
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       _UpperCamelCase:					Union[str, Any]     ='platform'

       import jax
       import jax.numpy as jnp

       from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  str=None		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]=None    ):






  """simple docstring"""




  if attention_mask is None:
    _lowerCAmelCase						=		np.where(input_ids != config.pad_token_id		,				1		,				0    )
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
  }





@require_flax
class 						__lowercase:


       """simple docstring"""







       def __init__(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			Any=13  ,      _lowerCAmelCase					:			List[str]=7  ,      _lowerCAmelCase					:			Dict=True  ,      _lowerCAmelCase					:			Optional[Any]=False  ,      _lowerCAmelCase					:			List[str]=99  ,      _lowerCAmelCase					:			Optional[Any]=16  ,      _lowerCAmelCase					:			Any=2  ,      _lowerCAmelCase					:			Dict=4  ,      _lowerCAmelCase					:			List[str]=4  ,      _lowerCAmelCase					:			Dict="gelu"  ,      _lowerCAmelCase					:			List[str]=0.1  ,      _lowerCAmelCase					:			Optional[Any]=0.1  ,      _lowerCAmelCase					:			Tuple=20  ,      _lowerCAmelCase					:			Tuple=2  ,      _lowerCAmelCase					:			int=1  ,      _lowerCAmelCase					:			str=0  ,      _lowerCAmelCase					:			List[str]=16  ,      _lowerCAmelCase					:			Union[str, Any]=16  ,      _lowerCAmelCase					:			List[str]=0.02  ,      )					->   Any:
         _lowerCAmelCase						=		parent
         _lowerCAmelCase						=		batch_size
         _lowerCAmelCase						=		seq_length
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_labels
         _lowerCAmelCase						=		vocab_size
         _lowerCAmelCase						=		hidden_size
         _lowerCAmelCase						=		num_hidden_layers
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		intermediate_size
         _lowerCAmelCase						=		hidden_act
         _lowerCAmelCase						=		hidden_dropout_prob
         _lowerCAmelCase						=		attention_probs_dropout_prob
         _lowerCAmelCase						=		max_position_embeddings
         _lowerCAmelCase						=		eos_token_id
         _lowerCAmelCase						=		pad_token_id
         _lowerCAmelCase						=		bos_token_id
         _lowerCAmelCase						=		embed_dim
         _lowerCAmelCase						=		word_embed_proj_dim
         _lowerCAmelCase						=		initializer_range
         _lowerCAmelCase						=		False



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   List[Any]:
         _lowerCAmelCase						=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,      self.vocab_size       )  ,      3  ,      self.vocab_size       )
         _lowerCAmelCase						=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,      dtype=np.intaa       ))  ,      -1       )

         _lowerCAmelCase						=		OPTConfig(
             vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      ffn_dim=self.intermediate_size  ,      dropout=self.hidden_dropout_prob  ,      attention_dropout=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      eos_token_id=self.eos_token_id  ,      bos_token_id=self.bos_token_id  ,      pad_token_id=self.pad_token_id  ,      embed_dim=self.embed_dim  ,      is_encoder_decoder=_lowerCAmelCase  ,      word_embed_proj_dim=self.word_embed_proj_dim  ,      initializer_range=self.initializer_range  ,      use_cache=_lowerCAmelCase  ,      )
         _lowerCAmelCase						=		prepare_opt_inputs_dict(_lowerCAmelCase  ,      _lowerCAmelCase       )
         return config, inputs_dict



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   str:
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.prepare_config_and_inputs()
         return config, inputs_dict



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			List[Any]  ,      _lowerCAmelCase					:			int       )					->   Any:
         _lowerCAmelCase						=		20
         _lowerCAmelCase						=		model_class_name(_lowerCAmelCase       )

         _lowerCAmelCase						=		inputs_dict['input_ids']
         _lowerCAmelCase						=		inputs_dict['attention_mask']

         _lowerCAmelCase						=		model.init_cache(input_ids.shape[0]  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		jnp.ones((input_ids.shape[0], max_length)  ,      dtype='i4'       )

         _lowerCAmelCase						=		jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1       )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)  ,      )
         _lowerCAmelCase						=		model(
             input_ids[:, :-1]  ,      attention_mask=_lowerCAmelCase  ,      past_key_values=_lowerCAmelCase  ,      position_ids=_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,      dtype='i4'       )
         _lowerCAmelCase						=		model(
             input_ids[:, -1:]  ,      attention_mask=_lowerCAmelCase  ,      past_key_values=outputs_cache.past_key_values  ,      position_ids=_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		model(_lowerCAmelCase       )

         _lowerCAmelCase						=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
         self.parent.assertTrue(diff < 1e-3  ,      msg=F'''Max diff is {diff}'''       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Any       )					->   Any:
         _lowerCAmelCase						=		20
         _lowerCAmelCase						=		model_class_name(_lowerCAmelCase       )

         _lowerCAmelCase	,       _lowerCAmelCase						=		(
             inputs_dict['input_ids'],
             inputs_dict['attention_mask'],
         )

         _lowerCAmelCase						=		jnp.concatenate(
             [
                 attention_mask,
                 jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])       ),
             ]  ,      axis=-1  ,      )

         _lowerCAmelCase						=		model.init_cache(input_ids.shape[0]  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1       )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)  ,      )

         _lowerCAmelCase						=		model(
             input_ids[:, :-1]  ,      attention_mask=_lowerCAmelCase  ,      past_key_values=_lowerCAmelCase  ,      position_ids=_lowerCAmelCase  ,      )
         _lowerCAmelCase						=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,      dtype='i4'       )
         _lowerCAmelCase						=		model(
             input_ids[:, -1:]  ,      past_key_values=outputs_cache.past_key_values  ,      attention_mask=_lowerCAmelCase  ,      position_ids=_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		model(_lowerCAmelCase  ,      attention_mask=_lowerCAmelCase       )

         _lowerCAmelCase						=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
         self.parent.assertTrue(diff < 1e-3  ,      msg=F'''Max diff is {diff}'''       )





@require_flax
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase		,  SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       UpperCamelCase_				 =							() if is_flax_available() else ()



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Any:
         _lowerCAmelCase						=		FlaxOPTModelTester(self       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   int:
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   List[Any]:
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   int:
         for model_class_name in self.all_model_classes:
           _lowerCAmelCase						=		model_class_name.from_pretrained('facebook/opt-125m'       )
           _lowerCAmelCase						=		np.ones((1, 1)       ) * model.config.eos_token_id
           _lowerCAmelCase						=		model(_lowerCAmelCase       )
           self.assertIsNotNone(_lowerCAmelCase       )





@require_sentencepiece
@require_flax
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   List[str]:
         _lowerCAmelCase						=		FlaxOPTModel.from_pretrained('facebook/opt-350m'       )
         _lowerCAmelCase						=		jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       )
         _lowerCAmelCase						=		model(input_ids=_lowerCAmelCase       ).last_hidden_state
         _lowerCAmelCase						=		(1, 11, 512)
         self.assertEqual(output.shape  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		jnp.array(
             [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]       )
         self.assertTrue(jnp.allclose(output[:, :3, :3]  ,      _lowerCAmelCase  ,      atol=4e-2       )       )





@require_flax
@slow
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Tuple:
         super().setUp()
         _lowerCAmelCase						=		'facebook/opt-350m'






       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Optional[Any]:
         _lowerCAmelCase						=		FlaxOPTForCausalLM.from_pretrained(self.path_model       )
         _lowerCAmelCase						=		GPTaTokenizer.from_pretrained(self.path_model       )

         _lowerCAmelCase						=		[
             'Today is a beautiful day and I want to',
             'In the city of',
             'Paris is the capital of France and',
             'Computers and mobile phones have taken',
         ]
         # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase  ,      return_tensors='jax'  ,      padding=_lowerCAmelCase  ,      add_special_tokens=_lowerCAmelCase       )
         _lowerCAmelCase						=		model(inputs.input_ids  ,      attention_mask=inputs.attention_mask       )[0].mean(axis=-1       )
         _lowerCAmelCase						=		jnp.array(
             [
                 [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                 [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                 [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                 [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
             ]       )
         self.assertTrue(jnp.allclose(_lowerCAmelCase  ,      _lowerCAmelCase  ,      atol=4e-2       )       )

         _lowerCAmelCase						=		jax.jit(_lowerCAmelCase       )
         _lowerCAmelCase						=		model(inputs.input_ids  ,      attention_mask=inputs.attention_mask       )[0].mean(axis=-1       )
         self.assertTrue(jnp.allclose(_lowerCAmelCase  ,      _lowerCAmelCase  ,      atol=4e-2       )       )





@require_flax
@slow
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   Any:
         return [
             "Today is a beautiful day and I want",
             "In the city of",
             "Paris is the capital of France and",
             "Computers and mobile phones have taken",
         ]



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		'facebook/opt-125m'

         _lowerCAmelCase						=		[
             'Today is a beautiful day and I want to',
             'In the city of New York, the city',
             'Paris is the capital of France and the capital',
             'Computers and mobile phones have taken over the',
         ]

         _lowerCAmelCase						=		[]

         _lowerCAmelCase						=		FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase       )
         _lowerCAmelCase						=		GPTaTokenizer.from_pretrained(_lowerCAmelCase       )

         for prompt in self.prompts:
           _lowerCAmelCase						=		tokenizer(_lowerCAmelCase  ,      return_tensors='jax'       ).input_ids

           _lowerCAmelCase						=		model.generate(_lowerCAmelCase  ,      max_length=10       )
           _lowerCAmelCase						=		generated_ids[0]

           _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase  ,      skip_special_tokens=_lowerCAmelCase       )
           predicted_outputs += generated_string

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[Any]:
         _lowerCAmelCase						=		'facebook/opt-350m'

         _lowerCAmelCase						=		[
             'Today is a beautiful day and I want to',
             'In the city of San Francisco, the city',
             'Paris is the capital of France and the capital',
             'Computers and mobile phones have taken over the',
         ]

         _lowerCAmelCase						=		[]
         _lowerCAmelCase						=		FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase       )
         _lowerCAmelCase						=		GPTaTokenizer.from_pretrained(_lowerCAmelCase       )

         for prompt in self.prompts:
           _lowerCAmelCase						=		tokenizer(_lowerCAmelCase  ,      return_tensors='jax'       ).input_ids

           _lowerCAmelCase						=		model.generate(_lowerCAmelCase  ,      max_length=10       )
           _lowerCAmelCase						=		generated_ids[0]

           _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase  ,      skip_special_tokens=_lowerCAmelCase       )
           predicted_outputs += generated_string

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		'facebook/opt-125m'
         _lowerCAmelCase						=		[
             'Today is a beautiful day and I want to thank',
             'In the city of Rome Canaver Canaver Canaver Canaver',
         ]
         _lowerCAmelCase						=		FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase       )
         _lowerCAmelCase						=		GPTaTokenizer.from_pretrained(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer(
             [
                 'Today is a beautiful day and I want to',
                 'In the city of',
             ]  ,      return_tensors='jax'  ,      padding=_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		jax.jit(model.generate       )

         _lowerCAmelCase						=		jit_generate(inputs['input_ids']  ,      attention_mask=inputs['attention_mask']       ).sequences

         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase  ,      skip_special_tokens=_lowerCAmelCase       )

         self.assertIsNotNone(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   int:
         _lowerCAmelCase						=		'facebook/opt-350m'

         _lowerCAmelCase						=		GPTaTokenizer.from_pretrained(_lowerCAmelCase       )
         _lowerCAmelCase						=		FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase       )

         _lowerCAmelCase						=		'left'

         # use different length sentences to test batching
         _lowerCAmelCase						=		[
             'Hello, my dog is a little',
             'Today, I',
         ]

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase  ,      return_tensors='jax'  ,      padding=_lowerCAmelCase       )
         _lowerCAmelCase						=		inputs['input_ids']

         _lowerCAmelCase						=		model.generate(input_ids=_lowerCAmelCase  ,      attention_mask=inputs['attention_mask']  ,      trace=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(sentences[0]  ,      return_tensors='jax'       ).input_ids
         _lowerCAmelCase						=		model.generate(input_ids=_lowerCAmelCase       )

         _lowerCAmelCase						=		inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
         _lowerCAmelCase						=		tokenizer(sentences[1]  ,      return_tensors='jax'       ).input_ids
         _lowerCAmelCase						=		model.generate(input_ids=_lowerCAmelCase  ,      max_length=model.config.max_length - num_paddings       )

         _lowerCAmelCase						=		tokenizer.batch_decode(outputs[0]  ,      skip_special_tokens=_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.decode(output_non_padded[0][0]  ,      skip_special_tokens=_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.decode(output_padded[0][0]  ,      skip_special_tokens=_lowerCAmelCase       )

         _lowerCAmelCase						=		[
             'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
             'Today, I was in the middle of a conversation with a friend about the',
         ]
         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )
         self.assertListEqual(_lowerCAmelCase  ,      [non_padded_sentence, padded_sentence]       )


import copy
from typing import Dict, Optional

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


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''oneformer'''
       UpperCamelCase_				 =							{'''hidden_size''': '''hidden_dim'''}



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 2]  ,      num_heads=[3, 6, 12, 24]  ,      window_size=7  ,      drop_path_rate=0.3  ,      use_absolute_embeddings=_lowerCAmelCase  ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
