


"""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

 UpperCAmelCase      		=			'''0.12'''  # assumed parallelism: 8


if is_torch_available():
 import torch






def       lowerCamelCase					(a_						:Dict				,				a_						:Union[str, Any]				,				a_						:Union[str, Any]=None)     ->							List[str]:
       if rng is None:
              lowercase		:Optional[int]       							=				random.Random()

       lowercase		:Optional[int]       							=				1
       for dim in shape:
              total_dims *= dim

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

       lowercase		:Any       							=				np.array(a_				,				dtype=jnp.intaa).reshape(a_)

       return output






def       lowerCamelCase					(a_						:Optional[int]				,				a_						:Tuple=None)     ->							str:
       lowercase		:List[Any]       							=				ids_tensor(a_				,				vocab_size=2				,				rng=a_)
       # make sure that at least one token is attended to for each batch
       lowercase		:int       							=				1
       return attn_mask
@require_flax
class 					__magic_name__		:
       __A			:      Dict        =   None
       __A			:      Optional[Any]        =   ()






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Union[str, Any]       							=				self.model_tester.prepare_config_and_inputs_for_common()

              # cut to half length & take max batch_size 3
              lowercase		:List[Any]       							=				2
              lowercase		:Optional[int]       							=				inputs['''input_ids'''].shape[-1] // 2
              lowercase		:Union[str, Any]       							=				inputs['''input_ids'''][:max_batch_size, :sequence_length]

              lowercase		:str       							=				jnp.ones_like(snake_case__				)
              lowercase		:Any       							=				attention_mask[:max_batch_size, :sequence_length]

              # generate max 5 tokens
              lowercase		:int       							=				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		:Optional[int]       							=				config.eos_token_id
              return config, input_ids, attention_mask, max_length






       @is_pt_flax_cross_test
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:Optional[Any]       							=				self._get_input_ids_and_config()
              lowercase		:Dict       							=				False
              lowercase		:Union[str, Any]       							=				max_length
              lowercase		:Optional[int]       							=				0

              for model_class in self.all_generative_model_classes:
                     lowercase		:List[str]       							=				model_class(snake_case__				)

                     lowercase		:Optional[Any]       							=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     lowercase		:Tuple       							=				getattr(snake_case__  ,     snake_case__				)
                     lowercase		:Optional[Any]       							=				pt_model_class(snake_case__				).eval()
                     lowercase		:Tuple       							=				load_flax_weights_in_pytorch_model(snake_case__  ,     flax_model.params				)

                     lowercase		:Dict       							=				flax_model.generate(snake_case__				).sequences
                     lowercase		:Tuple       							=				pt_model.generate(torch.tensor(snake_case__  ,     dtype=torch.long				)				)

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

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






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:Union[str, Any]       							=				self._get_input_ids_and_config()
              lowercase		:List[str]       							=				False
              lowercase		:List[str]       							=				max_length

              for model_class in self.all_generative_model_classes:
                     lowercase		:int       							=				model_class(snake_case__				)

                     lowercase		:List[Any]       							=				model.generate(snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:Any       							=				jit(model.generate				)
                     lowercase		:int       							=				jit_generate(snake_case__				).sequences

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






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:Optional[int]       							=				self._get_input_ids_and_config()
              lowercase		:Union[str, Any]       							=				True
              lowercase		:Optional[int]       							=				max_length

              for model_class in self.all_generative_model_classes:
                     lowercase		:List[Any]       							=				model_class(snake_case__				)

                     lowercase		:List[str]       							=				model.generate(snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:List[str]       							=				jit(model.generate				)
                     lowercase		:str       							=				jit_generate(snake_case__				).sequences

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






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:List[Any]       							=				self._get_input_ids_and_config()
              lowercase		:int       							=				False
              lowercase		:Optional[Any]       							=				max_length
              lowercase		:List[Any]       							=				2

              for model_class in self.all_generative_model_classes:
                     lowercase		:str       							=				model_class(snake_case__				)

                     lowercase		:str       							=				model.generate(snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:List[str]       							=				jit(model.generate				)
                     lowercase		:str       							=				jit_generate(snake_case__				).sequences

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






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:str       							=				self._get_input_ids_and_config()
              lowercase		:Optional[int]       							=				False
              lowercase		:List[str]       							=				max_length
              lowercase		:str       							=				2
              lowercase		:Any       							=				2

              for model_class in self.all_generative_model_classes:
                     lowercase		:Any       							=				model_class(snake_case__				)

                     lowercase		:Any       							=				model.generate(snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[0]  ,     input_ids.shape[0] * config.num_return_sequences				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:List[Any]       							=				self._get_input_ids_and_config()
              lowercase		:Any       							=				True
              lowercase		:int       							=				max_length
              lowercase		:List[Any]       							=				0.8
              lowercase		:List[str]       							=				1_0
              lowercase		:int       							=				0.3
              lowercase		:int       							=				1
              lowercase		:List[Any]       							=				8
              lowercase		:Optional[Any]       							=				9

              for model_class in self.all_generative_model_classes:
                     lowercase		:Optional[int]       							=				model_class(snake_case__				)

                     lowercase		:List[Any]       							=				model.generate(snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:str       							=				jit(model.generate				)
                     lowercase		:Tuple       							=				jit_generate(snake_case__				).sequences

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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:Optional[Any]       							=				self._get_input_ids_and_config()
              lowercase		:Optional[int]       							=				max_length
              lowercase		:Optional[Any]       							=				1
              lowercase		:Dict       							=				8
              lowercase		:Optional[Any]       							=				9

              for model_class in self.all_generative_model_classes:
                     lowercase		:List[Any]       							=				model_class(snake_case__				)

                     lowercase		:List[str]       							=				model.generate(snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:Union[str, Any]       							=				jit(model.generate				)
                     lowercase		:Tuple       							=				jit_generate(snake_case__				).sequences

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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:Dict       							=				self._get_input_ids_and_config()
              lowercase		:Union[str, Any]       							=				max_length
              lowercase		:List[Any]       							=				2
              lowercase		:Optional[Any]       							=				1
              lowercase		:Dict       							=				8
              lowercase		:Optional[Any]       							=				9

              for model_class in self.all_generative_model_classes:
                     lowercase		:Optional[int]       							=				model_class(snake_case__				)

                     lowercase		:Union[str, Any]       							=				model.generate(snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:Union[str, Any]       							=				jit(model.generate				)
                     lowercase		:List[str]       							=				jit_generate(snake_case__				).sequences

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






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:Optional[int]       							=				self._get_input_ids_and_config()

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

              lowercase		:List[Any]       							=				False
              lowercase		:str       							=				max_length

              for model_class in self.all_generative_model_classes:
                     lowercase		:Dict       							=				model_class(snake_case__				)

                     lowercase		:List[Any]       							=				model.generate(snake_case__  ,     attention_mask=snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:int       							=				jit(model.generate				)
                     lowercase		:Tuple       							=				jit_generate(snake_case__  ,     attention_mask=snake_case__				).sequences

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






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:Dict       							=				self._get_input_ids_and_config()

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

              lowercase		:Union[str, Any]       							=				True
              lowercase		:Tuple       							=				max_length

              for model_class in self.all_generative_model_classes:
                     lowercase		:Any       							=				model_class(snake_case__				)

                     lowercase		:Optional[int]       							=				model.generate(snake_case__  ,     attention_mask=snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:Union[str, Any]       							=				jit(model.generate				)
                     lowercase		:Optional[int]       							=				jit_generate(snake_case__  ,     attention_mask=snake_case__				).sequences

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







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase							,  lowercase							,  lowercase							,  lowercase		:List[str]       							=				self._get_input_ids_and_config()

              # pad attention mask on the left
              lowercase		:Union[str, Any]       							=				attention_mask.at[(0, 0)].set(0				)

              lowercase		:List[Any]       							=				2
              lowercase		:List[str]       							=				max_length

              for model_class in self.all_generative_model_classes:
                     lowercase		:Tuple       							=				model_class(snake_case__				)

                     lowercase		:Union[str, Any]       							=				model.generate(snake_case__  ,     attention_mask=snake_case__				).sequences
                     self.assertEqual(generation_outputs.shape[-1]  ,     snake_case__				)

                     lowercase		:Dict       							=				jit(model.generate				)
                     lowercase		:str       							=				jit_generate(snake_case__  ,     attention_mask=snake_case__				).sequences

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







@require_flax
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
              lowercase		:List[Any]       							=				FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

              lowercase		:Optional[int]       							=				'''Hello world'''
              lowercase		:Optional[Any]       							=				tokenizer(snake_case__  ,     return_tensors='''np'''				).input_ids

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

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





"""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 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   PriorTransformer
       __A			:      Any        =   "hidden_states"






       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				4
              lowercase		:List[Any]       							=				8
              lowercase		:Any       							=				7

              lowercase		:Optional[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

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






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Union[str, Any]       							=				4
              lowercase		:Optional[int]       							=				8
              lowercase		:str       							=				7

              lowercase		:Dict       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:Union[str, Any]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Tuple       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

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






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (4, 8)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return (4, 8)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				{
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              lowercase		:List[str]       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[Any]       							=				model(**self.dummy_input				)[0]

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






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				self.prepare_init_args_and_inputs_for_common()

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

              lowercase		:Optional[Any]       							=				['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]  ,     snake_case__				)







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              lowercase		:List[Any]       							=				model.to(snake_case__				)

              if hasattr(snake_case__  ,     '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              lowercase		:List[str]       							=				self.get_dummy_seed_input()

              with torch.no_grad():
                     lowercase		:List[Any]       							=				model(**snake_case__				)[0]

              lowercase		:Dict       							=				output[0, :5].flatten().cpu()
              print(snake_case__				)

              # 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.
              lowercase		:str       							=				torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]				)
              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)







@slow
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int=1  ,     snake_case__  :    int=7_6_8  ,     snake_case__  :    Optional[Any]=7_7  ,     snake_case__  :    Union[str, Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Any       							=				batch_size
              lowercase		:Dict       							=				embedding_dim
              lowercase		:int       							=				num_embeddings

              lowercase		:int       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[str]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Optional[Any]       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

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






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






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







       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
               [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
               # fmt: on
           ]				)
       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''  ,     subfolder='''prior'''				)
              model.to(snake_case__				)
              lowercase		:Union[str, Any]       							=				self.get_dummy_seed_input(seed=snake_case__				)

              with torch.no_grad():
                     lowercase		:int       							=				model(**snake_case__				)[0]

              assert list(sample.shape				) == [1, 7_6_8]

              lowercase		:List[Any]       							=				sample[0, :8].flatten().cpu()
              print(snake_case__				)
              lowercase		:Union[str, Any]       							=				torch.tensor(snake_case__				)

              assert torch_all_close(snake_case__  ,     snake_case__  ,     atol=1e-3				)

