"""simple docstring"""







import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor

class 	UpperCamelCase    (					UpperCamelCase__		):



				def       _lowercase  (self       :   List[Any])					->			str:
											__snake_case    :							List[str]   	=				self.config_class(**self.inputs_dict)
											self.parent.assertTrue(hasattr(__A			,						'hidden_sizes'))
											self.parent.assertTrue(hasattr(__A			,						'num_attention_heads'))
											self.parent.assertTrue(hasattr(__A			,						'num_encoder_blocks'))







class 	UpperCamelCase    :



				def __init__(self       :   List[str]			,						_A       :   Optional[Any]			,						_A       :   Tuple=13			,						_A       :   List[Any]=64			,						_A       :   int=3			,						_A       :   Dict=4			,						_A       :   str=[2, 2, 2, 2]			,						_A       :   Optional[Any]=[8, 4, 2, 1]			,						_A       :   str=[16, 32, 64, 1_28]			,						_A       :   Any=[1, 4, 8, 16]			,						_A       :   Tuple=[1, 2, 4, 8]			,						_A       :   List[Any]=True			,						_A       :   List[str]=True			,						_A       :   str="gelu"			,						_A       :   str=0.1			,						_A       :   Dict=0.1			,						_A       :   Optional[Any]=0.02			,						_A       :   Union[str, Any]=3			,						_A       :   Any=None			,						)					->			List[Any]:
											__snake_case    :							List[str]   	=				parent
											__snake_case    :							Tuple   	=				batch_size
											__snake_case    :							List[Any]   	=				image_size
											__snake_case    :							List[Any]   	=				num_channels
											__snake_case    :							Any   	=				num_encoder_blocks
											__snake_case    :							Optional[int]   	=				sr_ratios
											__snake_case    :							Dict   	=				depths
											__snake_case    :							List[str]   	=				hidden_sizes
											__snake_case    :							List[Any]   	=				downsampling_rates
											__snake_case    :							str   	=				num_attention_heads
											__snake_case    :							Union[str, Any]   	=				is_training
											__snake_case    :							Optional[Any]   	=				use_labels
											__snake_case    :							str   	=				hidden_act
											__snake_case    :							Optional[Any]   	=				hidden_dropout_prob
											__snake_case    :							int   	=				attention_probs_dropout_prob
											__snake_case    :							Optional[Any]   	=				initializer_range
											__snake_case    :							Tuple   	=				num_labels
											__snake_case    :							List[Any]   	=				scope



				def       _lowercase  (self       :   str)					->			List[Any]:
											__snake_case    :							str   	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											__snake_case    :							Dict   	=				None
											if self.use_labels:
																		__snake_case    :							str   	=				ids_tensor([self.batch_size, self.image_size, self.image_size]			,						self.num_labels)

											__snake_case    :							str   	=				self.get_config()
											return config, pixel_values, labels



				def       _lowercase  (self       :   Dict)					->			List[Any]:
											return SegformerConfig(
											    image_size=self.image_size			,						num_channels=self.num_channels			,						num_encoder_blocks=self.num_encoder_blocks			,						depths=self.depths			,						hidden_sizes=self.hidden_sizes			,						num_attention_heads=self.num_attention_heads			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						initializer_range=self.initializer_range			,						)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Tuple			,						_A       :   Optional[int]			,						_A       :   str)					->			Tuple:
											__snake_case    :							Optional[Any]   	=				SegformerModel(config=__A)
											model.to(__A)
											model.eval()
											__snake_case    :							Union[str, Any]   	=				model(__A)
											__snake_case    :							Tuple   	=				self.image_size // (self.downsampling_rates[-1] * 2)
											self.parent.assertEqual(
											    result.last_hidden_state.shape			,						(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))



				def       _lowercase  (self       :   Tuple			,						_A       :   Optional[Any]			,						_A       :   Any			,						_A       :   Union[str, Any])					->			str:
											__snake_case    :							Optional[Any]   	=				self.num_labels
											__snake_case    :							str   	=				SegformerForSemanticSegmentation(__A)
											model.to(__A)
											model.eval()
											__snake_case    :							Any   	=				model(__A)
											self.parent.assertEqual(
											    result.logits.shape			,						(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
											__snake_case    :							List[Any]   	=				model(__A			,						labels=__A)
											self.parent.assertEqual(
											    result.logits.shape			,						(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
											self.parent.assertGreater(result.loss			,						0.0)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   List[Any]			,						_A       :   int			,						_A       :   List[str])					->			Dict:
											__snake_case    :							Optional[int]   	=				1
											__snake_case    :							Any   	=				SegformerForSemanticSegmentation(config=__A)
											model.to(__A)
											model.eval()
											__snake_case    :							List[Any]   	=				torch.randint(0			,						1			,						(self.batch_size, self.image_size, self.image_size)).to(__A)
											__snake_case    :							Tuple   	=				model(__A			,						labels=__A)
											self.parent.assertGreater(result.loss			,						0.0)






				def       _lowercase  (self       :   List[Any])					->			Optional[Any]:
											__snake_case    :							Optional[Any]   	=				self.prepare_config_and_inputs()
											__snake_case	,       __snake_case	,       __snake_case    :							Dict   	=				config_and_inputs
											__snake_case    :							Any   	=				{'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class 	UpperCamelCase    (					UpperCamelCase__   ,			UpperCamelCase__   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[Any]					=	(
				    (
				        SegformerModel,
				        SegformerForSemanticSegmentation,
				        SegformerForImageClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase       :   Optional[Any]					=	(
				    {
				        """feature-extraction""": SegformerModel,
				        """image-classification""": SegformerForImageClassification,
				        """image-segmentation""": SegformerForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase       :   str					=	True
				UpperCAmelCase       :   Union[str, Any]					=	False
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   Optional[int]					=	False



				def       _lowercase  (self       :   int)					->			Tuple:
											__snake_case    :							int   	=				SegformerModelTester(self)
											__snake_case    :							List[Any]   	=				SegformerConfigTester(self			,						config_class=__A)



				def       _lowercase  (self       :   Any)					->			int:
											self.config_tester.run_common_tests()



				def       _lowercase  (self       :   List[Any])					->			Union[str, Any]:
											__snake_case    :							Optional[Any]   	=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__A)



				def       _lowercase  (self       :   Optional[Any])					->			Dict:
											__snake_case    :							Union[str, Any]   	=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_binary_image_segmentation(*__A)



				def       _lowercase  (self       :   Union[str, Any])					->			Optional[Any]:
											__snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_segmentation(*__A)



				@unittest.skip('SegFormer does not use inputs_embeds')
				def       _lowercase  (self       :   List[str])					->			Optional[Any]:
											pass



				@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
				def       _lowercase  (self       :   Union[str, Any])					->			Any:
											pass



				def       _lowercase  (self       :   Union[str, Any])					->			Union[str, Any]:
											__snake_case	,       __snake_case    :							int   	=				self.model_tester.prepare_config_and_inputs_for_common()

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

																		__snake_case    :							List[str]   	=				['pixel_values']
																		self.assertListEqual(arg_names[:1]			,						__A)



				def       _lowercase  (self       :   int)					->			Tuple:
											__snake_case	,       __snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs_for_common()
											__snake_case    :							Any   	=				True

											for model_class in self.all_model_classes:
																		__snake_case    :							List[Any]   	=				True
																		__snake_case    :							List[str]   	=				False
																		__snake_case    :							List[str]   	=				True
																		__snake_case    :							Dict   	=				model_class(__A)
																		model.to(__A)
																		model.eval()
																		with torch.no_grad():
																									__snake_case    :							Tuple   	=				model(**self._prepare_for_class(__A			,						__A))
																		__snake_case    :							Tuple   	=				outputs.attentions

																		__snake_case    :							Optional[Any]   	=				sum(self.model_tester.depths)
																		self.assertEqual(len(__A)			,						__A)

																		# check that output_attentions also work using config
																		del inputs_dict["output_attentions"]
																		__snake_case    :							int   	=				True
																		__snake_case    :							List[str]   	=				model_class(__A)
																		model.to(__A)
																		model.eval()
																		with torch.no_grad():
																									__snake_case    :							Dict   	=				model(**self._prepare_for_class(__A			,						__A))
																		__snake_case    :							List[Any]   	=				outputs.attentions

																		self.assertEqual(len(__A)			,						__A)

																		# verify the first attentions (first block, first layer)
																		__snake_case    :							Dict   	=				(self.model_tester.image_size // 4) ** 2
																		__snake_case    :							Union[str, Any]   	=				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																		self.assertListEqual(
																		    list(attentions[0].shape[-3:])			,						[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,						)

																		# verify the last attentions (last block, last layer)
																		__snake_case    :							List[str]   	=				(self.model_tester.image_size // 32) ** 2
																		__snake_case    :							str   	=				(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
																		self.assertListEqual(
																		    list(attentions[-1].shape[-3:])			,						[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,						)
																		__snake_case    :							Any   	=				len(__A)

																		# Check attention is always last and order is fine
																		__snake_case    :							Optional[int]   	=				True
																		__snake_case    :							int   	=				True
																		__snake_case    :							Optional[int]   	=				model_class(__A)
																		model.to(__A)
																		model.eval()
																		with torch.no_grad():
																									__snake_case    :							Tuple   	=				model(**self._prepare_for_class(__A			,						__A))

																		self.assertEqual(out_len + 1			,						len(__A))

																		__snake_case    :							Union[str, Any]   	=				outputs.attentions

																		self.assertEqual(len(__A)			,						__A)
																		# verify the first attentions (first block, first layer)
																		__snake_case    :							Tuple   	=				(self.model_tester.image_size // 4) ** 2
																		__snake_case    :							Optional[Any]   	=				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																		self.assertListEqual(
																		    list(self_attentions[0].shape[-3:])			,						[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,						)



				def       _lowercase  (self       :   Dict)					->			Dict:



											def check_hidden_states_output(_A       :   Optional[Any]			,						_A       :   str			,						_A       :   List[str]):
																		__snake_case    :							str   	=				model_class(__A)
																		model.to(__A)
																		model.eval()

																		with torch.no_grad():
																									__snake_case    :							int   	=				model(**self._prepare_for_class(__A			,						__A))

																		__snake_case    :							str   	=				outputs.hidden_states

																		__snake_case    :							Dict   	=				self.model_tester.num_encoder_blocks
																		self.assertEqual(len(__A)			,						__A)

																		# verify the first hidden states (first block)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-3:])			,						[
																		        self.model_tester.hidden_sizes[0],
																		        self.model_tester.image_size // 4,
																		        self.model_tester.image_size // 4,
																		    ]			,						)

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

											for model_class in self.all_model_classes:
																		__snake_case    :							List[Any]   	=				True
																		check_hidden_states_output(__A			,						__A			,						__A)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		__snake_case    :							Optional[int]   	=				True

																		check_hidden_states_output(__A			,						__A			,						__A)



				def       _lowercase  (self       :   str)					->			List[Any]:



											if not self.model_tester.is_training:
																		return

											__snake_case	,       __snake_case    :							List[Any]   	=				self.model_tester.prepare_config_and_inputs_for_common()
											__snake_case    :							Optional[int]   	=				True

											for model_class in self.all_model_classes:
																		if model_class in get_values(__A):
																									continue

																		__snake_case    :							Optional[int]   	=				model_class(__A)
																		model.to(__A)
																		model.train()
																		__snake_case    :							Optional[Any]   	=				self._prepare_for_class(__A			,						__A			,						return_labels=__A)
																		__snake_case    :							Optional[Any]   	=				model(**__A).loss
																		loss.backward()



				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
				def       _lowercase  (self       :   str)					->			int:
											pass






				@slow
				def       _lowercase  (self       :   List[str])					->			List[str]:



											for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__snake_case    :							Any   	=				SegformerModel.from_pretrained(__A)
																		self.assertIsNotNone(__A)







def 						__UpperCAmelCase       (					)							->    Dict:

							'''simple docstring'''





							__snake_case    :							List[Any]   	=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							return image

@require_torch
class 	UpperCamelCase    (					unittest.TestCase		):



				@slow
				def       _lowercase  (self       :   Optional[int])					->			Tuple:
											__snake_case    :							str   	=				SegformerImageProcessor(
											    image_scale=(5_12, 5_12)			,						keep_ratio=__A			,						align=__A			,						do_random_crop=__A)
											__snake_case    :							Optional[int]   	=				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
											    __A)

											__snake_case    :							List[str]   	=				prepare_img()
											__snake_case    :							List[Any]   	=				image_processor(images=__A			,						return_tensors='pt')
											__snake_case    :							Optional[int]   	=				encoded_inputs.pixel_values.to(__A)

											with torch.no_grad():
																		__snake_case    :							Any   	=				model(__A)

											__snake_case    :							Optional[Any]   	=				torch.Size((1, model.config.num_labels, 1_28, 1_28))
											self.assertEqual(outputs.logits.shape			,						__A)

											__snake_case    :							List[str]   	=				torch.tensor(
											    [
											        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
											        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
											        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
											    ]).to(__A)
											self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,						__A			,						atol=1E-4))



				@slow
				def       _lowercase  (self       :   Dict)					->			List[Any]:
											__snake_case    :							Tuple   	=				SegformerImageProcessor(
											    image_scale=(5_12, 5_12)			,						keep_ratio=__A			,						align=__A			,						do_random_crop=__A)
											__snake_case    :							List[Any]   	=				SegformerForSemanticSegmentation.from_pretrained(
											    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(__A)

											__snake_case    :							List[Any]   	=				prepare_img()
											__snake_case    :							int   	=				image_processor(images=__A			,						return_tensors='pt')
											__snake_case    :							Dict   	=				encoded_inputs.pixel_values.to(__A)

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

											__snake_case    :							Optional[int]   	=				torch.Size((1, model.config.num_labels, 1_28, 1_28))
											self.assertEqual(outputs.logits.shape			,						__A)

											__snake_case    :							List[Any]   	=				torch.tensor(
											    [
											        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
											        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
											        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
											    ]).to(__A)
											self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,						__A			,						atol=1E-1))






				@slow
				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
											__snake_case    :							int   	=				SegformerImageProcessor(
											    image_scale=(5_12, 5_12)			,						keep_ratio=__A			,						align=__A			,						do_random_crop=__A)
											__snake_case    :							Dict   	=				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
											    __A)

											__snake_case    :							List[Any]   	=				prepare_img()
											__snake_case    :							int   	=				image_processor(images=__A			,						return_tensors='pt')
											__snake_case    :							Any   	=				encoded_inputs.pixel_values.to(__A)

											with torch.no_grad():
																		__snake_case    :							List[Any]   	=				model(__A)

											__snake_case    :							Optional[int]   	=				outputs.logits.detach().cpu()

											__snake_case    :							Optional[Any]   	=				image_processor.post_process_semantic_segmentation(outputs=__A			,						target_sizes=[(5_00, 3_00)])
											__snake_case    :							Any   	=				torch.Size((5_00, 3_00))
											self.assertEqual(segmentation[0].shape			,						__A)

											__snake_case    :							Tuple   	=				image_processor.post_process_semantic_segmentation(outputs=__A)
											__snake_case    :							Optional[Any]   	=				torch.Size((1_28, 1_28))
											self.assertEqual(segmentation[0].shape			,						__A)






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





