


import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=99		,				_UpperCAmelCase=13		,				_UpperCAmelCase=16		,				_UpperCAmelCase=7		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=2		,				_UpperCAmelCase=32		,				_UpperCAmelCase=4		,				_UpperCAmelCase=4		,				_UpperCAmelCase=30		,				_UpperCAmelCase=0		,				_UpperCAmelCase=1		,				_UpperCAmelCase=2		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				decoder_seq_length
       # For common tests
       snake_case_	=				self.decoder_seq_length
       snake_case_	=				is_training
       snake_case_	=				use_attention_mask
       snake_case_	=				use_labels

       snake_case_	=				vocab_size
       snake_case_	=				d_model
       snake_case_	=				d_model
       snake_case_	=				decoder_layers
       snake_case_	=				decoder_layers
       snake_case_	=				decoder_ffn_dim
       snake_case_	=				decoder_attention_heads
       snake_case_	=				decoder_attention_heads
       snake_case_	=				eos_token_id
       snake_case_	=				bos_token_id
       snake_case_	=				pad_token_id
       snake_case_	=				decoder_start_token_id
       snake_case_	=				use_cache
       snake_case_	=				max_position_embeddings

       snake_case_	=				None
       snake_case_	=				decoder_seq_length
       snake_case_	=				2
       snake_case_	=				1



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ids_tensor([self.batch_size, self.decoder_seq_length]		,				self.vocab_size )

       snake_case_	=				None
       if self.use_attention_mask:
             snake_case_	=				ids_tensor([self.batch_size, self.decoder_seq_length]		,				vocab_size=2 )

       snake_case_	=				None
       if self.use_labels:
             snake_case_	=				ids_tensor([self.batch_size, self.decoder_seq_length]		,				self.vocab_size )

       snake_case_	=				TrOCRConfig(
           vocab_size=self.vocab_size		,				d_model=self.d_model		,				decoder_layers=self.decoder_layers		,				decoder_ffn_dim=self.decoder_ffn_dim		,				decoder_attention_heads=self.decoder_attention_heads		,				eos_token_id=self.eos_token_id		,				bos_token_id=self.bos_token_id		,				use_cache=self.use_cache		,				pad_token_id=self.pad_token_id		,				decoder_start_token_id=self.decoder_start_token_id		,				max_position_embeddings=self.max_position_embeddings		,				)

       return (config, input_ids, attention_mask, lm_labels)



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				True
       snake_case_	=				TrOCRDecoder(config=_UpperCAmelCase ).to(_UpperCAmelCase ).eval()
       snake_case_	=				input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       snake_case_	=				model(_UpperCAmelCase		,				use_cache=_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				use_cache=_UpperCAmelCase )

       self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) )
       self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) + 1 )

       snake_case_	=				outputs['''past_key_values''']

       # create hypothetical next token and extent to next_input_ids
       snake_case_	=				ids_tensor((2, 1)		,				config.vocab_size - 1 ) + 1

       # append to next input_ids and
       snake_case_	=				torch.cat([input_ids, next_tokens]		,				dim=-1 )

       snake_case_	=				model(_UpperCAmelCase )['''last_hidden_state''']
       snake_case_	=				model(_UpperCAmelCase		,				past_key_values=_UpperCAmelCase )['''last_hidden_state''']

       # select random slice
       snake_case_	=				ids_tensor((1,)		,				output_from_past.shape[-1] ).item()
       snake_case_	=				output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       snake_case_	=				output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				config_and_inputs

       snake_case_	=				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 __snake_case												=  (TrOCRForCausalLM,) if is_torch_available() else ()
 __snake_case												=  {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
 __snake_case												=  True
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				TrOCRStandaloneDecoderModelTester(self		,				is_training=_UpperCAmelCase )
       snake_case_	=				ConfigTester(self		,				config_class=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       return





 @unittest.skip('''The model doesn\'t support left padding''' )  # and it's not used enough to be worth fixing :)
 def 						UpperCamelCase__					(     self ):
       pass



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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return (x_in - self.means[key]) / self.stds[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return x_in * self.stds[key] + self.means[key]



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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



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

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

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         snake_case_	=				torch.exp(0.5 * posterior_variance )
                         snake_case_	=				model_std * grad

                   snake_case_	=				0
                   snake_case_	=				x.detach()
                   snake_case_	=				x + scale * grad
                   snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

             snake_case_	=				self.unet(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample.permute(0		,				2		,				1 )

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

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





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

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

       # run the diffusion process
       snake_case_	,   snake_case_	=				self.run_diffusion(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

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

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

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions