
"""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 			A__	(     __lowerCAmelCase	,					unittest.TestCase):
		A_						:	str           =     PriorTransformer
		A_						:	Optional[Any]           =     "hidden_states"

		@property
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				4
			__lowerCAmelCase      :						Any						      =				8
			__lowerCAmelCase      :						Optional[int]						      =				7

			__lowerCAmelCase      :						str						      =				floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase__			)

			__lowerCAmelCase      :						List[Any]						      =				floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase__			)
			__lowerCAmelCase      :						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	,		_SCREAMING_SNAKE_CASE=0			):
			torch.manual_seed(lowerCamelCase__			)
			__lowerCAmelCase      :						Tuple						      =				4
			__lowerCAmelCase      :						str						      =				8
			__lowerCAmelCase      :						Optional[int]						      =				7

			__lowerCAmelCase      :						List[str]						      =				torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase__			)

			__lowerCAmelCase      :						Dict						      =				torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase__			)
			__lowerCAmelCase      :						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			):
			return (4, 8)

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

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

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				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__			)
			__lowerCAmelCase      :						Union[str, Any]						      =				model(**self.dummy_input			)[0]

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

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.prepare_init_args_and_inputs_for_common()

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

			__lowerCAmelCase      :						int						      =				['''hidden_states''', '''timestep''']
			self.assertListEqual(arg_names[:2]	,		lowerCamelCase__			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'			)
			__lowerCAmelCase      :						int						      =				model.to(lowerCamelCase__			)

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

			__lowerCAmelCase      :						Tuple						      =				self.get_dummy_seed_input()

			with torch.no_grad():
				__lowerCAmelCase      :						str						      =				model(**lowerCamelCase__			)[0]

			__lowerCAmelCase      :						Dict						      =				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.
			__lowerCAmelCase      :						Optional[Any]						      =				torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]			)
			self.assertTrue(torch_all_close(lowerCamelCase__	,		lowerCamelCase__	,		rtol=1E-2			)			)




@slow
class 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=7_68	,		_SCREAMING_SNAKE_CASE=77	,		_SCREAMING_SNAKE_CASE=0			):
			torch.manual_seed(lowerCamelCase__			)
			__lowerCAmelCase      :						Union[str, Any]						      =				batch_size
			__lowerCAmelCase      :						List[Any]						      =				embedding_dim
			__lowerCAmelCase      :						Optional[int]						      =				num_embeddings

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

			__lowerCAmelCase      :						Optional[int]						      =				torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase__			)
			__lowerCAmelCase      :						Union[str, 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			):
			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	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'	,		subfolder='prior'			)
			model.to(lowerCamelCase__			)
			__lowerCAmelCase      :						str						      =				self.get_dummy_seed_input(seed=lowerCamelCase__			)

			with torch.no_grad():
				__lowerCAmelCase      :						Tuple						      =				model(**lowerCamelCase__			)[0]

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

			__lowerCAmelCase      :						Dict						      =				sample[0, :8].flatten().cpu()
			print(lowerCamelCase__			)
			__lowerCAmelCase      :						Tuple						      =				torch.tensor(lowerCamelCase__			)

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



"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate




@dataclass(frozen=_lowerCamelCase)
class 			A__	(     _lowerCamelCase):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		A_						:	str           =     field(default='text-classification'	,					metadata={'include_in_asdict_even_if_is_default': True})
		A_						:	ClassVar[Features]           =     Features({'text': Value('string')})
		A_						:	ClassVar[Features]           =     Features({'labels': ClassLabel})
		A_						:	str           =     "text"
		A_						:	str           =     "labels"

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.label_column not in features:
				raise ValueError(f"Column {self.label_column} is not present in features."			)
			if not isinstance(features[self.label_column]	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
			__lowerCAmelCase      :						Any						      =				copy.deepcopy(self			)
			__lowerCAmelCase      :						Dict						      =				self.label_schema.copy()
			__lowerCAmelCase      :						List[Any]						      =				features[self.label_column]
			__lowerCAmelCase      :						Dict						      =				label_schema
			return task_template







		@property
		def 							__lowerCamelCase							(				self			):
			return {
			    self.text_column: "text",
			    self.label_column: "labels",
			}