
"""simple docstring"""







from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    AutoTokenizer,
							    TFAutoModelForCausalLM,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSpeechSeqaSeq,
							    TFAutoModelForVisionaSeq,
							    TFBartForConditionalGeneration,
							    TFLogitsProcessorList,
							    TFMinLengthLogitsProcessor,
							    tf_top_k_top_p_filtering,
							)

if is_tensorflow_text_available():
							import tensorflow_text as text




@require_tf
class 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   tf.convert_to_tensor(
			    [
			        [
			            8.222_0991,  # 3rd highest value; idx. 0
			            -0.562_0044,
			            5.2322_9752,
			            4.038_6393,
			            -6.879_8378,
			            -0.5478_5802,
			            -3.201_2153,
			            2.9277_7176,
			            1.8817_1953,
			            7.3534_1276,  # 5th highest value; idx. 9
			            8.4320_7833,  # 2nd highest value; idx. 10
			            -9.8571_1836,
			            -5.9620_9236,
			            -1.1303_9161,
			            -7.111_5294,
			            -0.836_9633,
			            -5.318_6408,
			            7.0642_7407,
			            0.8136_9344,
			            -0.8202_3817,
			            -5.917_9796,
			            0.5881_3443,
			            -6.9977_8438,
			            4.7155_1189,
			            -0.1877_1637,
			            7.4402_0759,  # 4th highest value; idx. 25
			            9.3845_0987,  # 1st highest value; idx. 26
			            2.1266_2941,
			            -9.3256_2038,
			            2.3565_2522,
			        ],  # cummulative prob of 5 highest values <= 0.6
			        [
			            0.5842_5518,
			            4.5313_9238,
			            -5.5751_0464,
			            -6.2803_0699,
			            -7.1952_9503,
			            -4.0212_2551,
			            1.3933_7037,
			            -6.0670_7057,
			            1.5948_0517,
			            -9.64_3119,
			            0.0390_7799,
			            0.6723_1762,
			            -8.8820_6726,
			            6.2711_5922,  # 4th highest value; idx. 13
			            2.2852_0723,
			            4.8276_7506,
			            4.3042_1368,
			            8.827_5313,  # 2nd highest value; idx. 17
			            5.4402_9958,  # 5th highest value; idx. 18
			            -4.473_5794,
			            7.3857_9536,  # 3rd highest value; idx. 20
			            -2.9105_1663,
			            2.6194_6077,
			            -2.567_4762,
			            -9.4895_9302,
			            -4.0292_2645,
			            -1.3541_6918,
			            9.6770_2323,  # 1st highest value; idx. 27
			            -5.8947_8553,
			            1.8537_0467,
			        ],  # cummulative prob of 5 highest values <= 0.6
			    ] ,	dtype=tf.floataa ,	)

			__lowerCAmelCase    :		List[str]		    =   tf.convert_to_tensor(
			    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] ,	dtype=tf.intaa ,	)  # expected non filtered idx as noted above

			__lowerCAmelCase    :		Tuple		    =   tf.convert_to_tensor(
			    [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] ,	dtype=tf.floataa ,	)  # expected non filtered values as noted above

			__lowerCAmelCase    :		Dict		    =   tf_top_k_top_p_filtering(_SCREAMING_SNAKE_CASE ,	top_k=10 ,	top_p=0.6 ,	min_tokens_to_keep=4				)

			__lowerCAmelCase    :		Optional[int]		    =   output[output != -float('inf'				)]
			__lowerCAmelCase    :		Dict		    =   tf.cast(
			    tf.where(tf.not_equal(_SCREAMING_SNAKE_CASE ,	tf.constant(-float('inf'				) ,	dtype=tf.floataa				)				)				) ,	dtype=tf.intaa ,	)

			tf.debugging.assert_near(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	rtol=1E-12				)
			tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)




@require_tf
class 				A__ (			unittest.TestCase ,  _lowerCamelCase):
		# setting framework_dependent_parameters needs to be gated, just like its contents' imports
		if is_tf_available():
				A_		: Union[str, Any]            =			{
				    'AutoModelForCausalLM': TFAutoModelForCausalLM,
				    'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
				    'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
				    'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
				    'LogitsProcessorList': TFLogitsProcessorList,
				    'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
				    'create_tensor_fn': tf.convert_to_tensor,
				    'floats_tensor': floats_tensor,
				    'return_tensors': 'tf',
				}

		@slow
		def 							__lowerCamelCase							(   self				):
			# TF-only test: tf.saved_model export
			__lowerCAmelCase    :		List[Any]		    =   TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
			__lowerCAmelCase    :		Union[str, Any]		    =   2
			__lowerCAmelCase    :		Tuple		    =   2




			class 				A__ (			tf.Module):

					def __init__(   self ,	_SCREAMING_SNAKE_CASE				):
						super(_SCREAMING_SNAKE_CASE ,	self				).__init__()
						__lowerCAmelCase    :		Optional[int]		    =   model







					@tf.function(
					    input_signature=(
					        tf.TensorSpec((None, input_length) ,	tf.intaa ,	name='input_ids'				),
					        tf.TensorSpec((None, input_length) ,	tf.intaa ,	name='attention_mask'				),
					    ) ,	jit_compile=_SCREAMING_SNAKE_CASE ,	)
					def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
						__lowerCAmelCase    :		Dict		    =   self.model.generate(
						    input_ids=_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE ,	max_new_tokens=_SCREAMING_SNAKE_CASE ,	return_dict_in_generate=_SCREAMING_SNAKE_CASE ,	)
						return {"sequences": outputs["sequences"]}



			__lowerCAmelCase    :		int		    =   [[2, 0], [1_02, 1_03]]
			__lowerCAmelCase    :		Dict		    =   [[1, 0], [1, 1]]
			__lowerCAmelCase    :		Union[str, Any]		    =   DummyModel(model=_SCREAMING_SNAKE_CASE				)
			with tempfile.TemporaryDirectory() as tmp_dir:
				tf.saved_model.save(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	signatures={'serving_default': dummy_model.serving}				)
				__lowerCAmelCase    :		List[Any]		    =   tf.saved_model.load(_SCREAMING_SNAKE_CASE				).signatures['serving_default']
				for batch_size in range(1 ,	len(_SCREAMING_SNAKE_CASE				) + 1				):
					__lowerCAmelCase    :		Dict		    =   {
					    'input_ids': tf.constant(dummy_input_ids[:batch_size]				),
					    'attention_mask': tf.constant(dummy_attention_masks[:batch_size]				),
					}
					__lowerCAmelCase    :		List[str]		    =   serving_func(**_SCREAMING_SNAKE_CASE				)['sequences']
					__lowerCAmelCase    :		Optional[int]		    =   test_model.generate(**_SCREAMING_SNAKE_CASE ,	max_new_tokens=_SCREAMING_SNAKE_CASE				)
					tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@slow
		def 							__lowerCamelCase							(   self				):
			# TF-only test: tf.saved_model export
			__lowerCAmelCase    :		Dict		    =   TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
			__lowerCAmelCase    :		Dict		    =   1
			__lowerCAmelCase    :		Dict		    =   2




			class 				A__ (			tf.Module):

					def __init__(   self ,	_SCREAMING_SNAKE_CASE				):
						super(_SCREAMING_SNAKE_CASE ,	self				).__init__()
						__lowerCAmelCase    :		List[Any]		    =   model







					@tf.function(
					    input_signature=(
					        tf.TensorSpec((batch_size, None) ,	tf.intaa ,	name='input_ids'				),
					        tf.TensorSpec((batch_size, None) ,	tf.intaa ,	name='attention_mask'				),
					    ) ,	jit_compile=_SCREAMING_SNAKE_CASE ,	)
					def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
						__lowerCAmelCase    :		Optional[int]		    =   self.model.generate(
						    input_ids=_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE ,	max_new_tokens=_SCREAMING_SNAKE_CASE ,	return_dict_in_generate=_SCREAMING_SNAKE_CASE ,	)
						return {"sequences": outputs["sequences"]}



			__lowerCAmelCase    :		str		    =   [[2], [1_02, 1_03]]
			__lowerCAmelCase    :		Optional[int]		    =   [[1], [1, 1]]
			__lowerCAmelCase    :		str		    =   DummyModel(model=_SCREAMING_SNAKE_CASE				)
			with tempfile.TemporaryDirectory() as tmp_dir:
				tf.saved_model.save(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	signatures={'serving_default': dummy_model.serving}				)
				__lowerCAmelCase    :		int		    =   tf.saved_model.load(_SCREAMING_SNAKE_CASE				).signatures['serving_default']
				for input_row in range(len(_SCREAMING_SNAKE_CASE				)				):
					__lowerCAmelCase    :		Union[str, Any]		    =   {
					    'input_ids': tf.constant([dummy_input_ids[input_row]]				),
					    'attention_mask': tf.constant([dummy_attention_masks[input_row]]				),
					}
					__lowerCAmelCase    :		List[Any]		    =   serving_func(**_SCREAMING_SNAKE_CASE				)['sequences']
					__lowerCAmelCase    :		Tuple		    =   test_model.generate(**_SCREAMING_SNAKE_CASE ,	max_new_tokens=_SCREAMING_SNAKE_CASE				)
					tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@slow
		@require_tensorflow_text
		def 							__lowerCamelCase							(   self				):
			# TF-only test: tf.saved_model export
			with tempfile.TemporaryDirectory() as tmp_dir:
				# file needed to load the TF tokenizer
				hf_hub_download(repo_id='google/flan-t5-small' ,	filename='spiece.model' ,	local_dir=_SCREAMING_SNAKE_CASE				)




				class 				A__ (			tf.keras.layers.Layer):

						def __init__(   self				):
							super().__init__()
							__lowerCAmelCase    :		Tuple		    =   text.SentencepieceTokenizer(
							    model=tf.io.gfile.GFile(os.path.join(_SCREAMING_SNAKE_CASE ,	'spiece.model'				) ,	'rb'				).read()				)
							__lowerCAmelCase    :		Optional[int]		    =   TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5'				)







						def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
							__lowerCAmelCase    :		List[str]		    =   self.tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
							__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[int]		    =   text.pad_model_inputs(
							    _SCREAMING_SNAKE_CASE ,	max_seq_length=64 ,	pad_value=self.model.config.pad_token_id				)
							__lowerCAmelCase    :		Tuple		    =   self.model.generate(input_ids=_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE				)
							return self.tokenizer.detokenize(_SCREAMING_SNAKE_CASE				)



				__lowerCAmelCase    :		List[Any]		    =   CompleteSentenceTransformer()
				__lowerCAmelCase    :		Union[str, Any]		    =   tf.keras.layers.Input(shape=(1,) ,	dtype=tf.string ,	name='inputs'				)
				__lowerCAmelCase    :		List[str]		    =   complete_model(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Any		    =   tf.keras.Model(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
				keras_model.save(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			# Has PT equivalent: this test relies on random sampling
			__lowerCAmelCase    :		str		    =   {
			    'do_sample': True,
			    'num_beams': 1,
			    'top_p': 0.7,
			    'top_k': 10,
			    'temperature': 0.7,
			}
			__lowerCAmelCase    :		Optional[Any]		    =   14

			__lowerCAmelCase    :		List[str]		    =   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
			__lowerCAmelCase    :		Any		    =   'Hello, my dog is cute and'
			__lowerCAmelCase    :		Tuple		    =   tokenizer(_SCREAMING_SNAKE_CASE ,	return_tensors='tf'				)
			__lowerCAmelCase    :		List[Any]		    =   TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)

			__lowerCAmelCase    :		Tuple		    =   6_38
			# forces the generation to happen on CPU, to avoid GPU-related quirks
			with tf.device(':/CPU:0'				):
				tf.random.set_seed(0				)
				__lowerCAmelCase    :		Union[str, Any]		    =   model.generate(**_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
			self.assertTrue(expectation == len(generated_tokens[0]				)				)

			__lowerCAmelCase    :		List[str]		    =   [6_38, 1_98]
			with tf.device(':/CPU:0'				):
				tf.random.set_seed(0				)
				__lowerCAmelCase    :		Optional[Any]		    =   model.generate(**_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
			self.assertTrue(expectation == len(generated_tokens[0]				)				)







		def 							__lowerCamelCase							(   self				):
			# Has PT equivalent: ample use of framework-specific code
			__lowerCAmelCase    :		List[str]		    =   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart'				)
			__lowerCAmelCase    :		Optional[Any]		    =   'Hugging Face is a technology company based in New York and Paris.'
			__lowerCAmelCase    :		Dict		    =   bart_tokenizer(_SCREAMING_SNAKE_CASE ,	return_tensors='tf'				).input_ids
			__lowerCAmelCase    :		int		    =   TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart'				)
			__lowerCAmelCase    :		List[Any]		    =   bart_model.generate(_SCREAMING_SNAKE_CASE				).numpy()




			class 				A__ (			_lowerCamelCase):

					def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE				):
						return super().call(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)



			__lowerCAmelCase    :		Optional[Any]		    =   FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart'				)
			__lowerCAmelCase    :		str		    =   bart_model.generate(_SCREAMING_SNAKE_CASE ,	foo='bar'				).numpy()
			self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)




			class 				A__ (			bart_model.model.encoder.__class__):

					def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
						return super().call(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)



			__lowerCAmelCase    :		Union[str, Any]		    =   FakeEncoder(bart_model.config ,	bart_model.model.shared				)
			__lowerCAmelCase    :		Any		    =   fake_encoder

			# Normal generation still works (the output will be different because the encoder weights are different)
			__lowerCAmelCase    :		str		    =   bart_model.generate(_SCREAMING_SNAKE_CASE				).numpy()
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):
				# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
				bart_model.generate(_SCREAMING_SNAKE_CASE ,	foo='bar'				)

"""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}')