
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       __A(				UpperCAmelCase       ):

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.config_class(**self.inputs_dict   )
												self.parent.assertTrue(hasattr(__UpperCamelCase   ,					"""hidden_sizes"""   )   )
												self.parent.assertTrue(hasattr(__UpperCamelCase   ,					"""num_attention_heads"""   )   )
												self.parent.assertTrue(hasattr(__UpperCamelCase   ,					"""num_encoder_blocks"""   )   )





class       __A:

					def __init__(	self					:     List[str]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]=1_3   ,					__UpperCamelCase					:     Dict=6_4   ,					__UpperCamelCase					:     Optional[int]=3   ,					__UpperCamelCase					:     Optional[int]=4   ,					__UpperCamelCase					:     Union[str, Any]=[2, 2, 2, 2]   ,					__UpperCamelCase					:     List[str]=[8, 4, 2, 1]   ,					__UpperCamelCase					:     int=[1_6, 3_2, 6_4, 1_2_8]   ,					__UpperCamelCase					:     str=[1, 4, 8, 1_6]   ,					__UpperCamelCase					:     Optional[Any]=[1, 2, 4, 8]   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     Dict=True   ,					__UpperCamelCase					:     str="gelu"   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     Any=0.1   ,					__UpperCamelCase					:     List[Any]=0.02   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     Union[str, Any]=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					num_encoder_blocks
												lowerCamelCase_							  =					sr_ratios
												lowerCamelCase_							  =					depths
												lowerCamelCase_							  =					hidden_sizes
												lowerCamelCase_							  =					downsampling_rates
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					scope

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

												lowerCamelCase_							  =					self.get_config()
												return config, pixel_values, labels

					def    lowercase__      (	self					:     List[str]   ):
												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					:     List[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_							  =					SegformerModel(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												lowerCamelCase_							  =					lowerCamelCase_							  =					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					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					SegformerForSemanticSegmentation(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												self.parent.assertEqual(
												    result.logits.shape   ,					(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   )
												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					:     List[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_							  =					1
												lowerCamelCase_							  =					SegformerForSemanticSegmentation(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					torch.randint(0   ,					1   ,					(self.batch_size, self.image_size, self.image_size)   ).to(__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   )
												self.parent.assertGreater(result.loss   ,					0.0   )



					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_torch
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (
					    (
					        SegformerModel,
					        SegformerForSemanticSegmentation,
					        SegformerForImageClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE			    =    (
					    {
					        '''feature-extraction''': SegformerModel,
					        '''image-classification''': SegformerForImageClassification,
					        '''image-segmentation''': SegformerForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE			    =    True
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					SegformerModelTester(self   )
												lowerCamelCase_							  =					SegformerConfigTester(self   ,					config_class=__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ):
												self.config_tester.run_common_tests()

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_segmentation(*__UpperCamelCase   )

					@unittest.skip("""SegFormer does not use inputs_embeds"""   )
					def    lowercase__      (	self					:     Any   ):
												pass

					@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""   )
					def    lowercase__      (	self					:     Tuple   ):
												pass

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_							  =					True

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					False
																			lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.eval()
																			with torch.no_grad():
																										lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   )
																			lowerCamelCase_							  =					outputs.attentions

																			lowerCamelCase_							  =					sum(self.model_tester.depths   )
																			self.assertEqual(len(__UpperCamelCase   )   ,					__UpperCamelCase   )

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.eval()
																			with torch.no_grad():
																										lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   )
																			lowerCamelCase_							  =					outputs.attentions

																			self.assertEqual(len(__UpperCamelCase   )   ,					__UpperCamelCase   )

																			# verify the first attentions (first block, first layer)
																			lowerCamelCase_							  =					(self.model_tester.image_size // 4) ** 2
																			lowerCamelCase_							  =					(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)
																			lowerCamelCase_							  =					(self.model_tester.image_size // 3_2) ** 2
																			lowerCamelCase_							  =					(self.model_tester.image_size // (3_2 * 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]   ,					)
																			lowerCamelCase_							  =					len(__UpperCamelCase   )

																			# Check attention is always last and order is fine
																			lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.eval()
																			with torch.no_grad():
																										lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   )

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

																			lowerCamelCase_							  =					outputs.attentions

																			self.assertEqual(len(__UpperCamelCase   )   ,					__UpperCamelCase   )
																			# verify the first attentions (first block, first layer)
																			lowerCamelCase_							  =					(self.model_tester.image_size // 4) ** 2
																			lowerCamelCase_							  =					(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					:     Optional[int]   ):
												def check_hidden_states_output(__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Any   ):
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.eval()

																			with torch.no_grad():
																										lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   )

																			lowerCamelCase_							  =					outputs.hidden_states

																			lowerCamelCase_							  =					self.model_tester.num_encoder_blocks
																			self.assertEqual(len(__UpperCamelCase   )   ,					__UpperCamelCase   )

																			# 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,
																			    ]   ,					)

												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					True
																			check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			lowerCamelCase_							  =					True

																			check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[int]   ):
												if not self.model_tester.is_training:
																			return

												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_							  =					True

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

																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.train()
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					model(**__UpperCamelCase   ).loss
																			loss.backward()

					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
					def    lowercase__      (	self					:     Any   ):
												pass



					@slow
					def    lowercase__      (	self					:     Dict   ):
												for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					SegformerModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



def      __lowerCAmelCase						(							)  ->    str:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





@require_torch
class       __A(				unittest.TestCase       ):

					@slow
					def    lowercase__      (	self					:     int   ):
												# only resize + normalize
												lowerCamelCase_							  =					SegformerImageProcessor(
												    image_scale=(5_1_2, 5_1_2)   ,					keep_ratio=__UpperCamelCase   ,					align=__UpperCamelCase   ,					do_random_crop=__UpperCamelCase   )
												lowerCamelCase_							  =					SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""   ).to(
												    __UpperCamelCase   )

												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""pt"""   )
												lowerCamelCase_							  =					encoded_inputs.pixel_values.to(__UpperCamelCase   )

												with torch.no_grad():
																			lowerCamelCase_							  =					model(__UpperCamelCase   )

												lowerCamelCase_							  =					torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					torch.tensor(
												    [
												        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
												        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
												        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
												    ]   ).to(__UpperCamelCase   )
												self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,					__UpperCamelCase   ,					atol=1E-4   )   )

					@slow
					def    lowercase__      (	self					:     int   ):
												# only resize + normalize
												lowerCamelCase_							  =					SegformerImageProcessor(
												    image_scale=(5_1_2, 5_1_2)   ,					keep_ratio=__UpperCamelCase   ,					align=__UpperCamelCase   ,					do_random_crop=__UpperCamelCase   )
												lowerCamelCase_							  =					SegformerForSemanticSegmentation.from_pretrained(
												    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""   ).to(__UpperCamelCase   )

												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""pt"""   )
												lowerCamelCase_							  =					encoded_inputs.pixel_values.to(__UpperCamelCase   )

												with torch.no_grad():
																			lowerCamelCase_							  =					model(__UpperCamelCase   )

												lowerCamelCase_							  =					torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					torch.tensor(
												    [
												        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
												        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
												        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
												    ]   ).to(__UpperCamelCase   )
												self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,					__UpperCamelCase   ,					atol=1E-1   )   )



					@slow
					def    lowercase__      (	self					:     List[str]   ):
												# only resize + normalize
												lowerCamelCase_							  =					SegformerImageProcessor(
												    image_scale=(5_1_2, 5_1_2)   ,					keep_ratio=__UpperCamelCase   ,					align=__UpperCamelCase   ,					do_random_crop=__UpperCamelCase   )
												lowerCamelCase_							  =					SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""   ).to(
												    __UpperCamelCase   )

												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""pt"""   )
												lowerCamelCase_							  =					encoded_inputs.pixel_values.to(__UpperCamelCase   )

												with torch.no_grad():
																			lowerCamelCase_							  =					model(__UpperCamelCase   )

												lowerCamelCase_							  =					outputs.logits.detach().cpu()

												lowerCamelCase_							  =					image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase   ,					target_sizes=[(5_0_0, 3_0_0)]   )
												lowerCamelCase_							  =					torch.Size((5_0_0, 3_0_0)   )
												self.assertEqual(segmentation[0].shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase   )
												lowerCamelCase_							  =					torch.Size((1_2_8, 1_2_8)   )
												self.assertEqual(segmentation[0].shape   ,					__UpperCamelCase   )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase        =  {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''RagModel''',
		    '''RagPreTrainedModel''',
		    '''RagSequenceForGeneration''',
		    '''RagTokenForGeneration''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TFRagModel''',
		    '''TFRagPreTrainedModel''',
		    '''TFRagSequenceForGeneration''',
		    '''TFRagTokenForGeneration''',
		]


if TYPE_CHECKING:
		from .configuration_rag import RagConfig
		from .retrieval_rag import RagRetriever
		from .tokenization_rag import RagTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_rag import (
				    TFRagModel,
				    TFRagPreTrainedModel,
				    TFRagSequenceForGeneration,
				    TFRagTokenForGeneration,
				)

else:
		import sys

		lowercase        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





