
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__a         =   5_0_0_0_0
__a         =   5_0_0_0

__a     ,	__a         =   os.path.split(__file__)
__a         =   os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def 							a		(				snake_case__:   datasets.Dataset		,  snake_case__:   Optional[int]   ):


				'''simple docstring'''
				for i in range(snake_case__   ):
								lowercase_	    =   dataset[i]




@get_duration
def 							a		(				snake_case__:   datasets.Dataset		,  snake_case__:   Tuple		,  snake_case__:   Tuple   ):


				'''simple docstring'''
				for i in range(0		,  len(snake_case__   )		,  snake_case__   ):
								lowercase_	    =   dataset[i : i + batch_size]




@get_duration
def 							a		(				snake_case__:   datasets.Dataset		,  snake_case__:   Any		,  snake_case__:   Optional[int]   ):


				'''simple docstring'''
				with dataset.formatted_as(type=snake_case__   ):
								for i in range(snake_case__   ):
												lowercase_	    =   dataset[i]




@get_duration
def 							a		(				snake_case__:   datasets.Dataset		,  snake_case__:   Tuple		,  snake_case__:   Union[str, Any]		,  snake_case__:   int   ):


				'''simple docstring'''
				with dataset.formatted_as(type=snake_case__   ):
								for i in range(0		,  snake_case__		,  snake_case__   ):
												lowercase_	    =   dataset[i : i + batch_size]




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   {'''num examples''': SPEED_TEST_N_EXAMPLES}
				lowercase_	    =   [
				    (read, {'''length''': SMALL_TEST}),
				    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
				    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
				    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_000}),
				    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
				    (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
				    (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
				    (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
				    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
				    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_000}),
				]

				lowercase_	    =   [
				    (read, {'''length''': SMALL_TEST}),
				    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
				    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
				    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_000}),
				    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
				    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
				    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_000}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print('''generating dataset'''   )
								lowercase_	    =   datasets.Features(
								    {'''list''': datasets.Sequence(datasets.Value('''float32'''   )   ), '''numbers''': datasets.Value('''float32'''   )}   )
								lowercase_	    =   generate_example_dataset(
								    os.path.join(snake_case__		,  '''dataset.arrow'''   )		,  snake_case__		,  num_examples=snake_case__		,  seq_shapes={'''list''': (100,)}		,  )
								print('''first set of iterations'''   )
								for func, kwargs in functions:
												print(func.__name__		,  str(snake_case__   )   )
												lowercase_	    =   func(snake_case__		,  **snake_case__   )

								print('''shuffling dataset'''   )
								lowercase_	    =   dataset.shuffle()
								print('''Second set of iterations (after shuffling'''   )
								for func, kwargs in functions_shuffled:
												print('''shuffled '''		,  func.__name__		,  str(snake_case__   )   )
												lowercase_	    =   func(
												    snake_case__		,  **snake_case__   )

				with open(snake_case__		,  '''wb'''   ) as f:
								f.write(json.dumps(snake_case__   ).encode('''utf-8'''   )   )


if __name__ == "__main__":  # useful to run the profiler
						benchmark_iterating()











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       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				PriorTransformer
	_A													=				"hidden_states"

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      List[Any]      =		4
			lowercase__					:      Optional[int]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Optional[Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      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    snake_case__(						self:						int,					lowerCamelCase_:						Dict=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Tuple      =		4
			lowercase__					:      List[Any]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Union[str, Any]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		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    snake_case__(						self:						str			):
			return (4, 8)

	@property
	def    snake_case__(						self:						List[str]			):
			return (4, 8)

	def    snake_case__(						self:						Dict			):
			lowercase__					:      int      =		{
			    'num_attention_heads': 2,
			    'attention_head_dim': 4,
			    'num_layers': 2,
			    'embedding_dim': 8,
			    'num_embeddings': 7,
			    'additional_embeddings': 4,
			}
			lowercase__					:      Optional[Any]      =		self.dummy_input
			return init_dict, inputs_dict

	def    snake_case__(						self:						int			):
			lowercase__   ,				lowercase__					:      Dict      =		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__					:      Tuple      =		model(**self.dummy_input			)[0]

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

	def    snake_case__(						self:						str			):
			lowercase__   ,				lowercase__					:      List[Any]      =		self.prepare_init_args_and_inputs_for_common()

			lowercase__					:      Optional[int]      =		self.model_class(**lowerCamelCase_			)
			lowercase__					:      int      =		inspect.signature(model.forward			)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			lowercase__					:      Dict      =		[*signature.parameters.keys()]

			lowercase__					:      List[str]      =		['hidden_states', 'timestep']
			self.assertListEqual(arg_names[:2],					lowerCamelCase_			)





	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      str      =		PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'			)
			lowercase__					:      Optional[int]      =		model.to(lowerCamelCase_			)

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

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

			with torch.no_grad():
					lowercase__					:      str      =		model(**lowerCamelCase_			)[0]

			lowercase__					:      int      =		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__					:      List[str]      =		torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]			)
			self.assertTrue(torch_all_close(lowerCamelCase_,					lowerCamelCase_,					rtol=1E-2			)			)


@slow
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Union[str, Any]=1,					lowerCamelCase_:						Tuple=768,					lowerCamelCase_:						Dict=77,					lowerCamelCase_:						Union[str, Any]=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Dict      =		batch_size
			lowercase__					:      Dict      =		embedding_dim
			lowercase__					:      Dict      =		num_embeddings

			lowercase__					:      int      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      str      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		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    snake_case__(						self:						str			):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
	        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
	        # fmt: on
	    ]			)
	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						str			):
			lowercase__					:      List[Any]      =		PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior',					subfolder='prior'			)
			model.to(lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		self.get_dummy_seed_input(seed=lowerCamelCase_			)

			with torch.no_grad():
					lowercase__					:      List[str]      =		model(**lowerCamelCase_			)[0]

			assert list(sample.shape			) == [1, 768]

			lowercase__					:      Union[str, Any]      =		sample[0, :8].flatten().cpu()
			print(lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		torch.tensor(lowerCamelCase_			)

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


