

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					_UpperCAmelCase						:  Union[str, Any]									=			"""platform"""

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				):







							'''simple docstring'''






							if attention_mask is None:
														snake_case_     							=       np.where(input_ids != config.pad_token_id  ,				1  ,				0 )
							if decoder_attention_mask is None:
														snake_case_     							=       np.where(decoder_input_ids != config.pad_token_id  ,				1  ,				0 )
							if head_mask is None:
														snake_case_     							=       np.ones((config.encoder_layers, config.encoder_attention_heads) )
							if decoder_head_mask is None:
														snake_case_     							=       np.ones((config.decoder_layers, config.decoder_attention_heads) )
							if cross_attn_head_mask is None:
														snake_case_     							=       np.ones((config.decoder_layers, config.decoder_attention_heads) )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}







class 					lowercase							:




				def __init__(   self ,  snake_case ,  snake_case=13 ,  snake_case=7 ,  snake_case=True ,  snake_case=False ,  snake_case=99 ,  snake_case=16 ,  snake_case=2 ,  snake_case=4 ,  snake_case=4 ,  snake_case="gelu" ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=32 ,  snake_case=2 ,  snake_case=1 ,  snake_case=0 ,  snake_case=0.02 ,  ):
											snake_case_     							=       parent
											snake_case_     							=       batch_size
											snake_case_     							=       seq_length
											snake_case_     							=       is_training
											snake_case_     							=       use_labels
											snake_case_     							=       vocab_size
											snake_case_     							=       hidden_size
											snake_case_     							=       num_hidden_layers
											snake_case_     							=       num_attention_heads
											snake_case_     							=       intermediate_size
											snake_case_     							=       hidden_act
											snake_case_     							=       hidden_dropout_prob
											snake_case_     							=       attention_probs_dropout_prob
											snake_case_     							=       max_position_embeddings
											snake_case_     							=       eos_token_id
											snake_case_     							=       pad_token_id
											snake_case_     							=       bos_token_id
											snake_case_     							=       initializer_range




				def        a				(   self			):
											snake_case_     							=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size			) ,  3 ,  self.vocab_size			)
											snake_case_     							=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,  dtype=np.intaa			)) ,  -1			)

											snake_case_     							=       shift_tokens_right(snake_case ,  1 ,  2			)

											snake_case_     							=       BlenderbotConfig(
											    vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_id=self.eos_token_id ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  initializer_range=self.initializer_range ,  use_cache=snake_case ,  )
											snake_case_     							=       prepare_blenderbot_inputs_dict(snake_case ,  snake_case ,  snake_case			)
											return config, inputs_dict




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.prepare_config_and_inputs()
											return config, inputs_dict




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       20
											snake_case_     							=       model_class_name(snake_case			)

											snake_case_     							=       model.encode(inputs_dict['input_ids']			)

											snake_case_							,       snake_case_     							=       (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											snake_case_     							=       model.init_cache(decoder_input_ids.shape[0] ,  snake_case ,  snake_case			)
											snake_case_     							=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,  dtype='i4'			)

											snake_case_     							=       jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )
											snake_case_     							=       model.decode(
											    decoder_input_ids[:, :-1] ,  snake_case ,  decoder_attention_mask=snake_case ,  past_key_values=snake_case ,  decoder_position_ids=snake_case ,  )

											snake_case_     							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4'			)
											snake_case_     							=       model.decode(
											    decoder_input_ids[:, -1:] ,  snake_case ,  decoder_attention_mask=snake_case ,  past_key_values=outputs_cache.past_key_values ,  decoder_position_ids=snake_case ,  )

											snake_case_     							=       model.decode(snake_case ,  snake_case			)

											snake_case_     							=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
											self.parent.assertTrue(diff < 1e-3 ,  msg=F'''Max diff is {diff}'''			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       20
											snake_case_     							=       model_class_name(snake_case			)

											snake_case_     							=       model.encode(inputs_dict['input_ids']			)

											snake_case_							,       snake_case_     							=       (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											snake_case_     							=       jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
											    ] ,  axis=-1 ,  )

											snake_case_     							=       model.init_cache(decoder_input_ids.shape[0] ,  snake_case ,  snake_case			)
											snake_case_     							=       jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )

											snake_case_     							=       model.decode(
											    decoder_input_ids[:, :-1] ,  snake_case ,  decoder_attention_mask=snake_case ,  past_key_values=snake_case ,  decoder_position_ids=snake_case ,  )
											snake_case_     							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4'			)
											snake_case_     							=       model.decode(
											    decoder_input_ids[:, -1:] ,  snake_case ,  past_key_values=outputs_cache.past_key_values ,  decoder_attention_mask=snake_case ,  decoder_position_ids=snake_case ,  )

											snake_case_     							=       model.decode(snake_case ,  snake_case ,  decoder_attention_mask=snake_case			)

											snake_case_     							=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
											self.parent.assertTrue(diff < 1e-3 ,  msg=F'''Max diff is {diff}'''			)







@require_flax
class 					lowercase							(			unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Any			     =     99




				def        a				(   self			):
											snake_case_     							=       np.array(
											    [
											        [71, 82, 18, 33, 46, 91, 2],
											        [68, 34, 26, 58, 30, 82, 2],
											        [5, 97, 17, 39, 94, 40, 2],
											        [76, 83, 94, 25, 70, 78, 2],
											        [87, 59, 41, 35, 48, 66, 2],
											        [55, 13, 16, 58, 5, 2, 1],  # note padding
											        [64, 27, 31, 51, 12, 75, 2],
											        [52, 64, 86, 17, 83, 39, 2],
											        [48, 61, 9, 24, 71, 82, 2],
											        [26, 1, 60, 48, 22, 13, 2],
											        [21, 5, 62, 28, 14, 76, 2],
											        [45, 98, 37, 86, 59, 48, 2],
											        [70, 70, 50, 9, 28, 0, 2],
											    ] ,  dtype=np.intaa ,  )

											snake_case_     							=       input_ids.shape[0]
											snake_case_     							=       BlenderbotConfig(
											    vocab_size=self.vocab_size ,  d_model=24 ,  encoder_layers=2 ,  decoder_layers=2 ,  encoder_attention_heads=2 ,  decoder_attention_heads=2 ,  encoder_ffn_dim=32 ,  decoder_ffn_dim=32 ,  max_position_embeddings=48 ,  eos_token_id=2 ,  pad_token_id=1 ,  bos_token_id=0 ,  )
											return config, input_ids, batch_size




				def        a				(   self			):
											snake_case_							,       snake_case_							,       snake_case_     							=       self._get_config_and_data()
											snake_case_     							=       FlaxBlenderbotForConditionalGeneration(snake_case			)
											snake_case_     							=       lm_model(input_ids=snake_case			)
											snake_case_     							=       (batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       BlenderbotConfig(
											    vocab_size=self.vocab_size ,  d_model=14 ,  encoder_layers=2 ,  decoder_layers=2 ,  encoder_attention_heads=2 ,  decoder_attention_heads=2 ,  encoder_ffn_dim=8 ,  decoder_ffn_dim=8 ,  max_position_embeddings=48 ,  )
											snake_case_     							=       FlaxBlenderbotForConditionalGeneration(snake_case			)
											snake_case_     							=       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,  dtype=np.intaa			)
											snake_case_     							=       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,  dtype=np.intaa			)
											snake_case_     							=       lm_model(input_ids=snake_case ,  decoder_input_ids=snake_case			)
											snake_case_     							=       (*summary.shape, config.vocab_size)
											self.assertEqual(outputs['logits'].shape ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,  dtype=np.intaa			)
											snake_case_     							=       shift_tokens_right(snake_case ,  1 ,  2			)
											snake_case_     							=       np.equal(snake_case ,  1			).astype(np.floataa			).sum()
											snake_case_     							=       np.equal(snake_case ,  1			).astype(np.floataa			).sum()
											self.assertEqual(shifted.shape ,  input_ids.shape			)
											self.assertEqual(snake_case ,  n_pad_before - 1			)
											self.assertTrue(np.equal(shifted[:, 0] ,  2			).all()			)







@require_flax
class 					lowercase							(			lowercase_      ,    unittest.TestCase      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     True
				__SCREAMING_SNAKE_CASE   :					str			     =     (
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




				def        a				(   self			):
											snake_case_     							=       FlaxBlenderbotModelTester(self			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(snake_case ,  snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(snake_case ,  snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__			):
																									snake_case_     							=       self._prepare_for_class(snake_case ,  snake_case			)
																									snake_case_     							=       model_class(snake_case			)

																									@jax.jit
																									def encode_jitted(snake_case ,  snake_case=None ,  **snake_case			):
																																return model.encode(input_ids=snake_case ,  attention_mask=snake_case			)

																									with self.subTest('JIT Enabled'			):
																																snake_case_     							=       encode_jitted(**snake_case			).to_tuple()

																									with self.subTest('JIT Disabled'			):
																																with jax.disable_jit():
																																							snake_case_     							=       encode_jitted(**snake_case			).to_tuple()

																									self.assertEqual(len(snake_case			) ,  len(snake_case			)			)
																									for jitted_output, output in zip(snake_case ,  snake_case			):
																																self.assertEqual(jitted_output.shape ,  output.shape			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__			):
																									snake_case_     							=       model_class(snake_case			)
																									snake_case_     							=       model.encode(inputs_dict['input_ids'] ,  inputs_dict['attention_mask']			)

																									snake_case_     							=       {
																									    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																									    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																									    'encoder_outputs': encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(snake_case ,  snake_case ,  snake_case			):
																																return model.decode(
																																    decoder_input_ids=snake_case ,  decoder_attention_mask=snake_case ,  encoder_outputs=snake_case ,  )

																									with self.subTest('JIT Enabled'			):
																																snake_case_     							=       decode_jitted(**snake_case			).to_tuple()

																									with self.subTest('JIT Disabled'			):
																																with jax.disable_jit():
																																							snake_case_     							=       decode_jitted(**snake_case			).to_tuple()

																									self.assertEqual(len(snake_case			) ,  len(snake_case			)			)
																									for jitted_output, output in zip(snake_case ,  snake_case			):
																																self.assertEqual(jitted_output.shape ,  output.shape			)




				@slow
				def        a				(   self			):
											for model_class_name in self.all_model_classes:
																		snake_case_     							=       model_class_name.from_pretrained('facebook/blenderbot-400M-distill'			)
																		# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																		snake_case_     							=       np.ones((1, 1)			) * model.config.eos_token_id
																		snake_case_     							=       model(snake_case			)
																		self.assertIsNotNone(snake_case			)




				@unittest.skipUnless(jax_device != 'cpu' ,  '3B test too slow on CPU.'			)
				@slow
				def        a				(   self			):
											snake_case_     							=       {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
											snake_case_     							=       {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

											snake_case_     							=       FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' ,  from_pt=snake_case			)
											snake_case_     							=       BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'			)

											snake_case_     							=       ['Sam']
											snake_case_     							=       tokenizer(snake_case ,  return_tensors='jax'			)

											snake_case_     							=       model.generate(**snake_case ,  **snake_case			)
											snake_case_     							=       'Sam is a great name. It means "sun" in Gaelic.'

											snake_case_     							=       tokenizer.batch_decode(snake_case ,  **snake_case			)
											assert generated_txt[0].strip() == tgt_text







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


_UpperCAmelCase						:  List[Any]									=			{
    """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__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

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

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase						:  int									=			pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			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())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

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




