


from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )







@require_tf
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			XGLMConfig
     SCREAMING_SNAKE_CASE__      			=			{}
     SCREAMING_SNAKE_CASE__      			=			'''gelu'''





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_4		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=2		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=0.02		,     )     ->   List[Any]:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =seq_length
      _lowercase							      =is_training
      _lowercase							      =use_input_mask
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =d_model
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =ffn_dim
      _lowercase							      =activation_function
      _lowercase							      =activation_dropout
      _lowercase							      =attention_dropout
      _lowercase							      =max_position_embeddings
      _lowercase							      =initializer_range
      _lowercase							      =None
      _lowercase							      =0
      _lowercase							      =2
      _lowercase							      =1





     def      __A					(self       )     ->   List[str]:
      return XGLMConfig.from_pretrained('''facebook/xglm-564M'''       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =tf.clip_by_value(
          ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )		,     clip_value_min=0		,     clip_value_max=3       )

      _lowercase							      =None
      if self.use_input_mask:
       _lowercase							      =random_attention_mask([self.batch_size, self.seq_length]       )

      _lowercase							      =self.get_config()

      _lowercase							      =floats_tensor([self.num_hidden_layers, self.num_attention_heads]		,     2       )

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
      )





     def      __A					(self       )     ->   int:
      return XGLMConfig(
          vocab_size=self.vocab_size		,     d_model=self.hidden_size		,     num_layers=self.num_hidden_layers		,     attention_heads=self.num_attention_heads		,     ffn_dim=self.ffn_dim		,     activation_function=self.activation_function		,     activation_dropout=self.activation_dropout		,     attention_dropout=self.attention_dropout		,     max_position_embeddings=self.max_position_embeddings		,     initializer_range=self.initializer_range		,     use_cache=UpperCAmelCase		,     bos_token_id=self.bos_token_id		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.pad_token_id		,     return_dict=UpperCAmelCase		,     )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.prepare_config_and_inputs()

      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =config_and_inputs

      _lowercase							      ={
          '''input_ids''': input_ids,
          '''head_mask''': head_mask,
      }

      return config, inputs_dict







@require_tf
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(TFXGLMForCausalLM,) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
     )
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =TFXGLMModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     n_embd=3_7       )





     def      __A					(self       )     ->   Tuple:
      self.config_tester.run_common_tests()





     @slow
     def      __A					(self       )     ->   str:
      for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =TFXGLMModel.from_pretrained(UpperCAmelCase       )
       self.assertIsNotNone(UpperCAmelCase       )





     @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''       )
     def      __A					(self       )     ->   Optional[Any]:
      super().test_resize_token_embeddings()







@require_tf
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     def      __A					(self		,     UpperCAmelCase=True       )     ->   str:
      _lowercase							      =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
      _lowercase							      =tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]		,     dtype=tf.intaa       )  # The dog
      # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
      # fmt: off
      _lowercase							      =[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
      # fmt: on
      _lowercase							      =model.generate(UpperCAmelCase		,     do_sample=UpperCAmelCase		,     num_beams=1       )
      if verify_outputs:
       self.assertListEqual(output_ids[0].numpy().tolist()		,     UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   int:
      _lowercase							      =XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''       )
      _lowercase							      =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )

      tf.random.set_seed(0       )
      _lowercase							      =tokenizer('''Today is a nice day and'''		,     return_tensors='''tf'''       )
      _lowercase							      =tokenized.input_ids
      # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
      with tf.device(''':/CPU:0'''       ):
       _lowercase							      =model.generate(UpperCAmelCase		,     do_sample=UpperCAmelCase		,     seed=[7, 0]       )
      _lowercase							      =tokenizer.decode(output_ids[0]		,     skip_special_tokens=UpperCAmelCase       )

      _lowercase							      =(
          '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
      )
      self.assertEqual(UpperCAmelCase		,     UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   str:
      _lowercase							      =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
      _lowercase							      =XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''       )

      _lowercase							      ='''left'''

      # use different length sentences to test batching
      _lowercase							      =[
          '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
          '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
          '''regardless of whether left padding is applied or not. When''',
          '''Hello, my dog is a little''',
      ]

      _lowercase							      =tokenizer(UpperCAmelCase		,     return_tensors='''tf'''		,     padding=UpperCAmelCase       )
      _lowercase							      =inputs['''input_ids''']

      _lowercase							      =model.generate(input_ids=UpperCAmelCase		,     attention_mask=inputs['''attention_mask''']		,     max_new_tokens=1_2       )

      _lowercase							      =tokenizer(sentences[0]		,     return_tensors='''tf'''       ).input_ids
      _lowercase							      =model.generate(input_ids=UpperCAmelCase		,     max_new_tokens=1_2       )

      _lowercase							      =tokenizer(sentences[1]		,     return_tensors='''tf'''       ).input_ids
      _lowercase							      =model.generate(input_ids=UpperCAmelCase		,     max_new_tokens=1_2       )

      _lowercase							      =tokenizer.batch_decode(UpperCAmelCase		,     skip_special_tokens=UpperCAmelCase       )
      _lowercase							      =tokenizer.decode(output_non_padded[0]		,     skip_special_tokens=UpperCAmelCase       )
      _lowercase							      =tokenizer.decode(output_padded[0]		,     skip_special_tokens=UpperCAmelCase       )

      _lowercase							      =[
          '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
          '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
          '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
          '''a single''',
          '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
      ]
      self.assertListEqual(UpperCAmelCase		,     UpperCAmelCase       )
      self.assertListEqual(UpperCAmelCase		,     [non_padded_sentence, padded_sentence]       )



"""simple docstring"""





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(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       Optional[int]:






							'''simple docstring'''
							super().__init__()
							_UpperCamelCase							 =  value_function
							_UpperCamelCase							 =  unet
							_UpperCamelCase							 =  scheduler
							_UpperCamelCase							 =  env
							_UpperCamelCase							 =  env.get_dataset()
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].mean()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].std()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  env.observation_space.shape[0]
							_UpperCamelCase							 =  env.action_space.shape[0]



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       int:






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



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       List[str]:






							'''simple docstring'''
							return x_in * self.stds[key] + self.means[key]



		def 						UpperCAmelCase							(					self				,		__a)       ->       Union[str, Any]:






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



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       str:






							'''simple docstring'''
							for key, val in cond.items():
												_UpperCamelCase							 =  val.clone()
							return x_in



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  x.shape[0]
							_UpperCamelCase							 =  None
							for i in tqdm.tqdm(self.scheduler.timesteps):
												# create batch of timesteps to pass into model
												_UpperCamelCase							 =  torch.full((batch_size,)				,		__a				,		device=self.unet.device				,		dtype=torch.long)
												for _ in range(__a):
																	with torch.enable_grad():
																						x.requires_grad_()

																						# permute to match dimension for pre-trained models
																						_UpperCamelCase							 =  self.value_function(x.permute(0				,		2				,		1)				,		__a).sample
																						_UpperCamelCase							 =  torch.autograd.grad([y.sum()]				,		[x])[0]

																						_UpperCamelCase							 =  self.scheduler._get_variance(__a)
																						_UpperCamelCase							 =  torch.exp(0.5 * posterior_variance)
																						_UpperCamelCase							 =  model_std * grad

																	_UpperCamelCase							 =  0
																	_UpperCamelCase							 =  x.detach()
																	_UpperCamelCase							 =  x + scale * grad
																	_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)

												_UpperCamelCase							 =  self.unet(x.permute(0				,		2				,		1)				,		__a).sample.permute(0				,		2				,		1)

												# TODO: verify deprecation of this kwarg
												_UpperCamelCase							 =  self.scheduler.step(__a				,		__a				,		__a				,		predict_epsilon=__a)['''prev_sample''']

												# apply conditions to the trajectory (set the initial state)
												_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
												_UpperCamelCase							 =  self.to_torch(__a)
							return x, y




		def __call__(					self				,		__a				,		__a=64				,		__a=32				,		__a=2				,		__a=0.1)       ->       Optional[Any]:






							'''simple docstring'''
							# normalize the observations and create  batch dimension
							_UpperCamelCase							 =  self.normalize(__a				,		'''observations''')
							_UpperCamelCase							 =  obs[None].repeat(__a				,		axis=0)

							_UpperCamelCase							 =  {0: self.to_torch(__a)}
							_UpperCamelCase							 =  (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							 =  randn_tensor(__a				,		device=self.unet.device)
							_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
							_UpperCamelCase							 =  self.to_torch(__a)

							# run the diffusion process
							_UpperCamelCase ,  _UpperCamelCase							 =  self.run_diffusion(__a				,		__a				,		__a				,		__a)

							# sort output trajectories by value
							_UpperCamelCase							 =  y.argsort(0				,		descending=__a).squeeze()
							_UpperCamelCase							 =  x[sorted_idx]
							_UpperCamelCase							 =  sorted_values[:, :, : self.action_dim]
							_UpperCamelCase							 =  actions.detach().cpu().numpy()
							_UpperCamelCase							 =  self.de_normalize(__a				,		key='''actions''')

							# select the action with the highest value
							if y is not None:
												_UpperCamelCase							 =  0
							else:
												# if we didn't run value guiding, select a random action
												_UpperCamelCase							 =  np.random.randint(0				,		__a)

							_UpperCamelCase							 =  denorm_actions[selected_index, 0]
							return denorm_actions






