




"""simple docstring"""

import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  pa.array(TypedSequence([1, 2, 3]						)						)
       self.assertEqual(arr.type       ,				pa.intaa()						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       with self.assertRaises(_UpperCAmelCase						):
             _A     =  pa.array(TypedSequence([1, 2, 3]						)       ,				type=pa.intaa()						)

 def      lowerCAmelCase_				(					self			:			str						):
       with self.assertRaises(_UpperCAmelCase						):
             _A     =  pa.array(TypedSequence([1, 2, 3]       ,				try_type=Value('bool'						)       ,				type=Value('int64'						)						)						)

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  pa.array(TypedSequence([1, 2, 3]       ,				type=Value('int32'						)						)						)
       self.assertEqual(arr.type       ,				pa.intaa()						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
             _A     =  pa.array(TypedSequence(['foo', 'bar']       ,				type=Value('int64'						)						)						)

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  pa.array(TypedSequence([1, 2, 3]       ,				try_type=Value('int32'						)						)						)
       self.assertEqual(arr.type       ,				pa.intaa()						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  pa.array(TypedSequence(['foo', 'bar']       ,				try_type=Value('int64'						)						)						)
       self.assertEqual(arr.type       ,				pa.string()						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  pa.array(TypedSequence([[[1, 2, 3]]]       ,				type=ArrayaD((1, 3)       ,				'int64'						)						)						)
       self.assertEqual(arr.type       ,				ArrayaDExtensionType((1, 3)       ,				'int64'						)						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
             _A     =  pa.array(TypedSequence(['foo', 'bar']       ,				type=ArrayaD((1, 3)       ,				'int64'						)						)						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  pa.array(TypedSequence([[[1, 2, 3]]]       ,				try_type=ArrayaD((1, 3)       ,				'int64'						)						)						)
       self.assertEqual(arr.type       ,				ArrayaDExtensionType((1, 3)       ,				'int64'						)						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  pa.array(TypedSequence(['foo', 'bar']       ,				try_type=ArrayaD((1, 3)       ,				'int64'						)						)						)
       self.assertEqual(arr.type       ,				pa.string()						)



 @require_pil
 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       import PIL.Image

       _A     =  PIL.Image.fromarray(np.arange(10       ,				dtype=np.uinta						).reshape(2       ,				5						)						)
       with patch(
           'datasets.arrow_writer.cast_to_python_objects'       ,				side_effect=_UpperCAmelCase						) as mock_cast_to_python_objects:
             _A     =  pa.array(TypedSequence([{'path': None, 'bytes': b'image_bytes'}, pil_image]       ,				type=Image()						)						)
             _A  ,       _A     =  mock_cast_to_python_objects.call_args_list[-1]
             self.assertIn('optimize_list_casting'       ,				_UpperCAmelCase						)
             self.assertFalse(kwargs['optimize_list_casting']						)






def 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   int				)       ->   List[str]:






      '''simple docstring'''


      _A     =  pa.BufferReader(_snake_case				) if isinstance(_snake_case							,    pa.Buffer				) else pa.memory_map(_snake_case				)
      _A     =  pa.ipc.open_stream(_snake_case				)
      _A     =  f.read_all()
      assert len(pa_table.to_batches()				) == expected_num_chunks
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
      del pa_table






@pytest.mark.parametrize('writer_batch_size'							,    [None, 1, 10]				)
@pytest.mark.parametrize(
    'fields'							,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]				)
def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Dict				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      _A     =  pa.schema(_snake_case				) if fields else None
      with ArrowWriter(stream=_snake_case							,    schema=_snake_case							,    writer_batch_size=_snake_case				) as writer:
            writer.write({'col_1': 'foo', 'col_2': 1}				)
            writer.write({'col_1': 'bar', 'col_2': 2}				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            _A     =  {'col_1': pa.string(), 'col_2': pa.intaa()}
      assert writer._schema == pa.schema(_snake_case							,    metadata=writer._schema.metadata				)
      _check_output(output.getvalue()							,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)






def 		_snake_case	(   )       ->   Dict:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      _A     =  Features({'labels': ClassLabel(names=['neg', 'pos']				)}				)
      with ArrowWriter(stream=_snake_case							,    features=_snake_case				) as writer:
            writer.write({'labels': 0}				)
            writer.write({'labels': 1}				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert writer._schema == features.arrow_schema
      assert writer._schema.metadata == features.arrow_schema.metadata
      _A     =  pa.BufferReader(output.getvalue()				)
      _A     =  pa.ipc.open_stream(_snake_case				)
      _A     =  f.read_all()
      _A     =  pa_table.schema
      assert pa_table.num_rows == 2
      assert schema == features.arrow_schema
      assert schema.metadata == features.arrow_schema.metadata
      assert features == Features.from_arrow_schema(_snake_case				)






@pytest.mark.parametrize('writer_batch_size'							,    [None, 1, 10]				)
def 		_snake_case	(   _snake_case				:   Union[str, Any]				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      with ArrowWriter(
          stream=_snake_case							,    writer_batch_size=_snake_case							,    hash_salt='split_name'							,    check_duplicates=_snake_case							,    ) as writer:
            with pytest.raises(_snake_case				):
                  writer.write({'col_1': 'foo', 'col_2': 1}							,    key=[1, 2]				)
                  _A  ,       _A     =  writer.finalize()






@pytest.mark.parametrize('writer_batch_size'							,    [None, 2, 10]				)
def 		_snake_case	(   _snake_case				:   str				)       ->   Dict:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      with ArrowWriter(
          stream=_snake_case							,    writer_batch_size=_snake_case							,    hash_salt='split_name'							,    check_duplicates=_snake_case							,    ) as writer:
            with pytest.raises(_snake_case				):
                  writer.write({'col_1': 'foo', 'col_2': 1}							,    key=10				)
                  writer.write({'col_1': 'bar', 'col_2': 2}							,    key=10				)
                  _A  ,       _A     =  writer.finalize()






@pytest.mark.parametrize('writer_batch_size'							,    [None, 2, 10]				)
def 		_snake_case	(   _snake_case				:   Dict				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      with ArrowWriter(
          stream=_snake_case							,    writer_batch_size=_snake_case							,    hash_salt='split_name'							,    check_duplicates=_snake_case							,    ) as writer:
            writer.write({'col_1': 'foo', 'col_2': 1}							,    key=1				)
            writer.write({'col_1': 'bar', 'col_2': 2}							,    key=2				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      _check_output(output.getvalue()							,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)






@pytest.mark.parametrize('writer_batch_size'							,    [None, 1, 10]				)
@pytest.mark.parametrize(
    'fields'							,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]				)
def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   Tuple				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      _A     =  pa.schema(_snake_case				) if fields else None
      with ArrowWriter(stream=_snake_case							,    schema=_snake_case							,    writer_batch_size=_snake_case				) as writer:
            writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}				)
            writer.write_batch({'col_1': [], 'col_2': []}				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            _A     =  {'col_1': pa.string(), 'col_2': pa.intaa()}
      assert writer._schema == pa.schema(_snake_case							,    metadata=writer._schema.metadata				)
      _check_output(output.getvalue()							,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)






@pytest.mark.parametrize('writer_batch_size'							,    [None, 1, 10]				)
@pytest.mark.parametrize(
    'fields'							,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]				)
def 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   Union[str, Any]				)       ->   Dict:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      _A     =  pa.schema(_snake_case				) if fields else None
      with ArrowWriter(stream=_snake_case							,    schema=_snake_case							,    writer_batch_size=_snake_case				) as writer:
            writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}				)				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            _A     =  {'col_1': pa.string(), 'col_2': pa.intaa()}
      assert writer._schema == pa.schema(_snake_case							,    metadata=writer._schema.metadata				)
      _check_output(output.getvalue()							,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)






@pytest.mark.parametrize('writer_batch_size'							,    [None, 1, 10]				)
@pytest.mark.parametrize(
    'fields'							,    [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}]				)
def 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   Union[str, Any]				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      _A     =  pa.schema(_snake_case				) if fields else None
      with ArrowWriter(stream=_snake_case							,    schema=_snake_case							,    writer_batch_size=_snake_case				) as writer:
            writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]}				)				)
            writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]}				)				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            _A     =  {'col_1': pa.string(), 'col_2': pa.intaa()}
      assert writer._schema == pa.schema(_snake_case							,    metadata=writer._schema.metadata				)
      _check_output(output.getvalue()							,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      with tempfile.TemporaryDirectory() as tmp_dir:
            _A     =  {'col_1': pa.string(), 'col_2': pa.intaa()}
            _A     =  os.path.join(_snake_case							,    'test.arrow'				)
            with ArrowWriter(path=_snake_case							,    schema=pa.schema(_snake_case				)				) as writer:
                  writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}				)
                  _A  ,       _A     =  writer.finalize()
            assert num_examples == 2
            assert num_bytes > 0
            assert writer._schema == pa.schema(_snake_case							,    metadata=writer._schema.metadata				)
            _check_output(_snake_case							,    1				)






def 		_snake_case	(   _snake_case				:   Optional[int]				)       ->   Tuple:






      '''simple docstring'''


      if pa.types.is_list(_snake_case				):
            return get_base_dtype(arr_type.value_type				)
      else:
            return arr_type






def 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   List[Any]				)       ->   Any:






      '''simple docstring'''


      if isinstance(lst[0]							,    _snake_case				):
            change_first_primitive_element_in_list(lst[0]							,    _snake_case				)
      else:
            _A     =  value






@pytest.mark.parametrize('optimized_int_type, expected_dtype'							,    [(None, pa.intaa()), (Value('int32'				), pa.intaa())]				)
@pytest.mark.parametrize('sequence'							,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 		_snake_case	(   _snake_case				:   List[Any]							,    _snake_case				:   List[str]							,    _snake_case				:   Optional[int]				)       ->   Any:






      '''simple docstring'''


      _A     =  pa.array(TypedSequence(_snake_case							,    optimized_int_type=_snake_case				)				)
      assert get_base_dtype(arr.type				) == expected_dtype






@pytest.mark.parametrize(
    'col, expected_dtype'							,    [
        ('attention_mask', pa.inta()),
        ('special_tokens_mask', pa.inta()),
        ('token_type_ids', pa.inta()),
        ('input_ids', pa.intaa()),
        ('other', pa.intaa()),
    ]							,    )
@pytest.mark.parametrize('sequence'							,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   Any							,    _snake_case				:   Optional[Any]				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  pa.array(OptimizedTypedSequence(_snake_case							,    col=_snake_case				)				)
      assert get_base_dtype(arr.type				) == expected_dtype

      # not in range
      if col != "other":
            # avoids errors due to in-place modifications
            _A     =  copy.deepcopy(_snake_case				)
            _A     =  np.iinfo(expected_dtype.to_pandas_dtype()				).max + 1
            change_first_primitive_element_in_list(_snake_case							,    _snake_case				)
            _A     =  pa.array(OptimizedTypedSequence(_snake_case							,    col=_snake_case				)				)
            assert get_base_dtype(arr.type				) == pa.intaa()






@pytest.mark.parametrize('raise_exception'							,    [False, True]				)
def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   Optional[int]				)       ->   str:






      '''simple docstring'''


      _A     =  str(tmp_path / 'dataset-train.arrow'				)
      try:
            with ArrowWriter(path=_snake_case				) as writer:
                  if raise_exception:
                        raise pa.lib.ArrowInvalid()
                  else:
                        writer.stream.close()
      except pa.lib.ArrowInvalid:
            pass
      finally:
            assert writer.stream.closed






def 		_snake_case	(   _snake_case				:   int				)       ->   Any:






      '''simple docstring'''


      _A     =  'mock://dataset-train.arrow'
      with ArrowWriter(path=_snake_case							,    storage_options=mockfs.storage_options				) as writer:
            assert isinstance(writer._fs							,    type(_snake_case				)				)
            assert writer._fs.storage_options == mockfs.storage_options
            writer.write({'col_1': 'foo', 'col_2': 1}				)
            writer.write({'col_1': 'bar', 'col_2': 2}				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert mockfs.exists(_snake_case				)






def 		_snake_case	(   )       ->   Dict:






      '''simple docstring'''


      _A     =  pa.BufferOutputStream()
      with ParquetWriter(stream=_snake_case				) as writer:
            writer.write({'col_1': 'foo', 'col_2': 1}				)
            writer.write({'col_1': 'bar', 'col_2': 2}				)
            _A  ,       _A     =  writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      _A     =  pa.BufferReader(output.getvalue()				)
      _A     =  pq.read_table(_snake_case				)
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}






@require_pil
@pytest.mark.parametrize('embed_local_files'							,    [False, True]				)
def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   List[str]				)       ->   Dict:






      '''simple docstring'''


      import PIL.Image

      _A     =  str(tmp_path / 'test_image_rgb.jpg'				)
      PIL.Image.fromarray(np.zeros((5, 5)							,    dtype=np.uinta				)				).save(_snake_case							,    format='png'				)
      _A     =  pa.BufferOutputStream()
      with ParquetWriter(
          stream=_snake_case							,    features=Features({'image': Image()}				)							,    embed_local_files=_snake_case				) as writer:
            writer.write({'image': image_path}				)
            writer.finalize()
      _A     =  pa.BufferReader(output.getvalue()				)
      _A     =  pq.read_table(_snake_case				)
      _A     =  pa_table.to_pydict()
      if embed_local_files:
            assert isinstance(out['image'][0]['path']							,    _snake_case				)
            with open(_snake_case							,    'rb'				) as f:
                  assert out["image"][0]["bytes"] == f.read()
      else:
            assert out["image"][0]["path"] == image_path
            assert out["image"][0]["bytes"] is None







def 		_snake_case	(   )       ->   int:






      '''simple docstring'''


      _A     =  pa.schema([pa.field('col_1'							,    pa.string()							,    nullable=_snake_case				)]				)
      _A     =  pa.BufferOutputStream()
      with ArrowWriter(stream=_snake_case				) as writer:
            writer._build_writer(inferred_schema=_snake_case				)
            assert writer._schema == pa.schema([pa.field('col_1'							,    pa.string()				)]				)









'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCamelCase      					=False




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 32
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Union[str, Any]            =				VQModel(
           block_out_channels=[32, 64]      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,  latent_channels=3      ,  num_vq_embeddings=self.num_embed      ,  vq_embed_dim=3      ,  )
       return model
 @property
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
       return tokenizer
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Any            =				CLIPTextConfig(
           bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=self.text_embedder_hidden_size      ,  intermediate_size=37      ,  layer_norm_eps=1E-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=10_00      ,  )
       return CLIPTextModel(__lowerCAmelCase			)
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)

       UpperCamelCase_					: Optional[Any]            =				12
       UpperCamelCase_					: Optional[int]            =				12

       UpperCamelCase_					: Optional[Any]            =				{
           """attention_bias""": True,
           """cross_attention_dim""": 32,
           """attention_head_dim""": height * width,
           """num_attention_heads""": 1,
           """num_vector_embeds""": self.num_embed,
           """num_embeds_ada_norm""": self.num_embeds_ada_norm,
           """norm_num_groups""": 32,
           """sample_size""": width,
           """activation_fn""": """geglu-approximate""",
       }

       UpperCamelCase_					: Optional[Any]            =				TransformeraDModel(**__lowerCAmelCase			)
       return model
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				"""cpu"""

       UpperCamelCase_					: List[str]            =				self.dummy_vqvae
       UpperCamelCase_					: str            =				self.dummy_text_encoder
       UpperCamelCase_					: Optional[Any]            =				self.dummy_tokenizer
       UpperCamelCase_					: Optional[int]            =				self.dummy_transformer
       UpperCamelCase_					: Optional[Any]            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: Union[str, Any]            =				LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Optional[int]            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: Union[str, Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[Any]            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: Dict            =				output.images

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Dict            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: Any            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[str]            =				np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: int            =				"""cpu"""

       UpperCamelCase_					: List[Any]            =				self.dummy_vqvae
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_text_encoder
       UpperCamelCase_					: int            =				self.dummy_tokenizer
       UpperCamelCase_					: Tuple            =				self.dummy_transformer
       UpperCamelCase_					: Tuple            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: List[Any]            =				LearnedClassifierFreeSamplingEmbeddings(
           learnable=__lowerCAmelCase      ,  hidden_size=self.text_embedder_hidden_size      ,  length=tokenizer.model_max_length			)

       UpperCamelCase_					: Optional[Any]            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: List[Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: List[str]            =				output.images

       UpperCamelCase_					: Optional[int]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[str]            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

       UpperCamelCase_					: Dict            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: Optional[int]            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[Any]            =				np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






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






 """simple docstring"""





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





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

       UpperCamelCase_					: Optional[int]            =				VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
       UpperCamelCase_					: str            =				pipeline.to(__lowerCAmelCase			)
       pipeline.set_progress_bar_config(disable=__lowerCAmelCase			)

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       UpperCamelCase_					: List[Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: str            =				pipeline(
           """teddy bear playing in the pool"""      ,  num_images_per_prompt=1      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  )

       UpperCamelCase_					: Union[str, Any]            =				output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image			).max() < 2.0






