
"""simple docstring"""







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.
lowerCamelCase__           =	{"""LayoutLMv2Config""", """LayoutLMv3Config"""}




@is_pipeline_test
class 				A__ (			unittest.TestCase):
		A_		: List[Any]            =			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
		A_		: str            =			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

		if model_mapping is not None:
				A_		: Optional[Any]            =			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
		if tf_model_mapping is not None:
				A_		: int            =			{
				    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
				}

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   pipeline(
			    task='text-classification' ,	model='hf-internal-testing/tiny-random-distilbert' ,	framework='pt'				)

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

			__lowerCAmelCase    :		int		    =   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}]				)

			__lowerCAmelCase    :		List[Any]		    =   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}],
			    ] ,	)

			__lowerCAmelCase    :		List[Any]		    =   text_classifier('This is great !' ,	top_k=1				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'LABEL_0', 'score': 0.504}]				)

			# Legacy behavior
			__lowerCAmelCase    :		int		    =   text_classifier('This is great !' ,	return_all_scores=_SCREAMING_SNAKE_CASE				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'LABEL_0', 'score': 0.504}]				)

			__lowerCAmelCase    :		Optional[int]		    =   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}]]				)

			__lowerCAmelCase    :		Tuple		    =   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}],
			    ] ,	)

			__lowerCAmelCase    :		Optional[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_0', 'score': 0.504},
			    ] ,	)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			import torch

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

			__lowerCAmelCase    :		Any		    =   text_classifier('This is great !'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'LABEL_0', 'score': 0.504}]				)

		@require_tf
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   pipeline(
			    task='text-classification' ,	model='hf-internal-testing/tiny-random-distilbert' ,	framework='tf'				)

			__lowerCAmelCase    :		Union[str, Any]		    =   text_classifier('This is great !'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'LABEL_0', 'score': 0.504}]				)

		@slow
		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   pipeline('text-classification'				)

			__lowerCAmelCase    :		int		    =   text_classifier('This is great !'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'POSITIVE', 'score': 1.0}]				)
			__lowerCAmelCase    :		Any		    =   text_classifier('This is bad !'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'NEGATIVE', 'score': 1.0}]				)
			__lowerCAmelCase    :		str		    =   text_classifier('Birds are a type of animal'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'POSITIVE', 'score': 0.988}]				)

		@slow
		@require_tf
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   pipeline('text-classification' ,	framework='tf'				)

			__lowerCAmelCase    :		List[Any]		    =   text_classifier('This is great !'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'POSITIVE', 'score': 1.0}]				)
			__lowerCAmelCase    :		Union[str, Any]		    =   text_classifier('This is bad !'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'NEGATIVE', 'score': 1.0}]				)
			__lowerCAmelCase    :		Optional[Any]		    =   text_classifier('Birds are a type of animal'				)
			self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE				) ,	[{'label': 'POSITIVE', 'score': 0.988}]				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   TextClassificationPipeline(model=_SCREAMING_SNAKE_CASE ,	tokenizer=_SCREAMING_SNAKE_CASE				)
			return text_classifier, ["HuggingFace is in", "This is another test"]







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   text_classifier.model
			# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
			__lowerCAmelCase    :		str		    =   "HuggingFace is in"
			__lowerCAmelCase    :		str		    =   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()				)

			__lowerCAmelCase    :		Dict		    =   ["HuggingFace is in ", "Paris is in France"]
			__lowerCAmelCase    :		Tuple		    =   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
			__lowerCAmelCase    :		Optional[int]		    =   text_classifier(_SCREAMING_SNAKE_CASE ,	top_k=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   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] ,	)

			__lowerCAmelCase    :		Union[str, Any]		    =   {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
			__lowerCAmelCase    :		Dict		    =   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.
			__lowerCAmelCase    :		Optional[int]		    =   [["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
			__lowerCAmelCase    :		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()				)

"""simple docstring"""







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCamelCase__           =	{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
							lowerCamelCase__           =	"""hopper-medium-v2"""
							lowerCamelCase__           =	gym.make(env_name)

							lowerCamelCase__           =	ValueGuidedRLPipeline.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32""",
							    env=env,
							)

							env.seed(0)
							lowerCamelCase__           =	env.reset()
							lowerCamelCase__           =	0
							lowerCamelCase__           =	0
							lowerCamelCase__           =	1_000
							lowerCamelCase__           =	[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCamelCase__           =	pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__           =	env.step(denorm_actions)
																					lowerCamelCase__           =	env.get_normalized_score(total_reward)

																					# update return
																					total_reward += reward
																					total_score += score
																					print(
																					    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																					    f' {total_score}'
																					)

																					# save observations for rendering
																					rollout.append(next_observation.copy())

																					lowerCamelCase__           =	next_observation
							except KeyboardInterrupt:
														pass

							print(f'Total reward: {total_reward}')