



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 		UpperCAmelCase__			(			lowercase__   ):




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: UNetaDModel       ,       a_							: UNetaDModel       ,       a_							: DDPMScheduler       ,       a_							: Dict       ,       ):
					'''simple docstring'''





					super().__init__()
					__UpperCAmelCase    :     Tuple													=	value_function
					__UpperCAmelCase    :     List[str]													=	unet
					__UpperCAmelCase    :     Optional[int]													=	scheduler
					__UpperCAmelCase    :     str													=	env
					__UpperCAmelCase    :     str													=	env.get_dataset()
					__UpperCAmelCase    :     Dict													=	{}
					for key in self.data.keys():
							try:
									__UpperCAmelCase    :     Any													=	self.data[key].mean()
							except:  # noqa: E722
									pass
					__UpperCAmelCase    :     List[Any]													=	{}
					for key in self.data.keys():
							try:
									__UpperCAmelCase    :     int													=	self.data[key].std()
							except:  # noqa: E722
									pass
					__UpperCAmelCase    :     int													=	env.observation_space.shape[0]
					__UpperCAmelCase    :     Tuple													=	env.action_space.shape[0]




			def  snake_case__     (   self							: Tuple       ,       a_							: Dict       ,       a_							: List[Any] ):
					'''simple docstring'''





					return (x_in - self.means[key]) / self.stds[key]




			def  snake_case__     (   self							: int       ,       a_							: Tuple       ,       a_							: int ):
					'''simple docstring'''





					return x_in * self.stds[key] + self.means[key]




			def  snake_case__     (   self							: str       ,       a_							: int ):
					'''simple docstring'''





					if type(SCREAMING_SNAKE_CASE__ ) is dict:
							return {k: self.to_torch(SCREAMING_SNAKE_CASE__ ) for k, v in x_in.items()}
					elif torch.is_tensor(SCREAMING_SNAKE_CASE__ ):
							return x_in.to(self.unet.device )
					return torch.tensor(SCREAMING_SNAKE_CASE__       ,       device=self.unet.device )




			def  snake_case__     (   self							: str       ,       a_							: Any       ,       a_							: str       ,       a_							: Tuple ):
					'''simple docstring'''





					for key, val in cond.items():
							__UpperCAmelCase    :     int													=	val.clone()
					return x_in




			def  snake_case__     (   self							: Tuple       ,       a_							: str       ,       a_							: Optional[Any]       ,       a_							: Any       ,       a_							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	x.shape[0]
					__UpperCAmelCase    :     Optional[Any]													=	None
					for i in tqdm.tqdm(self.scheduler.timesteps ):
							# create batch of timesteps to pass into model
							__UpperCAmelCase    :     Optional[int]													=	torch.full((batch_size,)       ,       SCREAMING_SNAKE_CASE__       ,       device=self.unet.device       ,       dtype=torch.long )
							for _ in range(SCREAMING_SNAKE_CASE__ ):
									with torch.enable_grad():
											x.requires_grad_()

											# permute to match dimension for pre-trained models
											__UpperCAmelCase    :     Tuple													=	self.value_function(x.permute(0       ,       2       ,       1 )       ,       SCREAMING_SNAKE_CASE__ ).sample
											__UpperCAmelCase    :     List[Any]													=	torch.autograd.grad([y.sum()]       ,       [x] )[0]

											__UpperCAmelCase    :     Union[str, Any]													=	self.scheduler._get_variance(SCREAMING_SNAKE_CASE__ )
											__UpperCAmelCase    :     Optional[int]													=	torch.exp(0.5 * posterior_variance )
											__UpperCAmelCase    :     List[Any]													=	model_std * grad

									__UpperCAmelCase    :     List[str]													=	0
									__UpperCAmelCase    :     Dict													=	x.detach()
									__UpperCAmelCase    :     Any													=	x + scale * grad
									__UpperCAmelCase    :     int													=	self.reset_xa(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       ,       self.action_dim )

							__UpperCAmelCase    :     Tuple													=	self.unet(x.permute(0       ,       2       ,       1 )       ,       SCREAMING_SNAKE_CASE__ ).sample.permute(0       ,       2       ,       1 )

							# TODO: verify deprecation of this kwarg
							__UpperCAmelCase    :     List[str]													=	self.scheduler.step(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       ,       predict_epsilon=SCREAMING_SNAKE_CASE__ )['prev_sample']

							# apply conditions to the trajectory (set the initial state)
							__UpperCAmelCase    :     Optional[Any]													=	self.reset_xa(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       ,       self.action_dim )
							__UpperCAmelCase    :     Union[str, Any]													=	self.to_torch(SCREAMING_SNAKE_CASE__ )
					return x, y




			def __call__(   self							: Union[str, Any]       ,       a_							: List[Any]       ,       a_							: Tuple=64       ,       a_							: Optional[Any]=32       ,       a_							: Tuple=2       ,       a_							: Dict=0.1 ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.normalize(SCREAMING_SNAKE_CASE__       ,       '''observations''' )
					__UpperCAmelCase    :     Dict													=	obs[None].repeat(SCREAMING_SNAKE_CASE__       ,       axis=0 )

					__UpperCAmelCase    :     Any													=	{0: self.to_torch(SCREAMING_SNAKE_CASE__ )}
					__UpperCAmelCase    :     Optional[int]													=	(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    :     List[str]													=	randn_tensor(SCREAMING_SNAKE_CASE__       ,       device=self.unet.device )
					__UpperCAmelCase    :     Dict													=	self.reset_xa(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       ,       self.action_dim )
					__UpperCAmelCase    :     Union[str, Any]													=	self.to_torch(SCREAMING_SNAKE_CASE__ )

					# run the diffusion process
					__UpperCAmelCase    :     List[str]													=	self.run_diffusion(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__ )

					# sort output trajectories by value
					__UpperCAmelCase    :     List[str]													=	y.argsort(0       ,       descending=SCREAMING_SNAKE_CASE__ ).squeeze()
					__UpperCAmelCase    :     int													=	x[sorted_idx]
					__UpperCAmelCase    :     Optional[int]													=	sorted_values[:, :, : self.action_dim]
					__UpperCAmelCase    :     Optional[Any]													=	actions.detach().cpu().numpy()
					__UpperCAmelCase    :     Dict													=	self.de_normalize(SCREAMING_SNAKE_CASE__       ,       key='''actions''' )

					# select the action with the highest value
					if y is not None:
							__UpperCAmelCase    :     str													=	0
					else:
							# if we didn't run value guiding, select a random action
							__UpperCAmelCase    :     List[Any]													=	np.random.randint(0       ,       SCREAMING_SNAKE_CASE__ )

					__UpperCAmelCase    :     Tuple													=	denorm_actions[selected_index, 0]
					return denorm_actions









import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_      : Dict  					=       {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      snake_case__				:     List[str]        =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__				:     Optional[Any]        =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            snake_case__				:     str        =						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            snake_case__				:     List[Any]        =						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     )

             a_					:		int							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]     )

             a_					:		List[str]							=  text_classifier(['This is great !', 'This is bad']     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=1     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             # Legacy behavior
             a_					:		Union[str, Any]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		List[str]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]]     )

             a_					:		int							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		str							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     {'label': 'LABEL_0', 'score': 0.504},
                     {'label': 'LABEL_0', 'score': 0.504},
                 ]     ,							)
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             import torch

             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     ,							device=torch.device('cpu'     )     ,							)

             a_					:		Any							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		List[str]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='tf'     )

             a_					:		Optional[int]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @slow
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_					:		List[str]							=  pipeline('text-classification'     )

             a_					:		Dict							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		Union[str, Any]							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Tuple							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      @slow
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             a_					:		Dict							=  pipeline('text-classification'     ,							framework='tf'     )

             a_					:		Optional[Any]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		int							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Optional[int]							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		Optional[Any]							=  TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__     ,							tokenizer=SCREAMING_SNAKE_CASE__     )
             return text_classifier, ["HuggingFace is in", "This is another test"]

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Union[str, Any]:
             a_					:		List[str]							=  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             a_					:		Union[str, Any]							=  'HuggingFace is in'
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )

             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     )
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )

             a_					:		Union[str, Any]							=  ['HuggingFace is in ', 'Paris is in France']
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}, {'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     ,							)
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )
             self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()     )

             # Forcing to get all results with `top_k=None`
             # This is NOT the legacy format
             a_					:		List[Any]							=  text_classifier(SCREAMING_SNAKE_CASE__     ,							top_k=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  len(model.config.idalabel.values()     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N]     ,							)

             a_					:		int							=  {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
             a_					:		Optional[int]							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}     ,							)
             self.assertTrue(outputs['label'] in model.config.idalabel.values()     )

             # This might be used a text pair, but tokenizer + pipe interaction
             # makes it hard to understand that it's not using the pair properly
             # https://github.com/huggingface/transformers/issues/17305
             # We disabled this usage instead as it was outputting wrong outputs.
             a_					:		Any							=  [['HuggingFace is in ', 'Paris is in France']]
             with self.assertRaises(SCREAMING_SNAKE_CASE__     ):
                    text_classifier(SCREAMING_SNAKE_CASE__     )

             # This used to be valid for doing text pairs
             # We're keeping it working because of backward compatibility
             a_					:		Tuple							=  text_classifier([[['HuggingFace is in ', 'Paris is in France']]]     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     ,							)
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )


