



'''simple docstring'''




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
						import torch

						from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				int							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				List[Any]=9_9							,  lowerCAmelCase__  :				Dict=1_3							,  lowerCAmelCase__  :				Union[str, Any]=7							,  lowerCAmelCase__  :				int=9							,  lowerCAmelCase__  :				Any=True							,  lowerCAmelCase__  :				Tuple=True							,  lowerCAmelCase__  :				List[Any]=False							,  lowerCAmelCase__  :				List[Any]=3_2							,  lowerCAmelCase__  :				List[Any]=5							,  lowerCAmelCase__  :				Dict=4							,  lowerCAmelCase__  :				Optional[int]=3_7							,  lowerCAmelCase__  :				Any=8							,  lowerCAmelCase__  :				Dict=0.1							,  lowerCAmelCase__  :				Dict=0.0_02							,  lowerCAmelCase__  :				Optional[Any]=1							,  lowerCAmelCase__  :				Optional[Any]=0							,  lowerCAmelCase__  :				int=0							,  lowerCAmelCase__  :				int=None							,  lowerCAmelCase__  :				int=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	parent
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	batch_size
					__SCREAMING_SNAKE_CASE :	Tuple					 =	encoder_seq_length
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	decoder_seq_length
					# For common tests
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.decoder_seq_length
					__SCREAMING_SNAKE_CASE :	Dict					 =	is_training
					__SCREAMING_SNAKE_CASE :	Any					 =	use_attention_mask
					__SCREAMING_SNAKE_CASE :	int					 =	use_labels
					__SCREAMING_SNAKE_CASE :	List[str]					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_size
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_hidden_layers
					__SCREAMING_SNAKE_CASE :	str					 =	num_attention_heads
					__SCREAMING_SNAKE_CASE :	int					 =	d_ff
					__SCREAMING_SNAKE_CASE :	Any					 =	relative_attention_num_buckets
					__SCREAMING_SNAKE_CASE :	Any					 =	dropout_rate
					__SCREAMING_SNAKE_CASE :	Dict					 =	initializer_factor
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	eos_token_id
					__SCREAMING_SNAKE_CASE :	Any					 =	pad_token_id
					__SCREAMING_SNAKE_CASE :	List[str]					 =	decoder_start_token_id
					__SCREAMING_SNAKE_CASE :	List[str]					 =	None
					__SCREAMING_SNAKE_CASE :	int					 =	decoder_layers

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					return TaConfig.from_pretrained("""google/umt5-base"""		)

			def UpperCamelCase__      (							self  :				str							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				Optional[int]=None							,  lowerCAmelCase__  :				Optional[int]=None							,  ):





					"""simple docstring"""






					if attention_mask is None:
							__SCREAMING_SNAKE_CASE :	str					 =	input_ids.ne(config.pad_token_id		)
					if decoder_attention_mask is None:
							__SCREAMING_SNAKE_CASE :	int					 =	decoder_input_ids.ne(config.pad_token_id		)
					if head_mask is None:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.ones(config.num_hidden_layers							,  config.num_attention_heads							,  device=lowerCAmelCase__		)
					if decoder_head_mask is None:
							__SCREAMING_SNAKE_CASE :	int					 =	torch.ones(config.num_decoder_layers							,  config.num_attention_heads							,  device=lowerCAmelCase__		)
					if cross_attn_head_mask is None:
							__SCREAMING_SNAKE_CASE :	int					 =	torch.ones(
							    config.num_decoder_layers							,  config.num_attention_heads							,  device=lowerCAmelCase__		)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	ids_tensor([self.batch_size, self.encoder_seq_length]							,  self.vocab_size		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	ids_tensor([self.batch_size, self.decoder_seq_length]							,  self.vocab_size		)

					# we need to clamp the input ids here to avoid having pad token in between
					# this is because for NllbMoe the position_ids are prepared such that
					# all pad tokens have pos id = 2 and rest are between 2..seq_length
					# and the seq_length here is seq_length - num_pad_tokens
					# but when using past, there is no way of knowing if the past input ids had
					# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
					# position_ids being off by num_pad_tokens in past input
					__SCREAMING_SNAKE_CASE :	int					 =	input_ids.clamp(self.pad_token_id + 1		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	decoder_input_ids.clamp(self.pad_token_id + 1		)

					__SCREAMING_SNAKE_CASE :	int					 =	self.get_config()
					__SCREAMING_SNAKE_CASE :	Dict					 =	config.num_attention_heads
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.prepare_inputs_dict(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
					return config, input_dict

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	self.prepare_config_and_inputs()
					return config, inputs_dict

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					return TaConfig(
					    vocab_size=1_6_6							,  d_model=self.hidden_size							,  d_ff=self.d_ff							,  d_kv=self.hidden_size // self.num_attention_heads							,  num_layers=self.num_hidden_layers							,  num_decoder_layers=self.decoder_layers							,  num_heads=self.num_attention_heads							,  relative_attention_num_buckets=self.relative_attention_num_buckets							,  dropout_rate=self.dropout_rate							,  initializer_factor=self.initializer_factor							,  eos_token_id=self.eos_token_id							,  bos_token_id=self.pad_token_id							,  pad_token_id=self.pad_token_id							,  decoder_start_token_id=self.decoder_start_token_id							,  )

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					return TaConfig(
					    vocab_size=self.vocab_size							,  d_model=self.hidden_size							,  d_ff=self.d_ff							,  d_kv=self.hidden_size // self.num_attention_heads							,  num_layers=self.num_hidden_layers							,  num_decoder_layers=self.decoder_layers							,  num_heads=self.num_attention_heads							,  relative_attention_num_buckets=self.relative_attention_num_buckets							,  dropout_rate=self.dropout_rate							,  initializer_factor=self.initializer_factor							,  eos_token_id=self.eos_token_id							,  bos_token_id=self.pad_token_id							,  pad_token_id=self.pad_token_id							,  decoder_start_token_id=self.decoder_start_token_id							,  )

			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				str							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	UMTaModel(config=lowerCAmelCase__		)
					model.to(lowerCAmelCase__		)
					model.eval()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	model(
					    input_ids=lowerCAmelCase__							,  decoder_input_ids=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  decoder_attention_mask=lowerCAmelCase__							,  )
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	model(input_ids=lowerCAmelCase__							,  decoder_input_ids=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	result.last_hidden_state
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	result.past_key_values
					__SCREAMING_SNAKE_CASE :	Any					 =	result.encoder_last_hidden_state

					self.parent.assertEqual(encoder_output.size()							,  (self.batch_size, self.encoder_seq_length, self.hidden_size)		)
					self.parent.assertEqual(decoder_output.size()							,  (self.batch_size, self.decoder_seq_length, self.hidden_size)		)
					# There should be `num_layers` key value embeddings stored in decoder_past
					self.parent.assertEqual(len(lowerCAmelCase__		)							,  config.num_layers		)
					# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
					self.parent.assertEqual(len(decoder_past[0]		)							,  4		)

			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				List[str]							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	UMTaModel(config=lowerCAmelCase__		).get_decoder().to(lowerCAmelCase__		).eval()
					# first forward pass
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(lowerCAmelCase__							,  use_cache=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	model(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	model(lowerCAmelCase__							,  use_cache=lowerCAmelCase__		)

					self.parent.assertTrue(len(lowerCAmelCase__		) == len(lowerCAmelCase__		)		)
					self.parent.assertTrue(len(lowerCAmelCase__		) == len(lowerCAmelCase__		) + 1		)

					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__SCREAMING_SNAKE_CASE :	int					 =	ids_tensor((self.batch_size, 1)							,  config.vocab_size		)

					# append to next input_ids and
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.cat([input_ids, next_tokens]							,  dim=-1		)

					__SCREAMING_SNAKE_CASE :	int					 =	model(lowerCAmelCase__		)["""last_hidden_state"""]
					__SCREAMING_SNAKE_CASE :	Dict					 =	model(lowerCAmelCase__							,  past_key_values=lowerCAmelCase__		)["""last_hidden_state"""]

					# select random slice
					__SCREAMING_SNAKE_CASE :	int					 =	ids_tensor((1,)							,  output_from_past.shape[-1]		).item()
					__SCREAMING_SNAKE_CASE :	str					 =	output_from_no_past[:, -1, random_slice_idx].detach()
					__SCREAMING_SNAKE_CASE :	int					 =	output_from_past[:, 0, random_slice_idx].detach()

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



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Any							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	UMTaModel(config=lowerCAmelCase__		).to(lowerCAmelCase__		).half().eval()
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(**lowerCAmelCase__		)["""last_hidden_state"""]
					self.parent.assertFalse(torch.isnan(lowerCAmelCase__		).any().item()		)






@require_torch
class 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Any    					=	(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			_A   :	Optional[Any]    					=	(UMTaForConditionalGeneration,) if is_torch_available() else ()
			_A   :	Union[str, Any]    					=	(
			    {
			        '''conversational''': UMTaForConditionalGeneration,
			        '''feature-extraction''': UMTaModel,
			        '''summarization''': UMTaForConditionalGeneration,
			        '''text2text-generation''': UMTaForConditionalGeneration,
			        '''translation''': UMTaForConditionalGeneration,
			        '''question-answering''': UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			_A   :	Optional[Any]    					=	True
			_A   :	Any    					=	False
			_A   :	Union[str, Any]    					=	False
			_A   :	Optional[int]    					=	True
			_A   :	Any    					=	True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			_A   :	Any    					=	[0.8, 0.9]

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	UMTaModelTester(self		)

			@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""		)
			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	self.model_tester.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE :	Any					 =	UMTaModel(config_and_inputs[0]		).to(lowerCAmelCase__		)
					with tempfile.TemporaryDirectory() as tmpdirname:
							torch.onnx.export(
							    lowerCAmelCase__							,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							,  F"{tmpdirname}/t5_test.onnx"							,  export_params=lowerCAmelCase__							,  opset_version=9							,  input_names=["""input_ids""", """decoder_input_ids"""]							,  )

			@unittest.skipIf(torch_device == """cpu"""							,  """Cant do half precision"""		)
			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.model_tester.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE :	Dict					 =	config_and_inputs[0]
					__SCREAMING_SNAKE_CASE :	Any					 =	UMTaForConditionalGeneration(lowerCAmelCase__		).eval()
					model.to(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{
					    """head_mask""": torch.zeros(config.num_layers							,  config.num_heads							,  device=lowerCAmelCase__		),
					    """decoder_head_mask""": torch.zeros(config.num_decoder_layers							,  config.num_heads							,  device=lowerCAmelCase__		),
					    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers							,  config.num_heads							,  device=lowerCAmelCase__		),
					}

					for attn_name, (name, mask) in zip(lowerCAmelCase__							,  head_masking.items()		):
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	{name: mask}
							# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
							if name == "head_mask":
									__SCREAMING_SNAKE_CASE :	Any					 =	torch.ones(
									    config.num_decoder_layers							,  config.num_heads							,  device=lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Tuple					 =	model.generate(
							    config_and_inputs[1]["""input_ids"""]							,  num_beams=1							,  max_length=3							,  output_attentions=lowerCAmelCase__							,  return_dict_in_generate=lowerCAmelCase__							,  **lowerCAmelCase__							,  )
							# We check the state of decoder_attentions and cross_attentions just from the last step
							__SCREAMING_SNAKE_CASE :	List[str]					 =	out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
							self.assertEqual(sum([w.sum().item() for w in attn_weights]		)							,  0.0		)



			@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""		)
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			@slow
			@unittest.skip(
			    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""		)
			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""							,  return_dict=lowerCAmelCase__		).to(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	AutoTokenizer.from_pretrained("""google/umt5-small"""							,  use_fast=lowerCAmelCase__							,  legacy=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[
					    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
					    """No se como puedo <extra_id_0>.""",
					    """This is the reason why we <extra_id_0> them.""",
					    """The <extra_id_0> walks in <extra_id_1>, seats""",
					    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
					]
					__SCREAMING_SNAKE_CASE :	List[str]					 =	tokenizer(lowerCAmelCase__							,  return_tensors="""pt"""							,  padding=lowerCAmelCase__		).input_ids
					# fmt: off
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	torch.tensor(
					    [
					        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
					        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
					    ]		)
					# fmt: on
					torch.testing.assert_allclose(lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	model.generate(input_ids.to(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[
					    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
					    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
					    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
					    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
					    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
					]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	tokenizer.batch_decode(lowerCAmelCase__		)
					self.assertEqual(lowerCAmelCase__							,  lowerCAmelCase__		)




'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCamelCase__ :			Optional[int]												=				{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCamelCase__ :			Union[str, Any]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	'''maskformer'''
			_A   :	Optional[int]    					=	{'''hidden_size''': '''mask_feature_size'''}
			_A   :	int    					=	['''resnet''', '''swin''']
			_A   :	Any    					=	['''detr''']

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 0.1							,  lowerCAmelCase__  :				bool = False							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				float = 0.02							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 20.0							,  lowerCAmelCase__  :				Optional[bool] = None							,  **lowerCAmelCase__  :				Any							,  ):





					"""simple docstring"""






					if backbone_config is None:
							# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
							__SCREAMING_SNAKE_CASE :	Any					 =	SwinConfig(
							    image_size=3_8_4							,  in_channels=3							,  patch_size=4							,  embed_dim=1_2_8							,  depths=[2, 2, 1_8, 2]							,  num_heads=[4, 8, 1_6, 3_2]							,  window_size=1_2							,  drop_path_rate=0.3							,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							,  )

					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	backbone_config.pop("""model_type"""		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	CONFIG_MAPPING[backbone_model_type]
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	config_class.from_dict(lowerCAmelCase__		)

					# verify that the backbone is supported
					if backbone_config.model_type not in self.backbones_supported:
							logger.warning_once(
							    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
							    F"Supported model types: {','.join(self.backbones_supported		)}"		)

					if decoder_config is None:
							# fall back to https://huggingface.co/facebook/detr-resnet-50
							__SCREAMING_SNAKE_CASE :	List[str]					 =	DetrConfig()
					else:
							# verify that the decoder is supported
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
							    decoder_config.pop("""model_type"""		) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		) else decoder_config.model_type
							)
							if decoder_type not in self.decoders_supported:
									raise ValueError(
									    F"Transformer Decoder {decoder_type} not supported, please use one of"
									    F" {','.join(self.decoders_supported		)}"		)
							if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
									__SCREAMING_SNAKE_CASE :	int					 =	CONFIG_MAPPING[decoder_type]
									__SCREAMING_SNAKE_CASE :	Dict					 =	config_class.from_dict(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	backbone_config
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	decoder_config
					# main feature dimension for the model
					__SCREAMING_SNAKE_CASE :	List[str]					 =	fpn_feature_size
					__SCREAMING_SNAKE_CASE :	int					 =	mask_feature_size
					# initializer
					__SCREAMING_SNAKE_CASE :	str					 =	init_std
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	init_xavier_std
					# Hungarian matcher && loss
					__SCREAMING_SNAKE_CASE :	Tuple					 =	cross_entropy_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	dice_weight
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	mask_weight
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	use_auxiliary_loss
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	no_object_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	output_auxiliary_logits

					__SCREAMING_SNAKE_CASE :	Any					 =	self.decoder_config.encoder_attention_heads
					__SCREAMING_SNAKE_CASE :	int					 =	self.decoder_config.num_hidden_layers
					super().__init__(**lowerCAmelCase__		)

			@classmethod
			def UpperCamelCase__      (							cls  :				List[Any]							,  lowerCAmelCase__  :				PretrainedConfig							,  lowerCAmelCase__  :				PretrainedConfig							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					return cls(
					    backbone_config=lowerCAmelCase__							,  decoder_config=lowerCAmelCase__							,  **lowerCAmelCase__							,  )



			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	copy.deepcopy(self.__dict__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.backbone_config.to_dict()
					__SCREAMING_SNAKE_CASE :	str					 =	self.decoder_config.to_dict()
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.__class__.model_type
					return output