =		'''Simply put, the theory of relativity states that '''
        A__		   =		LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''					)
        A__		   =		tokenizer.encode(__lowerCamelCase,return_tensors='''pt'''					)
        A__		   =		LlamaForCausalLM.from_pretrained(
            '''meta-llama/Llama-2-13b-chat-hf''',device_map='''sequential''',use_safetensors=__lowerCamelCase					)

        # greedy generation outputs
        A__		   =		model.generate(__lowerCamelCase,max_new_tokens=64,top_p=__lowerCamelCase,temperature=1,do_sample=__lowerCamelCase					)
        A__		   =		tokenizer.decode(generated_ids[0],skip_special_tokens=__lowerCamelCase					)
        self.assertEqual(__lowerCamelCase,__lowerCamelCase					)












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


a__: Optional[Any]							   =						logging.get_logger(__name__)

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




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''conditional_detr'''
 __SCREAMING_SNAKE_CASE							=      ['''past_key_values''']
 __SCREAMING_SNAKE_CASE							=      {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }



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

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



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



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




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




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      version.parse('''1.11''' )



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



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




 @property
 def 							UpperCamelCase     (				self					):
        return 12





