


import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm
	import torch_xla.debug.metrics as met





class       A    (   A_ ):


							def __init__(self      ,				*lowerCAmelCase      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				**lowerCAmelCase					):
									super().__init__(*lowerCAmelCase      ,				**lowerCAmelCase					)
									__lowercase=			eval_examples
									__lowercase=			post_process_function


							def 			_A       (self      ,				lowerCAmelCase = None      ,				lowerCAmelCase=None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = "eval"      ,				**lowerCAmelCase      ,				):
									__lowercase=			gen_kwargs.copy()
									__lowercase=			(
									    gen_kwargs['max_length'] if gen_kwargs.get('max_length'					) is not None else self.args.generation_max_length
									)
									__lowercase=			(
									    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
									)
									__lowercase=			gen_kwargs

									__lowercase=			self.eval_dataset if eval_dataset is None else eval_dataset
									__lowercase=			self.get_eval_dataloader(lowerCAmelCase					)
									__lowercase=			self.eval_examples if eval_examples is None else eval_examples

									# Temporarily disable metric computation, we will do it in the loop here.
									__lowercase=			self.compute_metrics
									__lowercase=			None
									__lowercase=			time.time()
									__lowercase=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
									try:
											__lowercase=			eval_loop(
											    lowerCAmelCase      ,				description='Evaluation'      ,				prediction_loss_only=True if compute_metrics is None else None      ,				ignore_keys=lowerCAmelCase      ,				metric_key_prefix=lowerCAmelCase      ,				)
									finally:
											__lowercase=			compute_metrics
									__lowercase=			self.args.eval_batch_size * self.args.world_size
									if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
											start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
									output.metrics.update(
									    speed_metrics(
									        lowerCAmelCase      ,				lowerCAmelCase      ,				num_samples=output.num_samples      ,				num_steps=math.ceil(output.num_samples / total_batch_size					)      ,				)					)

									if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
											# Only the main node write the results by default
											__lowercase=			self.post_process_function(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)
											__lowercase=			self.compute_metrics(lowerCAmelCase					)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()					):
													if not key.startswith(f'{metric_key_prefix}_'					):
															__lowercase=			metrics.pop(lowerCAmelCase					)

											metrics.update(output.metrics					)
									else:
											__lowercase=			output.metrics

									if self.args.should_log:
											# Only the main node log the results by default
											self.log(lowerCAmelCase					)

									if self.args.tpu_metrics_debug or self.args.debug:
											# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
											xm.master_print(met.metrics_report()					)

									__lowercase=			self.callback_handler.on_evaluate(self.args      ,				self.state      ,				self.control      ,				lowerCAmelCase					)
									return metrics







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=None      ,				lowerCAmelCase = "test"      ,				**lowerCAmelCase					):
									__lowercase=			gen_kwargs.copy()

									__lowercase=			self.get_test_dataloader(lowerCAmelCase					)

									# Temporarily disable metric computation, we will do it in the loop here.
									__lowercase=			self.compute_metrics
									__lowercase=			None
									__lowercase=			time.time()
									__lowercase=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
									try:
											__lowercase=			eval_loop(
											    lowerCAmelCase      ,				description='Prediction'      ,				prediction_loss_only=True if compute_metrics is None else None      ,				ignore_keys=lowerCAmelCase      ,				metric_key_prefix=lowerCAmelCase      ,				)
									finally:
											__lowercase=			compute_metrics

									__lowercase=			self.args.eval_batch_size * self.args.world_size
									if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
											start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
									output.metrics.update(
									    speed_metrics(
									        lowerCAmelCase      ,				lowerCAmelCase      ,				num_samples=output.num_samples      ,				num_steps=math.ceil(output.num_samples / total_batch_size					)      ,				)					)
									if self.post_process_function is None or self.compute_metrics is None:
											return output

									__lowercase=			self.post_process_function(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				'predict'					)
									__lowercase=			self.compute_metrics(lowerCAmelCase					)

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys()					):
											if not key.startswith(f'{metric_key_prefix}_'					):
													__lowercase=			metrics.pop(lowerCAmelCase					)
									metrics.update(output.metrics					)
									return PredictionOutput(predictions=predictions.predictions      ,				label_ids=predictions.label_ids      ,				metrics=lowerCAmelCase					)










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       A    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Any       =PriorTransformer
							UpperCamelCase_   :							List[str]       ='''hidden_states'''


							@property
							def 			_A       (self					):
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			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 			_A       (self      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			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 			_A       (self					):
									return (4, 8)


							@property
							def 			_A       (self					):
									return (4, 8)


							def 			_A       (self					):
									__lowercase=			{
									    'num_attention_heads': 2,
									    'attention_head_dim': 4,
									    'num_layers': 2,
									    'embedding_dim': 8,
									    'num_embeddings': 7,
									    'additional_embeddings': 4,
									}
									__lowercase=			self.dummy_input
									return init_dict, inputs_dict


							def 			_A       (self					):
									__lowercase,							__lowercase=			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					)
									__lowercase=			model(**self.dummy_input					)[0]

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


							def 			_A       (self					):
									__lowercase,							__lowercase=			self.prepare_init_args_and_inputs_for_common()

									__lowercase=			self.model_class(**lowerCAmelCase					)
									__lowercase=			inspect.signature(model.forward					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowercase=			[*signature.parameters.keys()]

									__lowercase=			['hidden_states', 'timestep']
									self.assertListEqual(arg_names[:2]      ,				lowerCAmelCase					)







							def 			_A       (self					):
									__lowercase=			PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'					)
									__lowercase=			model.to(lowerCAmelCase					)

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

									__lowercase=			self.get_dummy_seed_input()

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

									__lowercase=			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.
									__lowercase=			torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]					)
									self.assertTrue(torch_all_close(lowerCAmelCase      ,				lowerCAmelCase      ,				rtol=1E-2					)					)





@slow
class       A    (   unittest.TestCase ):


							def 			_A       (self      ,				lowerCAmelCase=1      ,				lowerCAmelCase=7_6_8      ,				lowerCAmelCase=7_7      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			batch_size
									__lowercase=			embedding_dim
									__lowercase=			num_embeddings

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			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 			_A       (self					):
									# clean up the VRAM after each test
									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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'      ,				subfolder='prior'					)
									model.to(lowerCAmelCase					)
									__lowercase=			self.get_dummy_seed_input(seed=lowerCAmelCase					)

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

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

									__lowercase=			sample[0, :8].flatten().cpu()
									print(lowerCAmelCase					)
									__lowercase=			torch.tensor(lowerCAmelCase					)

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






