



"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = False   ,  _SCREAMING_SNAKE_CASE = False   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[Any]:





    '''simple docstring'''




    super().__init__(
        __lowerCAmelCase   ,  split=__lowerCAmelCase   ,  features=__lowerCAmelCase   ,  cache_dir=__lowerCAmelCase   ,  keep_in_memory=__lowerCAmelCase   ,  streaming=__lowerCAmelCase   ,  num_proc=__lowerCAmelCase   ,  **__lowerCAmelCase   ,  )
    UpperCAmelCase			:      List[Any]			=			field
    UpperCAmelCase			:      int			=			path_or_paths if isinstance(__lowerCAmelCase   ,  __lowerCAmelCase		) else {self.split: path_or_paths}
    UpperCAmelCase			:      List[str]			=			Json(
        cache_dir=__lowerCAmelCase   ,  data_files=__lowerCAmelCase   ,  features=__lowerCAmelCase   ,  field=__lowerCAmelCase   ,  **__lowerCAmelCase   ,  )




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    if self.streaming:
     UpperCAmelCase			:      Optional[int]			=			self.builder.as_streaming_dataset(split=self.split		)
    # Build regular (map-style) dataset
    else:
     UpperCAmelCase			:      Union[str, Any]			=			None
     UpperCAmelCase			:      Any			=			None
     UpperCAmelCase			:      List[Any]			=			None
     UpperCAmelCase			:      Tuple			=			None

     self.builder.download_and_prepare(
         download_config=__lowerCAmelCase   ,  download_mode=__lowerCAmelCase   ,  verification_mode=__lowerCAmelCase   ,  base_path=__lowerCAmelCase   ,  num_proc=self.num_proc   ,  )
     UpperCAmelCase			:      int			=			self.builder.as_dataset(
         split=self.split   ,  verification_mode=__lowerCAmelCase   ,  in_memory=self.keep_in_memory		)
    return dataset



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   List[str]:





    '''simple docstring'''




    if num_proc is not None and num_proc <= 0:
     raise ValueError(F"num_proc {num_proc} must be an integer > 0."		)

    UpperCAmelCase			:      str			=			dataset
    UpperCAmelCase			:      str			=			path_or_buf
    UpperCAmelCase			:      int			=			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    UpperCAmelCase			:      int			=			num_proc
    UpperCAmelCase			:      Any			=			"""utf-8"""
    UpperCAmelCase			:      int			=			to_json_kwargs

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.to_json_kwargs.pop("""path_or_buf"""   ,  __lowerCAmelCase		)
    UpperCAmelCase			:      Any			=			self.to_json_kwargs.pop("""orient"""   ,  """records"""		)
    UpperCAmelCase			:      List[Any]			=			self.to_json_kwargs.pop("""lines"""   ,  True if orient == """records""" else False		)
    UpperCAmelCase			:      Optional[Any]			=			self.to_json_kwargs.pop("""index"""   ,  False if orient in ["""split""", """table"""] else True		)
    UpperCAmelCase			:      List[Any]			=			self.to_json_kwargs.pop("""compression"""   ,  __lowerCAmelCase		)

    if compression not in [None, "infer", "gzip", "bz2", "xz"]:
     raise NotImplementedError(F"`datasets` currently does not support {compression} compression"		)

    if isinstance(self.path_or_buf   ,  (str, bytes, os.PathLike)		):
     with fsspec.open(self.path_or_buf   ,  """wb"""   ,  compression=__lowerCAmelCase		) as buffer:
      UpperCAmelCase			:      List[str]			=			self._write(file_obj=__lowerCAmelCase   ,  orient=__lowerCAmelCase   ,  lines=__lowerCAmelCase   ,  index=__lowerCAmelCase   ,  **self.to_json_kwargs		)
    else:
     if compression:
      raise NotImplementedError(
          F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
          """ was passed. Please provide a local path instead."""		)
     UpperCAmelCase			:      Dict			=			self._write(
         file_obj=self.path_or_buf   ,  orient=__lowerCAmelCase   ,  lines=__lowerCAmelCase   ,  index=__lowerCAmelCase   ,  **self.to_json_kwargs		)
    return written

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			args

    UpperCAmelCase			:      Any			=			query_table(
        table=self.dataset.data   ,  key=slice(__lowerCAmelCase   ,  offset + self.batch_size		)   ,  indices=self.dataset._indices   ,  )
    UpperCAmelCase			:      Dict			=			batch.to_pandas().to_json(
        path_or_buf=__lowerCAmelCase   ,  orient=__lowerCAmelCase   ,  lines=__lowerCAmelCase   ,  index=__lowerCAmelCase   ,  **__lowerCAmelCase		)
    if not json_str.endswith("""\n"""		):
     json_str += "\n"
    return json_str.encode(self.encoding		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			0

    if self.num_proc is None or self.num_proc == 1:
     for offset in logging.tqdm(
         range(0   ,  len(self.dataset		)   ,  self.batch_size		)   ,  unit="""ba"""   ,  disable=not logging.is_progress_bar_enabled()   ,  desc="""Creating json from Arrow format"""   ,  ):
      UpperCAmelCase			:      Union[str, Any]			=			self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
      written += file_obj.write(__lowerCAmelCase		)
    else:
     UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			len(self.dataset		), self.batch_size
     with multiprocessing.Pool(self.num_proc		) as pool:
      for json_str in logging.tqdm(
          pool.imap(
              self._batch_json   ,  [(offset, orient, lines, index, to_json_kwargs) for offset in range(0   ,  __lowerCAmelCase   ,  __lowerCAmelCase		)]   ,  )   ,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,  unit="""ba"""   ,  disable=not logging.is_progress_bar_enabled()   ,  desc="""Creating json from Arrow format"""   ,  ):
       written += file_obj.write(__lowerCAmelCase		)

    return written





"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     int		=		PriorTransformer
   __lowerCAmelCase						:     Dict		=		'hidden_states'

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			4
    UpperCAmelCase			:      Optional[Any]			=			8
    UpperCAmelCase			:      Union[str, Any]			=			7

    UpperCAmelCase			:      Any			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=0		)						->   Any:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			4
    UpperCAmelCase			:      Any			=			8
    UpperCAmelCase			:      List[Any]			=			7

    UpperCAmelCase			:      Any			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    return (4, 8)

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    return (4, 8)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			{
        """num_attention_heads""": 2,
        """attention_head_dim""": 4,
        """num_layers""": 2,
        """embedding_dim""": 8,
        """num_embeddings""": 7,
        """additional_embeddings""": 4,
    }
    UpperCAmelCase			:      Tuple			=			self.dummy_input
    return init_dict, inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained(
        """hf-internal-testing/prior-dummy"""   ,  output_loading_info=_SCREAMING_SNAKE_CASE		)
    self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(len(loading_info["""missing_keys"""]		)   ,  0		)

    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			model(**self.dummy_input		)[0]

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			self.prepare_init_args_and_inputs_for_common()

    UpperCAmelCase			:      str			=			self.model_class(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			inspect.signature(model.forward		)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    UpperCAmelCase			:      Union[str, Any]			=			[*signature.parameters.keys()]

    UpperCAmelCase			:      int			=			["""hidden_states""", """timestep"""]
    self.assertListEqual(arg_names[:2]   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
    UpperCAmelCase			:      Dict			=			model.to(_SCREAMING_SNAKE_CASE		)

    if hasattr(_SCREAMING_SNAKE_CASE   ,  """set_default_attn_processor"""		):
     model.set_default_attn_processor()

    UpperCAmelCase			:      str			=			self.get_dummy_seed_input()

    with torch.no_grad():
     UpperCAmelCase			:      str			=			model(**_SCREAMING_SNAKE_CASE		)[0]

    UpperCAmelCase			:      Dict			=			output[0, :5].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)

    # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
    # the expected output slices are not the same for CPU and GPU.
    UpperCAmelCase			:      Dict			=			torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]		)
    self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  rtol=1E-2		)		)



@slow
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=768   ,  _SCREAMING_SNAKE_CASE=77   ,  _SCREAMING_SNAKE_CASE=0		)						->   int:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			batch_size
    UpperCAmelCase			:      int			=			embedding_dim
    UpperCAmelCase			:      Tuple			=			num_embeddings

    UpperCAmelCase			:      Optional[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




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




   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
           [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
           # fmt: on
       ]		)
   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""   ,  subfolder="""prior"""		)
    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			self.get_dummy_seed_input(seed=_SCREAMING_SNAKE_CASE		)

    with torch.no_grad():
     UpperCAmelCase			:      Any			=			model(**_SCREAMING_SNAKE_CASE		)[0]

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

    UpperCAmelCase			:      int			=			sample[0, :8].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			torch.tensor(_SCREAMING_SNAKE_CASE		)

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