"""simple docstring"""


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
 import os

 import jax.numpy as jnp
 from jax import jit

 from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
 from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

 lowerCAmelCase_:					Optional[Any]     =					"0.12"  # assumed parallelism: 8


if is_torch_available():
 import torch







def 					__a   (		A    ,				A    ,				A=None      ):






     '''simple docstring'''



     if rng is None:
          lowercase__				 =							random.Random()

     lowercase__				 =							1
     for dim in shape:
          total_dims *= dim

     lowercase__				 =							[]
     for _ in range(A      ):
          values.append(rng.randint(0    ,				vocab_size - 1      )      )

     lowercase__				 =							np.array(A    ,				dtype=jnp.intaa      ).reshape(A      )

     return output







def 					__a   (		A    ,				A=None      ):






     '''simple docstring'''



     lowercase__				 =							ids_tensor(A    ,				vocab_size=2    ,				rng=A      )
     # make sure that at least one token is attended to for each batch
     lowercase__				 =							1
     return attn_mask






@require_flax
class    a__    :
   snake_case_							=      None
   snake_case_							=      ()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        lowercase__				 =							2
        lowercase__				 =							inputs["input_ids"].shape[-1] // 2
        lowercase__				 =							inputs["input_ids"][:max_batch_size, :sequence_length]

        lowercase__				 =							jnp.ones_like(_UpperCAmelCase  )
        lowercase__				 =							attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        lowercase__				 =							input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
             # hack to allow generate for models such as GPT2 as is done in `generate()`
             lowercase__				 =							config.eos_token_id
        return config, input_ids, attention_mask, max_length


   @is_pt_flax_cross_test
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							False
        lowercase__				 =							max_length
        lowercase__				 =							0

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowercase__				 =							getattr(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							pt_model_class(_UpperCAmelCase  ).eval()
             lowercase__				 =							load_flax_weights_in_pytorch_model(_UpperCAmelCase,				flax_model.params  )

             lowercase__				 =							flax_model.generate(_UpperCAmelCase  ).sequences
             lowercase__				 =							pt_model.generate(torch.tensor(_UpperCAmelCase,				dtype=torch.long  )  )

             if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                  lowercase__				 =							flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

             self.assertListEqual(pt_generation_outputs.numpy().tolist(),				flax_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							False
        lowercase__				 =							max_length

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							True
        lowercase__				 =							max_length

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							False
        lowercase__				 =							max_length
        lowercase__				 =							2

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							False
        lowercase__				 =							max_length
        lowercase__				 =							2
        lowercase__				 =							2

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[0],				input_ids.shape[0] * config.num_return_sequences  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							True
        lowercase__				 =							max_length
        lowercase__				 =							0.8
        lowercase__				 =							10
        lowercase__				 =							0.3
        lowercase__				 =							1
        lowercase__				 =							8
        lowercase__				 =							9

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							max_length
        lowercase__				 =							1
        lowercase__				 =							8
        lowercase__				 =							9

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()
        lowercase__				 =							max_length
        lowercase__				 =							2
        lowercase__				 =							1
        lowercase__				 =							8
        lowercase__				 =							9

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()

        # pad attention mask on the left
        lowercase__				 =							attention_mask.at[(0, 0)].set(0  )

        lowercase__				 =							False
        lowercase__				 =							max_length

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase,				attention_mask=_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase,				attention_mask=_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()

        # pad attention mask on the left
        lowercase__				 =							attention_mask.at[(0, 0)].set(0  )

        lowercase__				 =							True
        lowercase__				 =							max_length

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase,				attention_mask=_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase,				attention_mask=_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							self._get_input_ids_and_config()

        # pad attention mask on the left
        lowercase__				 =							attention_mask.at[(0, 0)].set(0  )

        lowercase__				 =							2
        lowercase__				 =							max_length

        for model_class in self.all_generative_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model.generate(_UpperCAmelCase,				attention_mask=_UpperCAmelCase  ).sequences
             self.assertEqual(generation_outputs.shape[-1],				_UpperCAmelCase  )

             lowercase__				 =							jit(model.generate  )
             lowercase__				 =							jit_generate(_UpperCAmelCase,				attention_mask=_UpperCAmelCase  ).sequences

             self.assertListEqual(generation_outputs.tolist(),				jit_generation_outputs.tolist()  )






@require_flax
class    a__    (   unittest.TestCase	):


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"  )
        lowercase__				 =							FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"  )

        lowercase__				 =							"Hello world"
        lowercase__				 =							tokenizer(_UpperCAmelCase,				return_tensors="np"  ).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(_UpperCAmelCase,				"do_samples"  ):
             model.generate(_UpperCAmelCase,				do_samples=_UpperCAmelCase  )

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(_UpperCAmelCase,				"foo"  ):
             lowercase__				 =							{"foo": "bar"}
             model.generate(_UpperCAmelCase,				**_UpperCAmelCase  )





"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    a__    (   _a	):
   snake_case_							=      (IPNDMScheduler,)
   snake_case_							=      (("num_inference_steps", 50),)


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							{"num_train_timesteps": 1000}
        config.update(**_UpperCAmelCase  )
        return config


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config(**_UpperCAmelCase  )
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )
             scheduler.set_timesteps(_UpperCAmelCase  )
             # copy over dummy past residuals
             lowercase__				 =							dummy_past_residuals[:]

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_UpperCAmelCase  )
                  lowercase__				 =							scheduler_class.from_pretrained(_UpperCAmelCase  )
                  new_scheduler.set_timesteps(_UpperCAmelCase  )
                  # copy over dummy past residuals
                  lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config()
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )
             scheduler.set_timesteps(_UpperCAmelCase  )

             # copy over dummy past residuals (must be after setting timesteps)
             lowercase__				 =							dummy_past_residuals[:]

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_UpperCAmelCase  )
                  lowercase__				 =							scheduler_class.from_pretrained(_UpperCAmelCase  )
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(_UpperCAmelCase  )

                  # copy over dummy past residual (must be after setting timesteps)
                  lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							self.scheduler_classes[0]
        lowercase__				 =							self.get_scheduler_config(**_UpperCAmelCase  )
        lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

        lowercase__				 =							10
        lowercase__				 =							self.dummy_model()
        lowercase__				 =							self.dummy_sample_deter
        scheduler.set_timesteps(_UpperCAmelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        return sample


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )

        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config()
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

             lowercase__				 =							self.dummy_sample
             lowercase__				 =							0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  scheduler.set_timesteps(_UpperCAmelCase  )
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  lowercase__				 =							num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.timesteps[5]
             lowercase__				 =							scheduler.timesteps[6]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             self.assertEqual(output_a.shape,				sample.shape  )
             self.assertEqual(output_a.shape,				output_a.shape  )

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             self.assertEqual(output_a.shape,				sample.shape  )
             self.assertEqual(output_a.shape,				output_a.shape  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for timesteps in [100, 1000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase,				time_step=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for t, num_inference_steps in zip([1, 5, 10],				[10, 50, 100]  ):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase,				time_step=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop()
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_mean.item() - 254_0529  ) < 10




