
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 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     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 				lowercase					:



		def __init__( self				,      A_				,      A_=13				,      A_=64				,      A_=3				,      A_=4				,      A_=[2, 2, 2, 2]				,      A_=[8, 4, 2, 1]				,      A_=[16, 32, 64, 128]				,      A_=[1, 4, 8, 16]				,      A_=[1, 2, 4, 8]				,      A_=True				,      A_=True				,      A_="gelu"				,      A_=0.1				,      A_=0.1				,      A_=0.02				,      A_=3				,      A_=None				,      )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     image_size
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     num_encoder_blocks
				UpperCamelCase		=     sr_ratios
				UpperCamelCase		=     depths
				UpperCamelCase		=     hidden_sizes
				UpperCamelCase		=     downsampling_rates
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     hidden_dropout_prob
				UpperCamelCase		=     attention_probs_dropout_prob
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     num_labels
				UpperCamelCase		=     scope



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				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 __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     SegformerModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				UpperCamelCase		=     UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     SegformerForSemanticSegmentation(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				self.parent.assertEqual(
				    result.logits.shape				,      (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
				UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     1
				UpperCamelCase		=     SegformerForSemanticSegmentation(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     torch.randint(0				,      1				,      (self.batch_size, self.image_size, self.image_size)       ).to(A_       )
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertGreater(result.loss				,      0.0       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     config_and_inputs
				UpperCamelCase		=     {'pixel_values': pixel_values}
				return config, inputs_dict






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

		__lowercase       :       Tuple	     =				True
		__lowercase       :       Union[str, Any]	     =				False
		__lowercase       :       Dict	     =				False
		__lowercase       :       Optional[int]	     =				False



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     SegformerModelTester(self       )
				UpperCamelCase		=     SegformerConfigTester(self				,      config_class=A_       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_binary_image_segmentation(*A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     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 __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				pass



		@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'       )
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase		=     ['pixel_values']
						self.assertListEqual(arg_names[:1]				,      A_       )



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase		=     True

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

						UpperCamelCase		=     sum(self.model_tester.depths       )
						self.assertEqual(len(A_       )				,      A_       )

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

						self.assertEqual(len(A_       )				,      A_       )

						# verify the first attentions (first block, first layer)
						UpperCamelCase		=     (self.model_tester.image_size // 4) ** 2
						UpperCamelCase		=     (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)
						UpperCamelCase		=     (self.model_tester.image_size // 32) ** 2
						UpperCamelCase		=     (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]				,      )
						UpperCamelCase		=     len(A_       )

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

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

						UpperCamelCase		=     outputs.attentions

						self.assertEqual(len(A_       )				,      A_       )
						# verify the first attentions (first block, first layer)
						UpperCamelCase		=     (self.model_tester.image_size // 4) ** 2
						UpperCamelCase		=     (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 __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				def check_hidden_states_output(A_				,      A_				,      A_       ):
						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.eval()

						with torch.no_grad():
								UpperCamelCase		=     model(**self._prepare_for_class(A_				,      A_       )       )

						UpperCamelCase		=     outputs.hidden_states

						UpperCamelCase		=     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,
						    ]				,      )

				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase		=     True
						check_hidden_states_output(A_				,      A_				,      A_       )

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

						check_hidden_states_output(A_				,      A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				if not self.model_tester.is_training:
						return

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

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

						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.train()
						UpperCamelCase		=     self._prepare_for_class(A_				,      A_				,      return_labels=A_       )
						UpperCamelCase		=     model(**A_       ).loss
						loss.backward()



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase		=     SegformerModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )






def       A       (					)   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image


@require_torch
class 				lowercase					(		unittest.TestCase   ):



		@slow
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				# only resize + normalize
				UpperCamelCase		=     SegformerImageProcessor(
				    image_scale=(512, 512)				,      keep_ratio=A_				,      align=A_				,      do_random_crop=A_       )
				UpperCamelCase		=     SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'       ).to(
				    A_       )

				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='pt'       )
				UpperCamelCase		=     encoded_inputs.pixel_values.to(A_       )

				with torch.no_grad():
						UpperCamelCase		=     model(A_       )

				UpperCamelCase		=     torch.Size((1, model.config.num_labels, 128, 128)       )
				self.assertEqual(outputs.logits.shape				,      A_       )

				UpperCamelCase		=     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(A_       )
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]				,      A_				,      atol=1e-4       )       )



		@slow
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				# only resize + normalize
				UpperCamelCase		=     SegformerImageProcessor(
				    image_scale=(512, 512)				,      keep_ratio=A_				,      align=A_				,      do_random_crop=A_       )
				UpperCamelCase		=     SegformerForSemanticSegmentation.from_pretrained(
				    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'       ).to(A_       )

				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='pt'       )
				UpperCamelCase		=     encoded_inputs.pixel_values.to(A_       )

				with torch.no_grad():
						UpperCamelCase		=     model(A_       )

				UpperCamelCase		=     torch.Size((1, model.config.num_labels, 128, 128)       )
				self.assertEqual(outputs.logits.shape				,      A_       )

				UpperCamelCase		=     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(A_       )
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]				,      A_				,      atol=1e-1       )       )



		@slow
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				# only resize + normalize
				UpperCamelCase		=     SegformerImageProcessor(
				    image_scale=(512, 512)				,      keep_ratio=A_				,      align=A_				,      do_random_crop=A_       )
				UpperCamelCase		=     SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'       ).to(
				    A_       )

				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='pt'       )
				UpperCamelCase		=     encoded_inputs.pixel_values.to(A_       )

				with torch.no_grad():
						UpperCamelCase		=     model(A_       )

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

				UpperCamelCase		=     image_processor.post_process_semantic_segmentation(outputs=A_				,      target_sizes=[(500, 300)]       )
				UpperCamelCase		=     torch.Size((500, 300)       )
				self.assertEqual(segmentation[0].shape				,      A_       )

				UpperCamelCase		=     image_processor.post_process_semantic_segmentation(outputs=A_       )
				UpperCamelCase		=     torch.Size((128, 128)       )
				self.assertEqual(segmentation[0].shape				,      A_       )


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     value_function
				UpperCamelCase		=     unet
				UpperCamelCase		=     scheduler
				UpperCamelCase		=     env
				UpperCamelCase		=     env.get_dataset()
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].mean()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].std()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     env.observation_space.shape[0]
				UpperCamelCase		=     env.action_space.shape[0]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return (x_in - self.means[key]) / self.stds[key]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				return x_in * self.stds[key] + self.means[key]



		def __UpperCamelCase ( self				,      A_       )  ->					Any:
				"""simple docstring"""
				if type(A_       ) is dict:
						return {k: self.to_torch(A_       ) for k, v in x_in.items()}
				elif torch.is_tensor(A_       ):
						return x_in.to(self.unet.device       )
				return torch.tensor(A_				,      device=self.unet.device       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for key, val in cond.items():
						UpperCamelCase		=     val.clone()
				return x_in



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     x.shape[0]
				UpperCamelCase		=     None
				for i in tqdm.tqdm(self.scheduler.timesteps       ):
						# create batch of timesteps to pass into model
						UpperCamelCase		=     torch.full((batch_size,)				,      A_				,      device=self.unet.device				,      dtype=torch.long       )
						for _ in range(A_       ):
								with torch.enable_grad():
										x.requires_grad_()

										# permute to match dimension for pre-trained models
										UpperCamelCase		=     self.value_function(x.permute(0				,      2				,      1       )				,      A_       ).sample
										UpperCamelCase		=     torch.autograd.grad([y.sum()]				,      [x]       )[0]

										UpperCamelCase		=     self.scheduler._get_variance(A_       )
										UpperCamelCase		=     torch.exp(0.5 * posterior_variance       )
										UpperCamelCase		=     model_std * grad

								UpperCamelCase		=     0
								UpperCamelCase		=     x.detach()
								UpperCamelCase		=     x + scale * grad
								UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )

						UpperCamelCase		=     self.unet(x.permute(0				,      2				,      1       )				,      A_       ).sample.permute(0				,      2				,      1       )

						# TODO: verify deprecation of this kwarg
						UpperCamelCase		=     self.scheduler.step(A_				,      A_				,      A_				,      predict_epsilon=A_       )['prev_sample']

						# apply conditions to the trajectory (set the initial state)
						UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
						UpperCamelCase		=     self.to_torch(A_       )
				return x, y



		def __call__( self				,      A_				,      A_=64				,      A_=32				,      A_=2				,      A_=0.1       )  ->					List[str]:
				"""simple docstring"""
				# normalize the observations and create  batch dimension
				UpperCamelCase		=     self.normalize(A_				,      'observations'       )
				UpperCamelCase		=     obs[None].repeat(A_				,      axis=0       )

				UpperCamelCase		=     {0: self.to_torch(A_       )}
				UpperCamelCase		=     (batch_size, planning_horizon, self.state_dim + self.action_dim)

				# generate initial noise and apply our conditions (to make the trajectories start at current state)
				UpperCamelCase		=     randn_tensor(A_				,      device=self.unet.device       )
				UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
				UpperCamelCase		=     self.to_torch(A_       )

				# run the diffusion process
				UpperCamelCase	,  UpperCamelCase		=     self.run_diffusion(A_				,      A_				,      A_				,      A_       )

				# sort output trajectories by value
				UpperCamelCase		=     y.argsort(0				,      descending=A_       ).squeeze()
				UpperCamelCase		=     x[sorted_idx]
				UpperCamelCase		=     sorted_values[:, :, : self.action_dim]
				UpperCamelCase		=     actions.detach().cpu().numpy()
				UpperCamelCase		=     self.de_normalize(A_				,      key='actions'       )

				# select the action with the highest value
				if y is not None:
						UpperCamelCase		=     0
				else:
						# if we didn't run value guiding, select a random action
						UpperCamelCase		=     np.random.randint(0				,      A_       )

				UpperCamelCase		=     denorm_actions[selected_index, 0]
				return denorm_actions
