
'''simple docstring'''

import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json






def 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







  a_      :	Tuple      =							filter(lambda A_   : p.requires_grad     ,		model.parameters()   )
  a_      :	List[Any]      =							sum([np.prod(p.size()   ) for p in model_parameters]   )
  return params


__snake_case:  int								=			logging.getLogger(__name__)






def 	_snake_case						(      A_       :    List[Any]     ,		A_       :    Union[str, Any]   ):
  """simple docstring"""







  if metric == "rouge2":
    a_      :	Optional[Any]      =							"""{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    a_      :	List[str]      =							"""{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    a_      :	Optional[Any]      =							"""{val_avg_em:.4f}-{step_count}"""
  elif metric == "loss":
    a_      :	List[Any]      =							"""{val_avg_loss:.4f}-{step_count}"""
  else:
    raise NotImplementedError(
        f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
        """ function."""   )

  a_      :	Union[str, Any]      =							ModelCheckpoint(
      dirpath=__lowerCAmelCase     ,		filename=__lowerCAmelCase     ,		monitor=f'''val_{metric}'''     ,		mode="""max"""     ,		save_top_k=1     ,		every_n_epochs=1     ,		)

  return checkpoint_callback






def 	_snake_case						(      A_       :    Optional[int]     ,		A_       :    Tuple   ):
  """simple docstring"""







  return EarlyStopping(
      monitor=f'''val_{metric}'''     ,		mode="""min""" if """loss""" in metric else """max"""     ,		patience=__lowerCAmelCase     ,		verbose=__lowerCAmelCase     ,		)




class  _UpperCAmelCase ( pl.Callback    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							{f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
       pl_module.logger.log_metrics(_lowerCAmelCase )


     @rank_zero_only
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=True ):

       '''simple docstring'''

       logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
       a_      :	Union[str, Any]      =							trainer.callback_metrics
       trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
       # Log results
       a_      :	int      =							Path(pl_module.hparams.output_dir )
       if type_path == "test":
         a_      :	Optional[int]      =							od / """test_results.txt"""
         a_      :	str      =							od / """test_generations.txt"""
       else:
         # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
         # If people want this it will be easy enough to add back.
         a_      :	List[str]      =							od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
         a_      :	Dict      =							od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
         results_file.parent.mkdir(exist_ok=_lowerCAmelCase )
         generations_file.parent.mkdir(exist_ok=_lowerCAmelCase )
       with open(_lowerCAmelCase							,		"""a+""" ) as writer:
         for key in sorted(_lowerCAmelCase ):
           if key in ["log", "progress_bar", "preds"]:
             continue
           a_      :	List[Any]      =							metrics[key]
           if isinstance(_lowerCAmelCase							,		torch.Tensor ):
             a_      :	str      =							val.item()
           a_      :	str      =							f'''{key}: {val:.6f}\n'''
           writer.write(_lowerCAmelCase )

       if not save_generations:
         return

       if "preds" in metrics:
         a_      :	Any      =							"""\n""".join(metrics["""preds"""] )
         generations_file.open("""w+""" ).write(_lowerCAmelCase )


     @rank_zero_only
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       try:
         a_      :	Any      =							pl_module.model.model.num_parameters()
       except AttributeError:
         a_      :	str      =							pl_module.model.num_parameters()

       a_      :	Tuple      =							count_trainable_parameters(_lowerCAmelCase )
       # mp stands for million parameters
       trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6} )


     @rank_zero_only
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       save_json(pl_module.metrics							,		pl_module.metrics_save_path )
       return self._write_logs(_lowerCAmelCase							,		_lowerCAmelCase							,		"""test""" )






     @rank_zero_only
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       save_json(pl_module.metrics							,		pl_module.metrics_save_path )
       # Uncommenting this will save val generations
       # return self._write_logs(trainer, pl_module, "valid")

'''simple docstring'''

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					PriorTransformer
     a_    	=					"hidden_states"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							4
       a_      :	List[str]      =							8
       a_      :	List[str]      =							7

       a_      :	Tuple      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[Any]      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	List[Any]      =							4
       a_      :	Union[str, Any]      =							8
       a_      :	Optional[int]      =							7

       a_      :	List[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							{
           """num_attention_heads""": 2,
           """attention_head_dim""": 4,
           """num_layers""": 2,
           """embedding_dim""": 8,
           """num_embeddings""": 7,
           """additional_embeddings""": 4,
       }
       a_      :	Optional[int]      =							self.dummy_input
       return init_dict, inputs_dict


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[Any]      =							PriorTransformer.from_pretrained(
           """hf-internal-testing/prior-dummy"""							,		output_loading_info=lowerCAmelCase_ )
       self.assertIsNotNone(lowerCAmelCase_ )
       self.assertEqual(len(loading_info["""missing_keys"""] )							,		0 )

       model.to(lowerCAmelCase_ )
       a_      :	List[Any]      =							model(**self.dummy_input )[0]

       assert hidden_states is not None, "Make sure output is not None"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[int]      =							self.prepare_init_args_and_inputs_for_common()

       a_      :	str      =							self.model_class(**lowerCAmelCase_ )
       a_      :	Optional[Any]      =							inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       a_      :	str      =							[*signature.parameters.keys()]

       a_      :	str      =							["""hidden_states""", """timestep"""]
       self.assertListEqual(arg_names[:2]							,		lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""" )
       a_      :	Optional[Any]      =							model.to(lowerCAmelCase_ )

       if hasattr(lowerCAmelCase_							,		"""set_default_attn_processor""" ):
         model.set_default_attn_processor()

       a_      :	Tuple      =							self.get_dummy_seed_input()

       with torch.no_grad():
         a_      :	List[str]      =							model(**lowerCAmelCase_ )[0]

       a_      :	Optional[Any]      =							output[0, :5].flatten().cpu()
       print(lowerCAmelCase_ )

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       a_      :	List[str]      =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
       self.assertTrue(torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		rtol=1E-2 ) )







@slow
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=1							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=77							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	str      =							batch_size
       a_      :	Dict      =							embedding_dim
       a_      :	Tuple      =							num_embeddings

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[str]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
             [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
             # fmt: on
         ] )
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""							,		subfolder="""prior""" )
       model.to(lowerCAmelCase_ )
       a_      :	Optional[int]      =							self.get_dummy_seed_input(seed=lowerCAmelCase_ )

       with torch.no_grad():
         a_      :	str      =							model(**lowerCAmelCase_ )[0]

       assert list(sample.shape ) == [1, 7_68]

       a_      :	int      =							sample[0, :8].flatten().cpu()
       print(lowerCAmelCase_ )
       a_      :	str      =							torch.tensor(lowerCAmelCase_ )

       assert torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		atol=1E-3 )


