

import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
      from huggingface_hub import snapshot_download
      from pyctcdecode import BeamSearchDecoderCTC

      from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
      from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
      from transformers import WavaVecaForCTC






@require_pyctcdecode
class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[Any]    )    ->      List[Any]:
   UpperCAmelCase					      =  "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
   UpperCAmelCase					      =  dict(zip(_a			,						range(len(_a    )    )    )    )

   UpperCAmelCase					      =  {
       "unk_token": "<unk>",
       "bos_token": "<s>",
       "eos_token": "</s>",
   }
   UpperCAmelCase					      =  {
       "feature_size": 1,
       "padding_value": 0.0,
       "sampling_rate": 1_6_0_0_0,
       "return_attention_mask": False,
       "do_normalize": True,
   }

   UpperCAmelCase					      =  tempfile.mkdtemp()
   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["vocab_file"]    )
   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						_a    )
   with open(self.vocab_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write(json.dumps(_a    ) + "\n"    )

   with open(self.feature_extraction_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write(json.dumps(_a    ) + "\n"    )

   # load decoder from hub
   UpperCAmelCase					      =  "hf-internal-testing/ngram-beam-search-decoder"


 def _UpperCamelCase	(						self		: Any			,						**lowerCAmelCase__		: Any    )    ->      int:
   UpperCAmelCase					      =  self.add_kwargs_tokens_map.copy()
   kwargs.update(_a    )
   return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,						**_a    )


 def _UpperCamelCase	(						self		: List[str]			,						**lowerCAmelCase__		: Union[str, Any]    )    ->      List[str]:
   return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,						**_a    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						**lowerCAmelCase__		: str    )    ->      Tuple:
   return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,						**_a    )


 def _UpperCamelCase	(						self		: List[Any]    )    ->      Dict:
   shutil.rmtree(self.tmpdirname    )


 def _UpperCamelCase	(						self		: int    )    ->      Tuple:
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   processor.save_pretrained(self.tmpdirname    )
   UpperCAmelCase					      =  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname    )

   # tokenizer
   self.assertEqual(processor.tokenizer.get_vocab()			,						tokenizer.get_vocab()    )
   self.assertIsInstance(processor.tokenizer			,						_a    )

   # feature extractor
   self.assertEqual(processor.feature_extractor.to_json_string()			,						feature_extractor.to_json_string()    )
   self.assertIsInstance(processor.feature_extractor			,						_a    )

   # decoder
   self.assertEqual(processor.decoder._alphabet.labels			,						decoder._alphabet.labels    )
   self.assertEqual(
       processor.decoder.model_container[decoder._model_key]._unigram_set			,						decoder.model_container[decoder._model_key]._unigram_set			,						)
   self.assertIsInstance(processor.decoder			,						_a    )


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      int:
   UpperCAmelCase					      =  WavaVecaProcessorWithLM(
       tokenizer=self.get_tokenizer()			,						feature_extractor=self.get_feature_extractor()			,						decoder=self.get_decoder()    )
   processor.save_pretrained(self.tmpdirname    )

   # make sure that error is thrown when decoder alphabet doesn't match
   UpperCAmelCase					      =  WavaVecaProcessorWithLM.from_pretrained(
       self.tmpdirname			,						alpha=5.0			,						beta=3.0			,						score_boundary=-7.0			,						unk_score_offset=3    )

   # decoder
   self.assertEqual(processor.language_model.alpha			,						5.0    )
   self.assertEqual(processor.language_model.beta			,						3.0    )
   self.assertEqual(processor.language_model.score_boundary			,						-7.0    )
   self.assertEqual(processor.language_model.unk_score_offset			,						3    )


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  self.get_tokenizer()
   # add token to trigger raise
   tokenizer.add_tokens(["xx"]    )
   with self.assertRaisesRegex(_a			,						"include"    ):
     WavaVecaProcessorWithLM(
         tokenizer=_a			,						feature_extractor=self.get_feature_extractor()			,						decoder=self.get_decoder()    )


 def _UpperCamelCase	(						self		: List[Any]    )    ->      int:
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   UpperCAmelCase					      =  floats_list((3, 1_0_0_0)    )

   UpperCAmelCase					      =  feature_extractor(_a			,						return_tensors="np"    )
   UpperCAmelCase					      =  processor(_a			,						return_tensors="np"    )

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()			,						input_processor[key].sum()			,						delta=1e-2    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      List[Any]:
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   UpperCAmelCase					      =  "This is a test string"

   UpperCAmelCase					      =  processor(text=_a    )

   UpperCAmelCase					      =  tokenizer(_a    )

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]			,						encoded_processor[key]    )


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: Optional[Any]=(2, 1_0, 1_6)			,						lowerCAmelCase__		: Tuple=7_7    )    ->      int:
   np.random.seed(_a    )
   return np.random.rand(*_a    )


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   UpperCAmelCase					      =  self._get_dummy_logits(shape=(1_0, 1_6)			,						seed=1_3    )

   UpperCAmelCase					      =  processor.decode(_a    )

   UpperCAmelCase					      =  decoder.decode_beams(_a    )[0]

   self.assertEqual(decoded_decoder[0]			,						decoded_processor.text    )
   self.assertEqual("</s> <s> </s>"			,						decoded_processor.text    )
   self.assertEqual(decoded_decoder[-2]			,						decoded_processor.logit_score    )
   self.assertEqual(decoded_decoder[-1]			,						decoded_processor.lm_score    )


 @parameterized.expand([[None], ["fork"], ["spawn"]]    )
 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: List[Any]    )    ->      int:
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   UpperCAmelCase					      =  self._get_dummy_logits()

   # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
   #       otherwise, the LM won't be available to the pool's sub-processes.
   # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
   if pool_context is None:
     UpperCAmelCase					      =  processor.batch_decode(_a    )
   else:
     with get_context(_a    ).Pool() as pool:
       UpperCAmelCase					      =  processor.batch_decode(_a			,						_a    )

   UpperCAmelCase					      =  list(_a    )

   with get_context("fork"    ).Pool() as p:
     UpperCAmelCase					      =  decoder.decode_beams_batch(_a			,						_a    )

   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  [], [], []
   for beams in decoded_beams:
     texts_decoder.append(beams[0][0]    )
     logit_scores_decoder.append(beams[0][-2]    )
     lm_scores_decoder.append(beams[0][-1]    )

   self.assertListEqual(_a			,						decoded_processor.text    )
   self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]			,						decoded_processor.text    )
   self.assertListEqual(_a			,						decoded_processor.logit_score    )
   self.assertListEqual(_a			,						decoded_processor.lm_score    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   UpperCAmelCase					      =  self._get_dummy_logits()

   UpperCAmelCase					      =  1_5
   UpperCAmelCase					      =  -20.0
   UpperCAmelCase					      =  -4.0

   UpperCAmelCase					      =  processor.batch_decode(
       _a			,						beam_width=_a			,						beam_prune_logp=_a			,						token_min_logp=_a			,						)
   UpperCAmelCase					      =  decoded_processor_out.text

   UpperCAmelCase					      =  list(_a    )

   with get_context("fork"    ).Pool() as pool:
     UpperCAmelCase					      =  decoder.decode_beams_batch(
         _a			,						_a			,						beam_width=_a			,						beam_prune_logp=_a			,						token_min_logp=_a			,						)

   UpperCAmelCase					      =  [d[0][0] for d in decoded_decoder_out]
   UpperCAmelCase					      =  [d[0][2] for d in decoded_decoder_out]
   UpperCAmelCase					      =  [d[0][3] for d in decoded_decoder_out]

   self.assertListEqual(_a			,						_a    )
   self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]			,						_a    )

   self.assertTrue(np.array_equal(_a			,						decoded_processor_out.logit_score    )    )
   self.assertTrue(np.allclose([-20.054, -18.447]			,						_a			,						atol=1e-3    )    )

   self.assertTrue(np.array_equal(_a			,						decoded_processor_out.lm_score    )    )
   self.assertTrue(np.allclose([-15.554, -13.9_474]			,						_a			,						atol=1e-3    )    )


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Any:
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   UpperCAmelCase					      =  self._get_dummy_logits()

   UpperCAmelCase					      =  2.0
   UpperCAmelCase					      =  5.0
   UpperCAmelCase					      =  -20.0
   UpperCAmelCase					      =  True

   UpperCAmelCase					      =  processor.batch_decode(
       _a			,						alpha=_a			,						beta=_a			,						unk_score_offset=_a			,						lm_score_boundary=_a			,						)
   UpperCAmelCase					      =  decoded_processor_out.text

   UpperCAmelCase					      =  list(_a    )
   decoder.reset_params(
       alpha=_a			,						beta=_a			,						unk_score_offset=_a			,						lm_score_boundary=_a			,						)

   with get_context("fork"    ).Pool() as pool:
     UpperCAmelCase					      =  decoder.decode_beams_batch(
         _a			,						_a			,						)

   UpperCAmelCase					      =  [d[0][0] for d in decoded_decoder_out]

   self.assertListEqual(_a			,						_a    )
   self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]			,						_a    )
   UpperCAmelCase					      =  processor.decoder.model_container[processor.decoder._model_key]
   self.assertEqual(lm_model.alpha			,						2.0    )
   self.assertEqual(lm_model.beta			,						5.0    )
   self.assertEqual(lm_model.unk_score_offset			,						-20.0    )
   self.assertEqual(lm_model.score_boundary			,						_a    )


 def _UpperCamelCase	(						self		: str    )    ->      int:
   UpperCAmelCase					      =  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )

   UpperCAmelCase					      =  processor.decoder.model_container[processor.decoder._model_key]
   UpperCAmelCase					      =  Path(language_model._kenlm_model.path.decode("utf-8"    )    ).parent.parent.absolute()

   UpperCAmelCase					      =  os.listdir(_a    )
   UpperCAmelCase					      =  ["alphabet.json", "language_model"]

   downloaded_decoder_files.sort()
   expected_decoder_files.sort()

   # test that only decoder relevant files from
   # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
   # are downloaded and none of the rest (e.g. README.md, ...)
   self.assertListEqual(_a			,						_a    )


 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   UpperCAmelCase					      =  snapshot_download("hf-internal-testing/processor_with_lm"    )

   UpperCAmelCase					      =  WavaVecaProcessorWithLM.from_pretrained(_a    )

   UpperCAmelCase					      =  processor.decoder.model_container[processor.decoder._model_key]
   UpperCAmelCase					      =  Path(language_model._kenlm_model.path.decode("utf-8"    )    ).parent.parent.absolute()

   UpperCAmelCase					      =  os.listdir(_a    )
   UpperCAmelCase					      =  os.listdir(_a    )

   local_decoder_files.sort()
   expected_decoder_files.sort()

   # test that both decoder form hub and local files in cache are the same
   self.assertListEqual(_a			,						_a    )


 def _UpperCamelCase	(						self		: str    )    ->      Optional[Any]:
   UpperCAmelCase					      =  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
   UpperCAmelCase					      =  AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"    )

   UpperCAmelCase					      =  floats_list((3, 1_0_0_0)    )

   UpperCAmelCase					      =  processor_wavaveca(_a			,						return_tensors="np"    )
   UpperCAmelCase					      =  processor_auto(_a			,						return_tensors="np"    )

   for key in input_wavaveca.keys():
     self.assertAlmostEqual(input_wavaveca[key].sum()			,						input_auto[key].sum()			,						delta=1e-2    )

   UpperCAmelCase					      =  self._get_dummy_logits()

   UpperCAmelCase					      =  processor_wavaveca.batch_decode(_a    )
   UpperCAmelCase					      =  processor_auto.batch_decode(_a    )

   self.assertListEqual(decoded_wavaveca.text			,						decoded_auto.text    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      Optional[int]:
   UpperCAmelCase					      =  self.get_feature_extractor()
   UpperCAmelCase					      =  self.get_tokenizer()
   UpperCAmelCase					      =  self.get_decoder()

   UpperCAmelCase					      =  WavaVecaProcessorWithLM(tokenizer=_a			,						feature_extractor=_a			,						decoder=_a    )

   self.assertListEqual(
       processor.model_input_names			,						feature_extractor.model_input_names			,						msg="`processor` and `feature_extractor` model input names do not match"			,						)


 @staticmethod
 def _UpperCamelCase	(						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: int    )    ->      Dict:
   UpperCAmelCase					      =  [d[key] for d in offsets]
   return retrieved_list


 def _UpperCamelCase	(						self		: Tuple    )    ->      List[str]:
   UpperCAmelCase					      =  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
   UpperCAmelCase					      =  self._get_dummy_logits()[0]

   UpperCAmelCase					      =  processor.decode(_a			,						output_word_offsets=_a    )
   # check Wav2Vec2CTCTokenizerOutput keys for word
   self.assertEqual(len(outputs.keys()    )			,						4    )
   self.assertTrue("text" in outputs    )
   self.assertTrue("word_offsets" in outputs    )
   self.assertTrue(isinstance(_a			,						_a    )    )

   self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]			,						"word"    )    )			,						outputs.text    )
   self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]			,						"word"    )			,						["<s>", "<s>", "</s>"]    )
   self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]			,						"start_offset"    )			,						[0, 2, 4]    )
   self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]			,						"end_offset"    )			,						[1, 3, 5]    )


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
   UpperCAmelCase					      =  self._get_dummy_logits()

   UpperCAmelCase					      =  processor.batch_decode(_a			,						output_word_offsets=_a    )

   # check Wav2Vec2CTCTokenizerOutput keys for word
   self.assertEqual(len(outputs.keys()    )			,						4    )
   self.assertTrue("text" in outputs    )
   self.assertTrue("word_offsets" in outputs    )
   self.assertTrue(isinstance(_a			,						_a    )    )

   self.assertListEqual(
       [" ".join(self.get_from_offsets(_a			,						"word"    )    ) for o in outputs["word_offsets"]]			,						outputs.text    )
   self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]			,						"word"    )			,						["<s>", "<s>", "</s>"]    )
   self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]			,						"start_offset"    )			,						[0, 2, 4]    )
   self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]			,						"end_offset"    )			,						[1, 3, 5]    )


 @slow
 @require_torch
 @require_torchaudio
 def _UpperCamelCase	(						self		: int    )    ->      List[str]:
   import torch

   UpperCAmelCase					      =  load_dataset("common_voice"			,						"en"			,						split="train"			,						streaming=_a    )
   UpperCAmelCase					      =  ds.cast_column("audio"			,						datasets.Audio(sampling_rate=1_6_0_0_0    )    )
   UpperCAmelCase					      =  iter(_a    )
   UpperCAmelCase					      =  next(_a    )

   UpperCAmelCase					      =  AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"    )
   UpperCAmelCase					      =  WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"    )

   # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
   UpperCAmelCase					      =  processor(sample["audio"]["array"]			,						return_tensors="pt"    ).input_values

   with torch.no_grad():
     UpperCAmelCase					      =  model(_a    ).logits.cpu().numpy()

   UpperCAmelCase					      =  processor.decode(logits[0]			,						output_word_offsets=_a    )

   UpperCAmelCase					      =  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
   UpperCAmelCase					      =  [
       {
           "start_time": d["start_offset"] * time_offset,
           "end_time": d["end_offset"] * time_offset,
           "word": d["word"],
       }
       for d in output["word_offsets"]
   ]

   UpperCAmelCase					      =  "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

   # output words
   self.assertEqual(" ".join(self.get_from_offsets(_a			,						"word"    )    )			,						_a    )
   self.assertEqual(" ".join(self.get_from_offsets(_a			,						"word"    )    )			,						output.text    )

   # output times
   UpperCAmelCase					      =  torch.tensor(self.get_from_offsets(_a			,						"start_time"    )    )
   UpperCAmelCase					      =  torch.tensor(self.get_from_offsets(_a			,						"end_time"    )    )

   # fmt: off
   UpperCAmelCase					      =  torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]    )
   UpperCAmelCase					      =  torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]    )
   # fmt: on

   self.assertTrue(torch.allclose(_a			,						_a			,						atol=0.01    )    )
   self.assertTrue(torch.allclose(_a			,						_a			,						atol=0.01    )    )






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 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						KandinskyInpaintPipeline
 UpperCAmelCase							    =						["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 UpperCAmelCase							    =						[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 UpperCAmelCase							    =						[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 UpperCAmelCase							    =						False


 @property
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Tuple:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: int    )    ->      List[Any]:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      List[Any]:
   return self.time_input_dim


 @property
 def _UpperCamelCase	(						self		: Tuple    )    ->      Tuple:
   return self.time_input_dim * 4


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      Optional[int]:
   return 1_0_0


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[int]:
   UpperCAmelCase					      =  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"    )
   return tokenizer


 @property
 def _UpperCamelCase	(						self		: int    )    ->      Dict:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  MCLIPConfig(
       numDims=self.cross_attention_dim			,						transformerDimensions=self.text_embedder_hidden_size			,						hidden_size=self.text_embedder_hidden_size			,						intermediate_size=3_7			,						num_attention_heads=4			,						num_hidden_layers=5			,						vocab_size=1_0_0_5			,						)

   UpperCAmelCase					      =  MultilingualCLIP(lowerCAmelCase__    )
   UpperCAmelCase					      =  text_encoder.eval()

   return text_encoder


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[int]:
   torch.manual_seed(0    )

   UpperCAmelCase					      =  {
       "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					      =  UNetaDConditionModel(**lowerCAmelCase__    )
   return model


 @property
 def _UpperCamelCase	(						self		: str    )    ->      Optional[Any]:
   return {
       "block_out_channels": [3_2, 6_4],
       "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": 1_2,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      List[Any]:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  VQModel(**self.dummy_movq_kwargs    )
   return model


 def _UpperCamelCase	(						self		: Tuple    )    ->      Any:
   UpperCAmelCase					      =  self.dummy_text_encoder
   UpperCAmelCase					      =  self.dummy_tokenizer
   UpperCAmelCase					      =  self.dummy_unet
   UpperCAmelCase					      =  self.dummy_movq

   UpperCAmelCase					      =  DDIMScheduler(
       num_train_timesteps=1_0_0_0			,						beta_schedule="linear"			,						beta_start=0.00_085			,						beta_end=0.012			,						clip_sample=lowerCAmelCase__			,						set_alpha_to_one=lowerCAmelCase__			,						steps_offset=1			,						prediction_type="epsilon"			,						thresholding=lowerCAmelCase__			,						)

   UpperCAmelCase					      =  {
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Tuple=0    )    ->      str:
   UpperCAmelCase					      =  floats_tensor((1, self.cross_attention_dim)			,						rng=random.Random(lowerCAmelCase__    )    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  floats_tensor((1, self.cross_attention_dim)			,						rng=random.Random(seed + 1    )    ).to(lowerCAmelCase__    )
   # create init_image
   UpperCAmelCase					      =  floats_tensor((1, 3, 6_4, 6_4)			,						rng=random.Random(lowerCAmelCase__    )    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  image.cpu().permute(0			,						2			,						3			,						1    )[0]
   UpperCAmelCase					      =  Image.fromarray(np.uinta(lowerCAmelCase__    )    ).convert("RGB"    ).resize((2_5_6, 2_5_6)    )
   # create mask
   UpperCAmelCase					      =  np.ones((6_4, 6_4)			,						dtype=np.floataa    )
   UpperCAmelCase					      =  0

   if str(lowerCAmelCase__    ).startswith("mps"    ):
     UpperCAmelCase					      =  torch.manual_seed(lowerCAmelCase__    )
   else:
     UpperCAmelCase					      =  torch.Generator(device=lowerCAmelCase__    ).manual_seed(lowerCAmelCase__    )
   UpperCAmelCase					      =  {
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 6_4,
       "width": 6_4,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs


 def _UpperCamelCase	(						self		: Dict    )    ->      List[str]:
   UpperCAmelCase					      =  "cpu"

   UpperCAmelCase					      =  self.get_dummy_components()

   UpperCAmelCase					      =  self.pipeline_class(**lowerCAmelCase__    )
   UpperCAmelCase					      =  pipe.to(lowerCAmelCase__    )

   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

   UpperCAmelCase					      =  pipe(**self.get_dummy_inputs(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  output.images

   UpperCAmelCase					      =  pipe(
       **self.get_dummy_inputs(lowerCAmelCase__    )			,						return_dict=lowerCAmelCase__			,						)[0]

   UpperCAmelCase					      =  image[0, -3:, -3:, -1]
   UpperCAmelCase					      =  image_from_tuple[0, -3:, -3:, -1]

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

   assert image.shape == (1, 6_4, 6_4, 3)

   UpperCAmelCase					      =  np.array(
       [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]    )

   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		: str    )    ->      Tuple:
   super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






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


 def _UpperCamelCase	(						self		: str    )    ->      str:
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def _UpperCamelCase	(						self		: Tuple    )    ->      int:
   UpperCAmelCase					      =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"    )

   UpperCAmelCase					      =  load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"    )
   UpperCAmelCase					      =  np.ones((7_6_8, 7_6_8)			,						dtype=np.floataa    )
   UpperCAmelCase					      =  0

   UpperCAmelCase					      =  "a hat"

   UpperCAmelCase					      =  KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,						torch_dtype=torch.floataa    )
   pipe_prior.to(lowerCAmelCase__    )

   UpperCAmelCase					      =  KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,						torch_dtype=torch.floataa    )
   UpperCAmelCase					      =  pipeline.to(lowerCAmelCase__    )
   pipeline.set_progress_bar_config(disable=lowerCAmelCase__    )

   UpperCAmelCase					      =  torch.Generator(device="cpu"    ).manual_seed(0    )
   UpperCAmelCase      ,	UpperCAmelCase					      =  pipe_prior(
       lowerCAmelCase__			,						generator=lowerCAmelCase__			,						num_inference_steps=5			,						negative_prompt=""			,						).to_tuple()

   UpperCAmelCase					      =  pipeline(
       lowerCAmelCase__			,						image=lowerCAmelCase__			,						mask_image=lowerCAmelCase__			,						image_embeds=lowerCAmelCase__			,						negative_image_embeds=lowerCAmelCase__			,						generator=lowerCAmelCase__			,						num_inference_steps=1_0_0			,						height=7_6_8			,						width=7_6_8			,						output_type="np"			,						)

   UpperCAmelCase					      =  output.images[0]

   assert image.shape == (7_6_8, 7_6_8, 3)

   assert_mean_pixel_difference(lowerCAmelCase__			,						lowerCAmelCase__    )



