


"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
       import numpy as np
       import tensorflow as tf

       from transformers import (
           TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
           FlaubertConfig,
           TFFlaubertForMultipleChoice,
           TFFlaubertForQuestionAnsweringSimple,
           TFFlaubertForSequenceClassification,
           TFFlaubertForTokenClassification,
           TFFlaubertModel,
           TFFlaubertWithLMHeadModel,
       )


class 	A_ :




      '''simple docstring'''


      def __init__(    self:	int						,				a:	List[str]						,				):
       __lowerCamelCase		:							int		 =						parent
       __lowerCamelCase		:							List[str]		 =						13
       __lowerCamelCase		:							List[Any]		 =						7
       __lowerCamelCase		:							List[str]		 =						True
       __lowerCamelCase		:							List[Any]		 =						True
       __lowerCamelCase		:							int		 =						True
       __lowerCamelCase		:							List[str]		 =						True
       __lowerCamelCase		:							Any		 =						True
       __lowerCamelCase		:							List[Any]		 =						False
       __lowerCamelCase		:							Optional[Any]		 =						False
       __lowerCamelCase		:							List[str]		 =						False
       __lowerCamelCase		:							Any		 =						2
       __lowerCamelCase		:							List[str]		 =						99
       __lowerCamelCase		:							Any		 =						0
       __lowerCamelCase		:							Union[str, Any]		 =						32
       __lowerCamelCase		:							Any		 =						2
       __lowerCamelCase		:							str		 =						4
       __lowerCamelCase		:							int		 =						0.1
       __lowerCamelCase		:							Optional[Any]		 =						0.1
       __lowerCamelCase		:							Any		 =						512
       __lowerCamelCase		:							Any		 =						16
       __lowerCamelCase		:							List[Any]		 =						2
       __lowerCamelCase		:							Dict		 =						0.0_2
       __lowerCamelCase		:							List[str]		 =						3
       __lowerCamelCase		:							str		 =						4
       __lowerCamelCase		:							Dict		 =						'last'
       __lowerCamelCase		:							List[Any]		 =						True
       __lowerCamelCase		:							Union[str, Any]		 =						None
       __lowerCamelCase		:							Any		 =						0




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							Any		 =						ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size    )
       __lowerCamelCase		:							int		 =						random_attention_mask([self.batch_size, self.seq_length]						,				dtype=tf.floataa    )

       __lowerCamelCase		:							Dict		 =						None
       if self.use_input_lengths:
        __lowerCamelCase		:							List[str]		 =						(
            ids_tensor([self.batch_size]						,				vocab_size=2    ) + self.seq_length - 2
        )  # small variation of seq_length

       __lowerCamelCase		:							Optional[Any]		 =						None
       if self.use_token_type_ids:
        __lowerCamelCase		:							Optional[int]		 =						ids_tensor([self.batch_size, self.seq_length]						,				self.n_langs    )

       __lowerCamelCase		:							Dict		 =						None
       __lowerCamelCase		:							int		 =						None
       __lowerCamelCase		:							Any		 =						None
       if self.use_labels:
        __lowerCamelCase		:							List[str]		 =						ids_tensor([self.batch_size]						,				self.type_sequence_label_size    )
        __lowerCamelCase		:							List[str]		 =						ids_tensor([self.batch_size, self.seq_length]						,				self.num_labels    )
        __lowerCamelCase		:							Dict		 =						ids_tensor([self.batch_size]						,				2						,				dtype=tf.floataa    )
        __lowerCamelCase		:							Optional[Any]		 =						ids_tensor([self.batch_size]						,				self.num_choices    )

       __lowerCamelCase		:							int		 =						FlaubertConfig(
           vocab_size=self.vocab_size						,				n_special=self.n_special						,				emb_dim=self.hidden_size						,				n_layers=self.num_hidden_layers						,				n_heads=self.num_attention_heads						,				dropout=self.hidden_dropout_prob						,				attention_dropout=self.attention_probs_dropout_prob						,				gelu_activation=self.gelu_activation						,				sinusoidal_embeddings=self.sinusoidal_embeddings						,				asm=self.asm						,				causal=self.causal						,				n_langs=self.n_langs						,				max_position_embeddings=self.max_position_embeddings						,				initializer_range=self.initializer_range						,				summary_type=self.summary_type						,				use_proj=self.use_proj						,				bos_token_id=self.bos_token_id						,				)

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	Optional[Any]						,				a:	List[str]						,				a:	Optional[int]						,				a:	Dict						,				a:	List[Any]						,				a:	str						,				a:	List[Any]						,				a:	int						,				a:	Tuple						,				):
       __lowerCamelCase		:							Tuple		 =						TFFlaubertModel(config=a    )
       __lowerCamelCase		:							Dict		 =						{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       __lowerCamelCase		:							Dict		 =						model(a    )

       __lowerCamelCase		:							str		 =						[input_ids, input_mask]
       __lowerCamelCase		:							Any		 =						model(a    )
       self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	Any						,				a:	List[str]						,				a:	Optional[Any]						,				a:	Dict						,				a:	int						,				a:	Tuple						,				a:	List[str]						,				a:	Any						,				a:	str						,				):
       __lowerCamelCase		:							List[Any]		 =						TFFlaubertWithLMHeadModel(a    )

       __lowerCamelCase		:							Optional[int]		 =						{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       __lowerCamelCase		:							List[str]		 =						model(a    )

       self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.vocab_size)    )




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	int						,				a:	Tuple						,				a:	str						,				a:	Union[str, Any]						,				a:	List[Any]						,				a:	int						,				a:	Union[str, Any]						,				a:	Any						,				a:	Union[str, Any]						,				):
       __lowerCamelCase		:							str		 =						TFFlaubertForQuestionAnsweringSimple(a    )

       __lowerCamelCase		:							Dict		 =						{'input_ids': input_ids, 'lengths': input_lengths}

       __lowerCamelCase		:							List[Any]		 =						model(a    )

       self.parent.assertEqual(result.start_logits.shape						,				(self.batch_size, self.seq_length)    )
       self.parent.assertEqual(result.end_logits.shape						,				(self.batch_size, self.seq_length)    )




      def 					_snake_case     (    self:	List[Any]						,				a:	int						,				a:	str						,				a:	Dict						,				a:	Any						,				a:	List[str]						,				a:	Any						,				a:	int						,				a:	str						,				a:	Any						,				):
       __lowerCamelCase		:							Optional[Any]		 =						TFFlaubertForSequenceClassification(a    )

       __lowerCamelCase		:							Any		 =						{'input_ids': input_ids, 'lengths': input_lengths}

       __lowerCamelCase		:							List[str]		 =						model(a    )

       self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.type_sequence_label_size)    )




      def 					_snake_case     (    self:	Dict						,				a:	Union[str, Any]						,				a:	Tuple						,				a:	int						,				a:	Tuple						,				a:	str						,				a:	List[Any]						,				a:	Optional[int]						,				a:	Dict						,				a:	List[Any]						,				):
       __lowerCamelCase		:							Optional[Any]		 =						self.num_labels
       __lowerCamelCase		:							str		 =						TFFlaubertForTokenClassification(config=a    )
       __lowerCamelCase		:							Any		 =						{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
       __lowerCamelCase		:							str		 =						model(a    )
       self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.num_labels)    )




      def 					_snake_case     (    self:	List[str]						,				a:	Dict						,				a:	str						,				a:	str						,				a:	str						,				a:	Tuple						,				a:	Tuple						,				a:	Optional[Any]						,				a:	Optional[Any]						,				a:	Optional[int]						,				):
       __lowerCamelCase		:							Any		 =						self.num_choices
       __lowerCamelCase		:							List[Any]		 =						TFFlaubertForMultipleChoice(config=a    )
       __lowerCamelCase		:							str		 =						tf.tile(tf.expand_dims(a						,				1    )						,				(1, self.num_choices, 1)    )
       __lowerCamelCase		:							Any		 =						tf.tile(tf.expand_dims(a						,				1    )						,				(1, self.num_choices, 1)    )
       __lowerCamelCase		:							Optional[Any]		 =						tf.tile(tf.expand_dims(a						,				1    )						,				(1, self.num_choices, 1)    )
       __lowerCamelCase		:							Optional[Any]		 =						{
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       __lowerCamelCase		:							Dict		 =						model(a    )
       self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_choices)    )




      def 					_snake_case     (    self:	int    ):
       __lowerCamelCase		:							Any		 =						self.prepare_config_and_inputs()
       (
           __lowerCamelCase      
       )		:							List[Any]		 =						config_and_inputs
       __lowerCamelCase		:							Any		 =						{
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'langs': token_type_ids,
           'lengths': input_lengths,
       }
       return config, inputs_dict



@require_tf
class 	A_ (	SCREAMING_SNAKE_CASE__ ,				SCREAMING_SNAKE_CASE__ ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       (
          (
              TFFlaubertModel,
              TFFlaubertWithLMHeadModel,
              TFFlaubertForSequenceClassification,
              TFFlaubertForQuestionAnsweringSimple,
              TFFlaubertForTokenClassification,
              TFFlaubertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      __snake_case 	=       (
          (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      __snake_case 	=       (
          {
              """feature-extraction""": TFFlaubertModel,
              """fill-mask""": TFFlaubertWithLMHeadModel,
              """question-answering""": TFFlaubertForQuestionAnsweringSimple,
              """text-classification""": TFFlaubertForSequenceClassification,
              """token-classification""": TFFlaubertForTokenClassification,
              """zero-shot""": TFFlaubertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __snake_case 	=       False
      __snake_case 	=       False




      def 					_snake_case     (    self:	Any						,				a:	Optional[int]						,				a:	Union[str, Any]						,				a:	Optional[Any]						,				a:	Optional[Any]						,				a:	str    ):
       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('Fast'    )
       ):
        # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
        # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
        # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
        return True

       return False




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						TFFlaubertModelTester(self    )
       __lowerCamelCase		:							int		 =						ConfigTester(self						,				config_class=a						,				emb_dim=37    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       self.config_tester.run_common_tests()




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							Tuple		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_model(*a    )




      def 					_snake_case     (    self:	List[str]    ):
       __lowerCamelCase		:							str		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_lm_head(*a    )




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							str		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_qa(*a    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							Dict		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_sequence_classif(*a    )




      def 					_snake_case     (    self:	List[str]    ):
       __lowerCamelCase		:							int		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_token_classification(*a    )




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_multiple_choice(*a    )




      @slow
      def 					_snake_case     (    self:	Tuple    ):
       for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCamelCase		:							Dict		 =						TFFlaubertModel.from_pretrained(a    )
        self.assertIsNotNone(a    )



@require_tf
@require_sentencepiece
@require_tokenizers
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      @slow
      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							List[str]		 =						TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'    )

       __lowerCamelCase		:							List[Any]		 =						tf.convert_to_tensor(
           [[0, 158, 735, 2592, 1424, 6727, 82, 1]]						,				dtype=tf.intaa						,				)  # "J'aime flaubert !"

       __lowerCamelCase		:							List[str]		 =						model(a    )[0]
       __lowerCamelCase		:							List[str]		 =						tf.TensorShape((1, 8, 512)    )
       self.assertEqual(output.shape						,				a    )
       # compare the actual values for a slice.
       __lowerCamelCase		:							int		 =						tf.convert_to_tensor(
           [
               [
                   [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                   [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                   [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
               ]
           ]						,				dtype=tf.floataa						,				)

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()						,				expected_slice.numpy()						,				atol=1e-4    )    )





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


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    '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__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

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

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     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())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

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

