




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase_		:					Optional[Any]          =  logging.getLogger(__name__)
UpperCAmelCase_		:					Union[str, Any]          =  list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase_		:					List[str]          =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 		__A   :
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={
						        """help""": (
						            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
						        )
						    }       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__      )}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={
						        """help""": (
						            """Override some existing default config settings when a model is trained from scratch. Example: """
						            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
						        )
						    }       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}       ,    )
						UpperCamelCase      =       field(
						    default="""main"""       ,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={
						        """help""": (
						            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
						            """with private models)."""
						        )
						    }       ,    )

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
														raise ValueError(
														    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""			)




@dataclass
class 		__A   :
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}      )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
						UpperCamelCase      =       field(default=UpperCamelCase__       ,    metadata={"""help""": """The input training data file (a text file)."""}      )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )
						UpperCamelCase      =       field(
						    default=5       ,    metadata={
						        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
						    }       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated. Default to the max input length of the model."""
						        )
						    }       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """The number of processes to use for the preprocessing."""}       ,    )
						UpperCamelCase      =       field(
						    default=0.15       ,    metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}      )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={
						        """help""": (
						            """Whether to pad all samples to `max_seq_length`. """
						            """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
						        )
						    }       ,    )

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										if self.train_file is not None:
														__magic_name__						:						Dict										=self.train_file.split("""."""			)[-1]
														assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
										if self.validation_file is not None:
														__magic_name__						:						Union[str, Any]										=self.validation_file.split("""."""			)[-1]
														assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				with open(lowerCamelCase						,  """r"""						,  encoding="""utf-8""" ) as f:
								__magic_name__						:						Any										=[json.loads(lowerCamelCase ) for line in f.read().splitlines() if (len(lowerCamelCase ) > 0 and not line.isspace())]
				assert len(lowerCamelCase ) == len(lowerCamelCase )

				__magic_name__						:						Optional[Any]										={c: dataset[c] for c in dataset.column_names}
				__magic_name__						:						List[str]										=refs
				return Dataset.from_dict(lowerCamelCase )




def 		lowerCAmelCase_ (						):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				__magic_name__						:						str										=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								__magic_name__					,			__magic_name__					,			__magic_name__						:						int										=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[Any]										=parser.parse_args_into_dataclasses()

				# Detecting last checkpoint.
				__magic_name__						:						List[str]										=None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								__magic_name__						:						Optional[int]										=get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
												    """Use --overwrite_output_dir to overcome.""" )
								elif last_checkpoint is not None:
												logger.info(
												    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
												    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""						,  datefmt="""%m/%d/%Y %H:%M:%S"""						,  handlers=[logging.StreamHandler(sys.stdout )]						,  )
				logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

				# Log on each process the small summary:
				logger.warning(
				    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
				    + F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""						,  lowerCamelCase )

				# Set seed before initializing model.
				set_seed(training_args.seed )

				# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
				# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
				# (the dataset will be downloaded automatically from the datasets Hub).
				#
				# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
				# 'text' is found. You can easily tweak this behavior (see below).
				#
				# In distributed training, the load_dataset function guarantee that only one local process can concurrently
				# download the dataset.
				if data_args.dataset_name is not None:
								# Downloading and loading a dataset from the hub.
								__magic_name__						:						Union[str, Any]										=load_dataset(data_args.dataset_name						,  data_args.dataset_config_name )
								if "validation" not in datasets.keys():
												__magic_name__						:						int										=load_dataset(
												    data_args.dataset_name						,  data_args.dataset_config_name						,  split=F"train[:{data_args.validation_split_percentage}%]"						,  )
												__magic_name__						:						Optional[Any]										=load_dataset(
												    data_args.dataset_name						,  data_args.dataset_config_name						,  split=F"train[{data_args.validation_split_percentage}%:]"						,  )
				else:
								__magic_name__						:						str										={}
								if data_args.train_file is not None:
												__magic_name__						:						List[Any]										=data_args.train_file
								if data_args.validation_file is not None:
												__magic_name__						:						str										=data_args.validation_file
								__magic_name__						:						List[Any]										=data_args.train_file.split(""".""" )[-1]
								if extension == "txt":
												__magic_name__						:						List[str]										="""text"""
								__magic_name__						:						Optional[Any]										=load_dataset(lowerCamelCase						,  data_files=lowerCamelCase )
				# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
				# https://huggingface.co/docs/datasets/loading_datasets.html.

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				__magic_name__						:						List[Any]										={
				    """cache_dir""": model_args.cache_dir,
				    """revision""": model_args.model_revision,
				    """use_auth_token""": True if model_args.use_auth_token else None,
				}
				if model_args.config_name:
								__magic_name__						:						Tuple										=AutoConfig.from_pretrained(model_args.config_name						,  **lowerCamelCase )
				elif model_args.model_name_or_path:
								__magic_name__						:						int										=AutoConfig.from_pretrained(model_args.model_name_or_path						,  **lowerCamelCase )
				else:
								__magic_name__						:						Any										=CONFIG_MAPPING[model_args.model_type]()
								logger.warning("""You are instantiating a new config instance from scratch.""" )
								if model_args.config_overrides is not None:
												logger.info(F"Overriding config: {model_args.config_overrides}" )
												config.update_from_string(model_args.config_overrides )
												logger.info(F"New config: {config}" )

				__magic_name__						:						Any										={
				    """cache_dir""": model_args.cache_dir,
				    """use_fast""": model_args.use_fast_tokenizer,
				    """revision""": model_args.model_revision,
				    """use_auth_token""": True if model_args.use_auth_token else None,
				}
				if model_args.tokenizer_name:
								__magic_name__						:						List[str]										=AutoTokenizer.from_pretrained(model_args.tokenizer_name						,  **lowerCamelCase )
				elif model_args.model_name_or_path:
								__magic_name__						:						Dict										=AutoTokenizer.from_pretrained(model_args.model_name_or_path						,  **lowerCamelCase )
				else:
								raise ValueError(
								    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
								    """You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )

				if model_args.model_name_or_path:
								__magic_name__						:						List[str]										=AutoModelForMaskedLM.from_pretrained(
								    model_args.model_name_or_path						,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path )						,  config=lowerCamelCase						,  cache_dir=model_args.cache_dir						,  revision=model_args.model_revision						,  use_auth_token=True if model_args.use_auth_token else None						,  )
				else:
								logger.info("""Training new model from scratch""" )
								__magic_name__						:						List[str]										=AutoModelForMaskedLM.from_config(lowerCamelCase )

				model.resize_token_embeddings(len(lowerCamelCase ) )

				# Preprocessing the datasets.
				# First we tokenize all the texts.
				if training_args.do_train:
								__magic_name__						:						Union[str, Any]										=datasets["""train"""].column_names
				else:
								__magic_name__						:						List[str]										=datasets["""validation"""].column_names
				__magic_name__						:						Optional[Any]										="""text""" if """text""" in column_names else column_names[0]

				__magic_name__						:						Union[str, Any]										="""max_length""" if data_args.pad_to_max_length else False

				def tokenize_function(lowerCamelCase ):
								# Remove empty lines
								__magic_name__						:						Tuple										=[line for line in examples["""text"""] if len(lowerCamelCase ) > 0 and not line.isspace()]
								return tokenizer(examples["""text"""]						,  padding=lowerCamelCase						,  truncation=lowerCamelCase						,  max_length=data_args.max_seq_length )

				__magic_name__						:						List[Any]										=datasets.map(
				    lowerCamelCase						,  batched=lowerCamelCase						,  num_proc=data_args.preprocessing_num_workers						,  remove_columns=[text_column_name]						,  load_from_cache_file=not data_args.overwrite_cache						,  )

				# Add the chinese references if provided
				if data_args.train_ref_file is not None:
								__magic_name__						:						str										=add_chinese_references(tokenized_datasets["""train"""]						,  data_args.train_ref_file )
				if data_args.validation_ref_file is not None:
								__magic_name__						:						List[Any]										=add_chinese_references(
								    tokenized_datasets["""validation"""]						,  data_args.validation_ref_file )
				# If we have ref files, need to avoid it removed by trainer
				__magic_name__						:						str										=data_args.train_ref_file or data_args.validation_ref_file
				if has_ref:
								__magic_name__						:						Optional[Any]										=False

				# Data collator
				# This one will take care of randomly masking the tokens.
				__magic_name__						:						Any										=DataCollatorForWholeWordMask(tokenizer=lowerCamelCase						,  mlm_probability=data_args.mlm_probability )

				# Initialize our Trainer
				__magic_name__						:						Tuple										=Trainer(
				    model=lowerCamelCase						,  args=lowerCamelCase						,  train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None						,  eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None						,  tokenizer=lowerCamelCase						,  data_collator=lowerCamelCase						,  )

				# Training
				if training_args.do_train:
								if last_checkpoint is not None:
												__magic_name__						:						str										=last_checkpoint
								elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
												__magic_name__						:						Union[str, Any]										=model_args.model_name_or_path
								else:
												__magic_name__						:						int										=None
								__magic_name__						:						Dict										=trainer.train(resume_from_checkpoint=lowerCamelCase )
								trainer.save_model()  # Saves the tokenizer too for easy upload

								__magic_name__						:						List[str]										=os.path.join(training_args.output_dir						,  """train_results.txt""" )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase						,  """w""" ) as writer:
																logger.info("""***** Train results *****""" )
																for key, value in sorted(train_result.metrics.items() ):
																				logger.info(F"  {key} = {value}" )
																				writer.write(F"{key} = {value}\n" )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir						,  """trainer_state.json""" ) )

    # Evaluation
				__magic_name__						:						Dict										={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***""" )

								__magic_name__						:						Tuple										=trainer.evaluate()

								__magic_name__						:						Optional[int]										=math.exp(eval_output["""eval_loss"""] )
								__magic_name__						:						List[Any]										=perplexity

								__magic_name__						:						Optional[int]										=os.path.join(training_args.output_dir						,  """eval_results_mlm_wwm.txt""" )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase						,  """w""" ) as writer:
																logger.info("""***** Eval results *****""" )
																for key, value in sorted(results.items() ):
																				logger.info(F"  {key} = {value}" )
																				writer.write(F"{key} = {value}\n" )

				return results




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()










import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

							if is_vision_available():
														from transformers import MaskaFormerImageProcessor

if is_vision_available():
							from PIL import Image




class 		__A   :

						def __init__(		self :int     ,			__snake_case :List[Any]     ,			__snake_case :List[Any]=2     ,			__snake_case :Dict=True     ,			__snake_case :Tuple=False     ,			__snake_case :List[str]=10     ,			__snake_case :List[str]=3     ,			__snake_case :Union[str, Any]=32 * 8     ,			__snake_case :Optional[int]=32 * 8     ,			__snake_case :Any=4     ,			__snake_case :Union[str, Any]=64     ,			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=parent
										__magic_name__						:						List[Any]										=batch_size
										__magic_name__						:						List[str]										=is_training
										__magic_name__						:						List[str]										=use_auxiliary_loss
										__magic_name__						:						Union[str, Any]										=num_queries
										__magic_name__						:						str										=num_channels
										__magic_name__						:						Union[str, Any]										=min_size
										__magic_name__						:						Union[str, Any]										=max_size
										__magic_name__						:						Optional[int]										=num_labels
										__magic_name__						:						Tuple										=hidden_dim
										__magic_name__						:						Any										=hidden_dim

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
										    __snake_case			)

										__magic_name__						:						List[Any]										=torch.ones([self.batch_size, self.min_size, self.max_size]     ,			device=__snake_case			)

										__magic_name__						:						List[str]										=(
										    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,			device=__snake_case			) > 0.5
										).float()
										__magic_name__						:						Union[str, Any]										=(torch.rand((self.batch_size, self.num_labels)     ,			device=__snake_case			) > 0.5).long()

										__magic_name__						:						str										=self.get_config()
										return config, pixel_values, pixel_mask, mask_labels, class_labels

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Dict										=MaskaFormerConfig(
										    hidden_size=self.hidden_dim     ,			)
										__magic_name__						:						str										=self.num_queries
										__magic_name__						:						Dict										=self.num_labels

										__magic_name__						:						int										=[1, 1, 1, 1]
										__magic_name__						:						List[str]										=self.num_channels

										__magic_name__						:						str										=64
										__magic_name__						:						List[str]										=1_28
										__magic_name__						:						Optional[Any]										=self.hidden_dim
										__magic_name__						:						Tuple										=self.hidden_dim
										__magic_name__						:						Optional[int]										=self.hidden_dim
										return config

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Tuple										=self.prepare_config_and_inputs()
										__magic_name__						:						Optional[Any]										={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
										return config, inputs_dict

						def      A__    (		self :Union[str, Any]     ,			__snake_case :Tuple     ,			__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						int										=output.encoder_hidden_states
										__magic_name__						:						List[str]										=output.pixel_decoder_hidden_states
										__magic_name__						:						int										=output.transformer_decoder_hidden_states

										self.parent.assertTrue(len(__snake_case			)     ,			len(config.backbone_config.depths			)			)
										self.parent.assertTrue(len(__snake_case			)     ,			len(config.backbone_config.depths			)			)
										self.parent.assertTrue(len(__snake_case			)     ,			config.decoder_layers			)

						def      A__    (		self :List[Any]     ,			__snake_case :Optional[Any]     ,			__snake_case :int     ,			__snake_case :str     ,			__snake_case :str=False			):
										'''simple docstring'''

										with torch.no_grad():
														__magic_name__						:						List[str]										=MaskaFormerModel(config=__snake_case			)
														model.to(__snake_case			)
														model.eval()

														__magic_name__						:						Union[str, Any]										=model(pixel_values=__snake_case     ,			pixel_mask=__snake_case			)
														__magic_name__						:						int										=model(__snake_case     ,			output_hidden_states=__snake_case			)

										self.parent.assertEqual(
										    output.transformer_decoder_last_hidden_state.shape     ,			(self.batch_size, self.num_queries, self.hidden_dim)     ,			)
										# let's ensure the other two hidden state exists
										self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
										self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

										if output_hidden_states:
														self.check_output_hidden_state(__snake_case     ,			__snake_case			)


						def      A__    (		self :Optional[Any]     ,			__snake_case :List[str]     ,			__snake_case :List[Any]     ,			__snake_case :int     ,			__snake_case :Any     ,			__snake_case :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						str										=MaskaFormerForUniversalSegmentation(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()

										def comm_check_on_output(__snake_case :List[str]			):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
														self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape     ,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)     ,			)
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape     ,			(self.batch_size, self.num_queries, self.num_labels + 1)			)

										with torch.no_grad():
														__magic_name__						:						int										=model(pixel_values=__snake_case     ,			pixel_mask=__snake_case			)
														__magic_name__						:						List[str]										=model(__snake_case			)

														comm_check_on_output(__snake_case			)

														__magic_name__						:						Any										=model(
														    pixel_values=__snake_case     ,			pixel_mask=__snake_case     ,			mask_labels=__snake_case     ,			class_labels=__snake_case			)

										comm_check_on_output(__snake_case			)

										self.parent.assertTrue(result.loss is not None			)
										self.parent.assertEqual(result.loss.shape     ,			torch.Size([1]			)			)




@require_torch
class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
						UpperCamelCase      =       {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

						UpperCamelCase      =       False
						UpperCamelCase      =       False
						UpperCamelCase      =       False
						UpperCamelCase      =       False

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Any										=MaskaFormerModelTester(self			)
										__magic_name__						:						Union[str, Any]										=ConfigTester(self     ,			config_class=__snake_case     ,			has_text_modality=__snake_case			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						Union[str, Any]										=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(__snake_case     ,			**__snake_case     ,			output_hidden_states=__snake_case			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case			)

						@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""			)
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										pass

						@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""			)
						def      A__    (		self :Dict			):
										'''simple docstring'''

										pass

						@unittest.skip(reason="""Mask2Former is not a generative model"""			)
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										pass

						@unittest.skip(reason="""Mask2Former does not use token embeddings"""			)
						def      A__    (		self :int			):
										'''simple docstring'''

										pass

						@require_torch_multi_gpu
						@unittest.skip(
						    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""			)
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										pass

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						Union[str, Any]										=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														__magic_name__						:						Tuple										=model_class(__snake_case			)
														__magic_name__						:						Optional[Any]										=inspect.signature(model.forward			)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														__magic_name__						:						Tuple										=[*signature.parameters.keys()]

														__magic_name__						:						Optional[Any]										=["""pixel_values"""]
														self.assertListEqual(arg_names[:1]     ,			__snake_case			)

						@slow
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														__magic_name__						:						int										=MaskaFormerModel.from_pretrained(__snake_case			)
														self.assertIsNotNone(__snake_case			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Any										=(self.model_tester.min_size,) * 2
										__magic_name__						:						Union[str, Any]										={
										    """pixel_values""": torch.randn((2, 3, *size)     ,			device=__snake_case			),
										    """mask_labels""": torch.randn((2, 10, *size)     ,			device=__snake_case			),
										    """class_labels""": torch.zeros(2     ,			10     ,			device=__snake_case			).long(),
										}
										__magic_name__						:						Optional[Any]										=self.model_tester.get_config()

										__magic_name__						:						Dict										=MaskaFormerForUniversalSegmentation(__snake_case			).to(__snake_case			)
										__magic_name__						:						Any										=model(**__snake_case			)
										self.assertTrue(outputs.loss is not None			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						int										=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(__snake_case     ,			**__snake_case     ,			output_hidden_states=__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						List[Any]										=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														__magic_name__						:						List[Any]										=model_class(__snake_case			).to(__snake_case			)
														__magic_name__						:						Optional[int]										=model(**__snake_case     ,			output_attentions=__snake_case			)
														self.assertTrue(outputs.attentions is not None			)

						def      A__    (		self :int			):
										'''simple docstring'''

										if not self.model_tester.is_training:
														return

										__magic_name__						:						List[Any]										=self.all_model_classes[1]
										__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Union[str, Any]										=self.model_tester.prepare_config_and_inputs()

										__magic_name__						:						Dict										=model_class(__snake_case			)
										model.to(__snake_case			)
										model.train()

										__magic_name__						:						Optional[Any]										=model(__snake_case     ,			mask_labels=__snake_case     ,			class_labels=__snake_case			).loss
										loss.backward()


						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.all_model_classes[1]
										__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						List[Any]										=self.model_tester.prepare_config_and_inputs()
										__magic_name__						:						Tuple										=True
										__magic_name__						:						Optional[int]										=True

										__magic_name__						:						int										=model_class(__snake_case			).to(__snake_case			)
										model.train()

										__magic_name__						:						List[Any]										=model(__snake_case     ,			mask_labels=__snake_case     ,			class_labels=__snake_case			)

										__magic_name__						:						Optional[int]										=outputs.encoder_hidden_states[0]
										encoder_hidden_states.retain_grad()

										__magic_name__						:						Union[str, Any]										=outputs.pixel_decoder_hidden_states[0]
										pixel_decoder_hidden_states.retain_grad()

										__magic_name__						:						Union[str, Any]										=outputs.transformer_decoder_hidden_states[0]
										transformer_decoder_hidden_states.retain_grad()

										__magic_name__						:						Optional[int]										=outputs.attentions[0]
										attentions.retain_grad()

										outputs.loss.backward(retain_graph=__snake_case			)

										self.assertIsNotNone(encoder_hidden_states.grad			)
										self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
										self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
										self.assertIsNotNone(attentions.grad			)


UpperCAmelCase_		:					Dict          =  1e-4




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Dict										=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image




@require_vision
@slow
class 		__A   (      unittest.TestCase      ):

						@cached_property
						def      A__    (		self :int			):
										'''simple docstring'''

										return "facebook/mask2former-swin-small-coco-instance"

						@cached_property
						def      A__    (		self :int			):
										'''simple docstring'''

										return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints			) if is_vision_available() else None

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(__snake_case			)
										__magic_name__						:						int										=self.default_image_processor
										__magic_name__						:						List[Any]										=prepare_img()
										__magic_name__						:						Any										=image_processor(__snake_case     ,			return_tensors="""pt"""			).to(__snake_case			)
										__magic_name__						:						Dict										=inputs["""pixel_values"""].shape
										# check size is divisible by 32
										self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
										# check size
										self.assertEqual(__snake_case     ,			(1, 3, 3_84, 3_84)			)

										with torch.no_grad():
														__magic_name__						:						List[str]										=model(**__snake_case			)

										__magic_name__						:						Any										=torch.tensor(
										    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]			).to(__snake_case			)
										self.assertTrue(
										    torch.allclose(
										        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,			__snake_case     ,			atol=__snake_case			)			)

										__magic_name__						:						Dict										=torch.tensor(
										    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]			).to(__snake_case			)
										self.assertTrue(
										    torch.allclose(
										        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,			__snake_case     ,			atol=__snake_case			)			)

										__magic_name__						:						Any										=torch.tensor(
										    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]			).to(__snake_case			)
										self.assertTrue(
										    torch.allclose(
										        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,			__snake_case     ,			atol=__snake_case			)			)

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(__snake_case			).eval()
										__magic_name__						:						Optional[int]										=self.default_image_processor
										__magic_name__						:						Tuple										=prepare_img()
										__magic_name__						:						List[Any]										=image_processor(__snake_case     ,			return_tensors="""pt"""			).to(__snake_case			)
										__magic_name__						:						Union[str, Any]										=inputs["""pixel_values"""].shape
										# check size is divisible by 32
										self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
										# check size
										self.assertEqual(__snake_case     ,			(1, 3, 3_84, 3_84)			)

										with torch.no_grad():
														__magic_name__						:						str										=model(**__snake_case			)
										# masks_queries_logits
										__magic_name__						:						List[Any]										=outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape     ,			(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			)
										__magic_name__						:						List[Any]										=[
										    [-8.7839, -9.0056, -8.8121],
										    [-7.4104, -7.0313, -6.5401],
										    [-6.6105, -6.3427, -6.4675],
										]
										__magic_name__						:						Dict										=torch.tensor(__snake_case			).to(__snake_case			)
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,			__snake_case     ,			atol=__snake_case			)			)
										# class_queries_logits
										__magic_name__						:						Any										=outputs.class_queries_logits
										self.assertEqual(class_queries_logits.shape     ,			(1, model.config.num_queries, model.config.num_labels + 1)			)
										__magic_name__						:						int										=torch.tensor(
										    [
										        [1.8324, -8.0835, -4.1922],
										        [0.8450, -9.0050, -3.6053],
										        [0.3045, -7.7293, -3.0275],
										    ]			).to(__snake_case			)
										self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,			__snake_case     ,			atol=__snake_case			)			)


						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(__snake_case			).eval()
										__magic_name__						:						Any										=self.default_image_processor

										__magic_name__						:						Union[str, Any]										=image_processor(
										    [np.zeros((3, 8_00, 13_33)			), np.zeros((3, 8_00, 13_33)			)]     ,			segmentation_maps=[np.zeros((3_84, 3_84)			).astype(np.floataa			), np.zeros((3_84, 3_84)			).astype(np.floataa			)]     ,			return_tensors="""pt"""     ,			)

										__magic_name__						:						str										=inputs["""pixel_values"""].to(__snake_case			)
										__magic_name__						:						Tuple										=[el.to(__snake_case			) for el in inputs["""mask_labels"""]]
										__magic_name__						:						Union[str, Any]										=[el.to(__snake_case			) for el in inputs["""class_labels"""]]

										with torch.no_grad():
														__magic_name__						:						Dict										=model(**__snake_case			)

										self.assertTrue(outputs.loss is not None			)




