


import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    GPTNeoXForCausalLM,
			    GPTNeoXForQuestionAnswering,
			    GPTNeoXForSequenceClassification,
			    GPTNeoXForTokenClassification,
			    GPTNeoXModel,
			)

class 							a						:




							"""simple docstring"""


							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Optional[Any]=13       ,      lowerCamelCase			:    Optional[Any]=7       ,      lowerCamelCase			:    Union[str, Any]=True       ,      lowerCamelCase			:    Optional[Any]=True       ,      lowerCamelCase			:    Optional[int]=True       ,      lowerCamelCase			:    List[Any]=True       ,      lowerCamelCase			:    List[str]=99       ,      lowerCamelCase			:    str=64       ,      lowerCamelCase			:    Tuple=5       ,      lowerCamelCase			:    str=4       ,      lowerCamelCase			:    Any=37       ,      lowerCamelCase			:    Union[str, Any]="gelu"       ,      lowerCamelCase			:    str=0.1       ,      lowerCamelCase			:    str=0.1       ,      lowerCamelCase			:    Any=512       ,      lowerCamelCase			:    Tuple=16       ,      lowerCamelCase			:    List[str]=2       ,      lowerCamelCase			:    Dict=0.02       ,      lowerCamelCase			:    List[Any]=3       ,      lowerCamelCase			:    Any=4       ,      lowerCamelCase			:    Optional[int]=None       ,      )      ->       int:
														__snake_case						:     Union[str, Any]									=  parent
														__snake_case						:     List[str]									=  batch_size
														__snake_case						:     str									=  seq_length
														__snake_case						:     Tuple									=  is_training
														__snake_case						:     str									=  use_input_mask
														__snake_case						:     Any									=  use_token_type_ids
														__snake_case						:     List[Any]									=  use_labels
														__snake_case						:     str									=  vocab_size
														__snake_case						:     Any									=  hidden_size
														__snake_case						:     Optional[int]									=  num_hidden_layers
														__snake_case						:     Tuple									=  num_attention_heads
														__snake_case						:     Tuple									=  intermediate_size
														__snake_case						:     str									=  hidden_act
														__snake_case						:     List[Any]									=  hidden_dropout_prob
														__snake_case						:     Any									=  attention_probs_dropout_prob
														__snake_case						:     Dict									=  max_position_embeddings
														__snake_case						:     int									=  type_vocab_size
														__snake_case						:     Tuple									=  type_sequence_label_size
														__snake_case						:     Dict									=  initializer_range
														__snake_case						:     Tuple									=  num_labels
														__snake_case						:     str									=  num_choices
														__snake_case						:     Union[str, Any]									=  scope
														__snake_case						:     int									=  vocab_size - 1

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[int]:
														__snake_case						:     List[Any]									=  ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

														__snake_case						:     Any									=  None
														if self.use_input_mask:
																					__snake_case						:     Optional[Any]									=  random_attention_mask([self.batch_size, self.seq_length]							)

														__snake_case						:     Dict									=  None
														if self.use_labels:
																					__snake_case						:     Optional[int]									=  ids_tensor([self.batch_size, self.seq_length]       ,      self.num_labels							)

														__snake_case						:     Optional[Any]									=  self.get_config()

														return config, input_ids, input_mask, token_labels

							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[str]:
														return GPTNeoXConfig(
														    vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      type_vocab_size=self.type_vocab_size       ,      is_decoder=lowerCamelCase       ,      initializer_range=self.initializer_range       ,      pad_token_id=self.pad_token_id       ,      )

							def        __snake_case   (				self			:    List[str]							)      ->       str:
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     int									=  self.prepare_config_and_inputs()

														__snake_case						:     List[Any]									=  True

														return config, input_ids, input_mask, token_labels

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    List[str]							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  GPTNeoXModel(config=lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     List[Any]									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase							)
														__snake_case						:     Any									=  model(lowerCamelCase							)
														self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)

							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    Optional[int]							)      ->       int:
														__snake_case						:     Tuple									=  True
														__snake_case						:     Dict									=  GPTNeoXModel(lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     Union[str, Any]									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase							)
														self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Optional[Any]							)      ->       Tuple:
														__snake_case						:     str									=  GPTNeoXForCausalLM(config=lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     Any									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      labels=lowerCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)							)

							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    Union[str, Any]							)      ->       Any:
														__snake_case						:     Any									=  self.num_labels
														__snake_case						:     str									=  GPTNeoXForQuestionAnswering(lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     Optional[Any]									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase							)
														self.parent.assertEqual(result.start_logits.shape       ,      (self.batch_size, self.seq_length)							)
														self.parent.assertEqual(result.end_logits.shape       ,      (self.batch_size, self.seq_length)							)

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    Dict							)      ->       Union[str, Any]:
														__snake_case						:     Any									=  self.num_labels
														__snake_case						:     List[Any]									=  GPTNeoXForSequenceClassification(lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     Union[str, Any]									=  ids_tensor([self.batch_size]       ,      self.type_sequence_label_size							)
														__snake_case						:     Union[str, Any]									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      labels=lowerCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Any							)      ->       Optional[int]:
														__snake_case						:     Tuple									=  self.num_labels
														__snake_case						:     Dict									=  GPTNeoXForTokenClassification(lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     List[Any]									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      labels=lowerCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)							)

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    Dict							)      ->       List[Any]:
														__snake_case						:     Tuple									=  True
														__snake_case						:     int									=  GPTNeoXForCausalLM(config=lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()

														# first forward pass
														__snake_case						:     Tuple									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      use_cache=lowerCamelCase							)
														__snake_case						:     Any									=  outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														__snake_case						:     Dict									=  ids_tensor((self.batch_size, 3)       ,      config.vocab_size							)
														__snake_case						:     Dict									=  ids_tensor((self.batch_size, 3)       ,      vocab_size=2							)

														# append to next input_ids and
														__snake_case						:     Union[str, Any]									=  torch.cat([input_ids, next_tokens]       ,      dim=-1							)
														__snake_case						:     Optional[int]									=  torch.cat([input_mask, next_mask]       ,      dim=-1							)

														__snake_case						:     str									=  model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      output_hidden_states=lowerCamelCase							)
														__snake_case						:     Optional[int]									=  output_from_no_past["hidden_states"][0]
														__snake_case						:     Optional[Any]									=  model(
														    lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      past_key_values=lowerCamelCase       ,      output_hidden_states=lowerCamelCase       ,      )["hidden_states"][0]

														# select random slice
														__snake_case						:     List[str]									=  ids_tensor((1,)       ,      output_from_past.shape[-1]							).item()
														__snake_case						:     str									=  output_from_no_past[:, -3:, random_slice_idx].detach()
														__snake_case						:     Tuple									=  output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-3							)							)






							def        __snake_case   (				self			:    List[Any]							)      ->       Tuple:
														__snake_case						:     List[Any]									=  self.prepare_config_and_inputs()
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Dict									=  config_and_inputs
														__snake_case						:     Union[str, Any]									=  {"input_ids": input_ids, "attention_mask": input_mask}
														return config, inputs_dict

@require_torch
class 							a						(_lowerCAmelCase	,							_lowerCAmelCase	,							_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				int	    =    (
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase       :				str	    =    (GPTNeoXForCausalLM,) if is_torch_available() else ()
							__UpperCAmelCase       :				Any	    =    (
							    {
							        "feature-extraction": GPTNeoXModel,
							        "question-answering": GPTNeoXForQuestionAnswering,
							        "text-classification": GPTNeoXForSequenceClassification,
							        "text-generation": GPTNeoXForCausalLM,
							        "token-classification": GPTNeoXForTokenClassification,
							        "zero-shot": GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase       :				List[Any]	    =    False
							__UpperCAmelCase       :				List[str]	    =    False
							__UpperCAmelCase       :				Dict	    =    False
							__UpperCAmelCase       :				str	    =    False

							def        __snake_case   (				self			:    Optional[int]							)      ->       int:
														__snake_case						:     List[str]									=  GPTNeoXModelTester(self							)
														__snake_case						:     str									=  ConfigTester(self       ,      config_class=lowerCamelCase       ,      hidden_size=64       ,      num_attention_heads=8							)

							def        __snake_case   (				self			:    str							)      ->       Tuple:
														self.config_tester.run_common_tests()

							def        __snake_case   (				self			:    Optional[Any]							)      ->       str:
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     List[Any]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Any							)      ->       List[str]:
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Any									=  self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    str							)      ->       Optional[int]:
														# This regression test was failing with PyTorch < 1.3
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     str									=  self.model_tester.prepare_config_and_inputs_for_decoder()

														__snake_case						:     Tuple									=  None

														self.model_tester.create_and_check_model_as_decoder(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[Any]:
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Optional[int]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    str							)      ->       Dict:
														__snake_case						:     str									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase							)

							def        __snake_case   (				self			:    str							)      ->       Dict:
														__snake_case						:     int									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       Tuple:
														__snake_case						:     Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Optional[int]:
														__snake_case						:     str									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*lowerCamelCase							)

							@unittest.skip(reason="Feed forward chunking is not implemented"							)
							def        __snake_case   (				self			:    List[Any]							)      ->       Optional[int]:
														pass






							@parameterized.expand([("linear",), ("dynamic",)]							)
							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    Optional[int]							)      ->       str:
														__snake_case					,     __snake_case						:     int									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     int									=  ids_tensor([1, 10]       ,      config.vocab_size							)
														__snake_case						:     Tuple									=  ids_tensor([1, int(config.max_position_embeddings * 1.5							)]       ,      config.vocab_size							)

														set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
														__snake_case						:     str									=  GPTNeoXModel(lowerCamelCase							)
														original_model.to(lowerCamelCase							)
														original_model.eval()
														__snake_case						:     List[Any]									=  original_model(lowerCamelCase							).last_hidden_state
														__snake_case						:     Tuple									=  original_model(lowerCamelCase							).last_hidden_state

														set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
														__snake_case						:     Union[str, Any]									=  {"type": scaling_type, "factor": 10.0}
														__snake_case						:     List[Any]									=  GPTNeoXModel(lowerCamelCase							)
														scaled_model.to(lowerCamelCase							)
														scaled_model.eval()
														__snake_case						:     List[Any]									=  scaled_model(lowerCamelCase							).last_hidden_state
														__snake_case						:     Any									=  scaled_model(lowerCamelCase							).last_hidden_state

														# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
														# maximum sequence length, so the outputs for the short input should match.
														if scaling_type == "dynamic":
																					self.assertTrue(torch.allclose(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-5							)							)
														else:
																					self.assertFalse(torch.allclose(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-5							)							)

														# The output should be different for long inputs
														self.assertFalse(torch.allclose(lowerCamelCase       ,      lowerCamelCase       ,      atol=1E-5							)							)

@require_torch
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							@slow
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       List[Any]:
														__snake_case						:     List[Any]									=  AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"							)
														for checkpointing in [True, False]:
																					__snake_case						:     Union[str, Any]									=  GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"							)

																					if checkpointing:
																												model.gradient_checkpointing_enable()
																					else:
																												model.gradient_checkpointing_disable()
																					model.to(lowerCamelCase							)

																					__snake_case						:     Dict									=  tokenizer("My favorite food is"       ,      return_tensors="pt"							).to(lowerCamelCase							)
																					# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
																					# See: https://github.com/huggingface/transformers/pull/24193
																					__snake_case						:     Optional[Any]									=  "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

																					__snake_case						:     List[Any]									=  model.generate(**lowerCamelCase       ,      do_sample=lowerCamelCase       ,      max_new_tokens=20							)
																					__snake_case						:     List[str]									=  tokenizer.batch_decode(lowerCamelCase							)[0]

																					self.assertEqual(lowerCamelCase       ,      lowerCamelCase							)



'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Union[str, Any]				=							KandinskyInpaintPipeline
 lowercase_					:    Tuple				=							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 lowercase_					:    Dict				=							[
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 lowercase_					:    Optional[Any]				=							[
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_					:    Optional[Any]				=							False







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.time_input_dim







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return self.time_input_dim * 4







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return 1_00







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
   return tokenizer







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					Dict				       =							MCLIPConfig(
       numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=10_05			,				)

   UpperCAmelCase__      :					Any				       =							MultilingualCLIP(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							text_encoder.eval()

   return text_encoder







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   torch.manual_seed(0 )

   UpperCAmelCase__      :					str				       =							{
       "in_channels": 9,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "text_image",
       "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
       "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
       "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "layers_per_block": 1,
       "encoder_hid_dim": self.text_embedder_hidden_size,
       "encoder_hid_dim_type": "text_image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   UpperCAmelCase__      :					List[str]				       =							UNetaDConditionModel(**snake_case__ )
   return model







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }







 @property
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					str				       =							VQModel(**self.dummy_movq_kwargs )
   return model







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.dummy_text_encoder
   UpperCAmelCase__      :					Any				       =							self.dummy_tokenizer
   UpperCAmelCase__      :					Tuple				       =							self.dummy_unet
   UpperCAmelCase__      :					str				       =							self.dummy_movq

   UpperCAmelCase__      :					str				       =							DDIMScheduler(
       num_train_timesteps=10_00			,				beta_schedule="linear"			,				beta_start=0.00085			,				beta_end=0.012			,				clip_sample=snake_case__			,				set_alpha_to_one=snake_case__			,				steps_offset=1			,				prediction_type="epsilon"			,				thresholding=snake_case__			,				)

   UpperCAmelCase__      :					List[Any]				       =							{
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Any			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1 ) ).to(snake_case__ )
   # create init_image
   UpperCAmelCase__      :					Any				       =							floats_tensor((1, 3, 64, 64)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							image.cpu().permute(0			,				2			,				3			,				1 )[0]
   UpperCAmelCase__      :					Optional[int]				       =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((2_56, 2_56) )
   # create mask
   UpperCAmelCase__      :					Any				       =							np.ones((64, 64)			,				dtype=np.floataa )
   UpperCAmelCase__      :					Any				       =							0

   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Tuple				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					str				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							{
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"cpu"

   UpperCAmelCase__      :					Optional[Any]				       =							self.get_dummy_components()

   UpperCAmelCase__      :					int				       =							self.pipeline_class(**snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							pipe.to(snake_case__ )

   pipe.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							pipe(**self.get_dummy_inputs(snake_case__ ) )
   UpperCAmelCase__      :					int				       =							output.images

   UpperCAmelCase__      :					Tuple				       =							pipe(
       **self.get_dummy_inputs(snake_case__ )			,				return_dict=snake_case__			,				)[0]

   UpperCAmelCase__      :					Any				       =							image[0, -3:, -3:, -1]
   UpperCAmelCase__      :					Optional[int]				       =							image_from_tuple[0, -3:, -3:, -1]

   print(F"""image.shape {image.shape}""" )

   assert image.shape == (1, 64, 64, 3)

   UpperCAmelCase__      :					Any				       =							np.array(
       [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )

   assert (
       np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






@slow
@require_torch_gpu
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

   UpperCAmelCase__      :					List[str]				       =							load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
   UpperCAmelCase__      :					Union[str, Any]				       =							np.ones((7_68, 7_68)			,				dtype=np.floataa )
   UpperCAmelCase__      :					List[Any]				       =							0

   UpperCAmelCase__      :					Tuple				       =							"a hat"

   UpperCAmelCase__      :					Union[str, Any]				       =							KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa )
   pipe_prior.to(snake_case__ )

   UpperCAmelCase__      :					int				       =							KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Optional[int]				       =							pipeline.to(snake_case__ )
   pipeline.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							pipe_prior(
       snake_case__			,				generator=snake_case__			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

   UpperCAmelCase__      :					Optional[int]				       =							pipeline(
       snake_case__			,				image=snake_case__			,				mask_image=snake_case__			,				image_embeds=snake_case__			,				negative_image_embeds=snake_case__			,				generator=snake_case__			,				num_inference_steps=1_00			,				height=7_68			,				width=7_68			,				output_type="np"			,				)

   UpperCAmelCase__      :					int				       =							output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )



