
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor


class 				lowercase					:



		def __init__( self				,      A_				,      A_=13				,      A_=30				,      A_=2				,      A_=3				,      A_=True				,      A_=True				,      A_=32				,      A_=2				,      A_=4				,      A_=37				,      A_="gelu"				,      A_=0.1				,      A_=0.1				,      A_=10				,      A_=0.02				,      A_=3				,      A_=None				,      A_=2				,      )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     image_size
				UpperCamelCase		=     patch_size
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     hidden_dropout_prob
				UpperCamelCase		=     attention_probs_dropout_prob
				UpperCamelCase		=     type_sequence_label_size
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     scope
				UpperCamelCase		=     encoder_stride

				# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
				UpperCamelCase		=     (image_size // patch_size) ** 2
				UpperCamelCase		=     num_patches + 2



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""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.type_sequence_label_size       )

				UpperCamelCase		=     self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				return DeiTConfig(
				    image_size=self.image_size				,      patch_size=self.patch_size				,      num_channels=self.num_channels				,      hidden_size=self.hidden_size				,      num_hidden_layers=self.num_hidden_layers				,      num_attention_heads=self.num_attention_heads				,      intermediate_size=self.intermediate_size				,      hidden_act=self.hidden_act				,      hidden_dropout_prob=self.hidden_dropout_prob				,      attention_probs_dropout_prob=self.attention_probs_dropout_prob				,      is_decoder=A_				,      initializer_range=self.initializer_range				,      encoder_stride=self.encoder_stride				,      )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     TFDeiTModel(config=A_       )
				UpperCamelCase		=     model(A_       )
				self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.seq_length, self.hidden_size)       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     TFDeiTForMaskedImageModeling(config=A_       )
				UpperCamelCase		=     model(A_       )
				self.parent.assertEqual(
				    result.reconstruction.shape				,      (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

				# test greyscale images
				UpperCamelCase		=     1
				UpperCamelCase		=     TFDeiTForMaskedImageModeling(A_       )

				UpperCamelCase		=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
				UpperCamelCase		=     model(A_       )
				self.parent.assertEqual(result.reconstruction.shape				,      (self.batch_size, 1, self.image_size, self.image_size)       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.type_sequence_label_size
				UpperCamelCase		=     TFDeiTForImageClassification(A_       )
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.type_sequence_label_size)       )

				# test greyscale images
				UpperCamelCase		=     1
				UpperCamelCase		=     TFDeiTForImageClassification(A_       )

				UpperCamelCase		=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.type_sequence_label_size)       )



		def __UpperCamelCase ( self       )  ->					Union[str, 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_tf
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       str	     =				(
		    (
		        TFDeiTModel,
		        TFDeiTForImageClassification,
		        TFDeiTForImageClassificationWithTeacher,
		        TFDeiTForMaskedImageModeling,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase       :       List[str]	     =				(
		    {
		        "feature-extraction": TFDeiTModel,
		        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
		    }
		    if is_tf_available()
		    else {}
		)

		__lowercase       :       Optional[Any]	     =				False
		__lowercase       :       int	     =				False
		__lowercase       :       Optional[int]	     =				False
		__lowercase       :       Tuple	     =				False



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     TFDeiTModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      has_text_modality=A_				,      hidden_size=37       )



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



		@unittest.skip(reason='DeiT does not use inputs_embeds'       )
		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_       )
						self.assertIsInstance(model.get_input_embeddings()				,      (tf.keras.layers.Layer)       )
						UpperCamelCase		=     model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(A_				,      tf.keras.layers.Dense       )       )



		def __UpperCamelCase ( self       )  ->					int:
				"""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.call       )
						# 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       )  ->					str:
				"""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_masked_image_modeling(*A_       )



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_=False       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     super()._prepare_for_class(A_				,      A_				,      return_labels=A_       )

				if return_labels:
						if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call       ).parameters:
								del inputs_dict["labels"]

				return inputs_dict



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






def       A       (					)   ->  Dict:
		'''simple docstring'''

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


@require_tf
@require_vision
class 				lowercase					(		unittest.TestCase   ):



		@cached_property
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				return (
				    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'       )
				    if is_vision_available()
				    else None
				)



		@slow
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'       )

				UpperCamelCase		=     self.default_image_processor
				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='tf'       )

				# forward pass
				UpperCamelCase		=     model(**A_       )

				# verify the logits
				UpperCamelCase		=     tf.TensorShape((1, 1_000)       )
				self.assertEqual(outputs.logits.shape				,      A_       )

				UpperCamelCase		=     tf.constant([-1.0266, 0.1912, -1.2861]       )

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


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
