





import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard

     from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline




@require_flax
class  _lowerCamelCase       (			unittest.TestCase			):



      """simple docstring"""






      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






        with tempfile.TemporaryDirectory() as tmpdirname:
          # pipeline has Flax weights
          __snake_case      : Optional[Any]						=				FlaxDiffusionPipeline.from_pretrained(
              "hf-internal-testing/tiny-stable-diffusion-pipe" ,  safety_checker=UpperCAmelCase__ ,  cache_dir=UpperCAmelCase__      )

          __snake_case      : List[Any]						=				[t[-1] for t in os.walk(os.path.join(UpperCAmelCase__ ,  os.listdir(UpperCAmelCase__      )[0] ,  "snapshots"      )      )]
          __snake_case      : List[str]						=				[item for sublist in all_root_files for item in sublist]

          # None of the downloaded files should be a PyTorch file even if we have some here:
          # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
          assert not any(f.endswith(".bin"      ) for f in files      )




@slow
@require_flax
class  _lowerCamelCase       (			unittest.TestCase			):



      """simple docstring"""






      def   UpperCAmelCase						(       self      )				->      Union[str, Any]:






        '''simple docstring'''






        __snake_case			,      __snake_case      : Union[str, Any]						=				FlaxStableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-pipe" ,  safety_checker=UpperCAmelCase__      )

        __snake_case      : Union[str, Any]						=				(
            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
            " field, close up, split lighting, cinematic"
        )

        __snake_case      : Dict						=				jax.random.PRNGKey(0      )
        __snake_case      : Union[str, Any]						=				4

        __snake_case      : List[str]						=				jax.device_count()
        __snake_case      : Dict						=				num_samples * [prompt]
        __snake_case      : Optional[Any]						=				pipeline.prepare_inputs(UpperCAmelCase__      )

        # shard inputs and rng
        __snake_case      : int						=				replicate(UpperCAmelCase__      )
        __snake_case      : Optional[int]						=				jax.random.split(UpperCAmelCase__ ,  UpperCAmelCase__      )
        __snake_case      : Optional[int]						=				shard(UpperCAmelCase__      )

        __snake_case      : Dict						=				pipeline(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  jit=UpperCAmelCase__      ).images

        assert images.shape == (num_samples, 1, 64, 64, 3)
        if jax.device_count() == 8:
          assert np.abs(np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa      ).sum() - 4.1_514_745      ) < 1E-3
          assert np.abs(np.abs(UpperCAmelCase__ ,  dtype=np.floataa      ).sum() - 49947.875      ) < 5E-1

        __snake_case      : Optional[Any]						=				pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]      )      )      )
        assert len(UpperCAmelCase__      ) == num_samples







      def   UpperCAmelCase						(       self      )				->      str:






        '''simple docstring'''






        __snake_case			,      __snake_case      : int						=				FlaxStableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4" ,  revision="flax" ,  safety_checker=UpperCAmelCase__      )

        __snake_case      : str						=				(
            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
            " field, close up, split lighting, cinematic"
        )

        __snake_case      : int						=				jax.random.PRNGKey(0      )
        __snake_case      : Union[str, Any]						=				50

        __snake_case      : Tuple						=				jax.device_count()
        __snake_case      : List[Any]						=				num_samples * [prompt]
        __snake_case      : Tuple						=				pipeline.prepare_inputs(UpperCAmelCase__      )

        # shard inputs and rng
        __snake_case      : Dict						=				replicate(UpperCAmelCase__      )
        __snake_case      : Optional[Any]						=				jax.random.split(UpperCAmelCase__ ,  UpperCAmelCase__      )
        __snake_case      : Any						=				shard(UpperCAmelCase__      )

        __snake_case      : Dict						=				pipeline(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  jit=UpperCAmelCase__      ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa      ).sum() - 0.05_652_401)      ) < 1E-3
          assert np.abs((np.abs(UpperCAmelCase__ ,  dtype=np.floataa      ).sum() - 2383808.2)      ) < 5E-1







      def   UpperCAmelCase						(       self      )				->      Tuple:






        '''simple docstring'''






        __snake_case			,      __snake_case      : Dict						=				FlaxStableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4" ,  revision="bf16" ,  dtype=jnp.bfloataa ,  safety_checker=UpperCAmelCase__      )

        __snake_case      : str						=				(
            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
            " field, close up, split lighting, cinematic"
        )

        __snake_case      : str						=				jax.random.PRNGKey(0      )
        __snake_case      : Tuple						=				50

        __snake_case      : str						=				jax.device_count()
        __snake_case      : Union[str, Any]						=				num_samples * [prompt]
        __snake_case      : int						=				pipeline.prepare_inputs(UpperCAmelCase__      )

        # shard inputs and rng
        __snake_case      : List[Any]						=				replicate(UpperCAmelCase__      )
        __snake_case      : Optional[int]						=				jax.random.split(UpperCAmelCase__ ,  UpperCAmelCase__      )
        __snake_case      : Dict						=				shard(UpperCAmelCase__      )

        __snake_case      : List[Any]						=				pipeline(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  jit=UpperCAmelCase__      ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa      ).sum() - 0.04_003_906)      ) < 1E-3
          assert np.abs((np.abs(UpperCAmelCase__ ,  dtype=np.floataa      ).sum() - 2373516.75)      ) < 5E-1







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        __snake_case			,      __snake_case      : List[str]						=				FlaxStableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4" ,  revision="bf16" ,  dtype=jnp.bfloataa      )

        __snake_case      : str						=				(
            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
            " field, close up, split lighting, cinematic"
        )

        __snake_case      : Dict						=				jax.random.PRNGKey(0      )
        __snake_case      : Any						=				50

        __snake_case      : Any						=				jax.device_count()
        __snake_case      : Optional[Any]						=				num_samples * [prompt]
        __snake_case      : Dict						=				pipeline.prepare_inputs(UpperCAmelCase__      )

        # shard inputs and rng
        __snake_case      : int						=				replicate(UpperCAmelCase__      )
        __snake_case      : Optional[Any]						=				jax.random.split(UpperCAmelCase__ ,  UpperCAmelCase__      )
        __snake_case      : Dict						=				shard(UpperCAmelCase__      )

        __snake_case      : Union[str, Any]						=				pipeline(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  jit=UpperCAmelCase__      ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa      ).sum() - 0.04_003_906)      ) < 1E-3
          assert np.abs((np.abs(UpperCAmelCase__ ,  dtype=np.floataa      ).sum() - 2373516.75)      ) < 5E-1







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        __snake_case      : Any						=				FlaxDDIMScheduler(
            beta_start=0.00_085 ,  beta_end=0.012 ,  beta_schedule="scaled_linear" ,  set_alpha_to_one=UpperCAmelCase__ ,  steps_offset=1 ,  )

        __snake_case			,      __snake_case      : Optional[Any]						=				FlaxStableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4" ,  revision="bf16" ,  dtype=jnp.bfloataa ,  scheduler=UpperCAmelCase__ ,  safety_checker=UpperCAmelCase__ ,  )
        __snake_case      : Tuple						=				scheduler.create_state()

        __snake_case      : List[Any]						=				scheduler_state

        __snake_case      : List[str]						=				(
            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
            " field, close up, split lighting, cinematic"
        )

        __snake_case      : Any						=				jax.random.PRNGKey(0      )
        __snake_case      : Any						=				50

        __snake_case      : Tuple						=				jax.device_count()
        __snake_case      : Tuple						=				num_samples * [prompt]
        __snake_case      : Optional[Any]						=				pipeline.prepare_inputs(UpperCAmelCase__      )

        # shard inputs and rng
        __snake_case      : Tuple						=				replicate(UpperCAmelCase__      )
        __snake_case      : int						=				jax.random.split(UpperCAmelCase__ ,  UpperCAmelCase__      )
        __snake_case      : int						=				shard(UpperCAmelCase__      )

        __snake_case      : List[str]						=				pipeline(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  jit=UpperCAmelCase__      ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa      ).sum() - 0.045_043_945)      ) < 1E-3
          assert np.abs((np.abs(UpperCAmelCase__ ,  dtype=np.floataa      ).sum() - 2347693.5)      ) < 5E-1







      def   UpperCAmelCase						(       self      )				->      Union[str, Any]:






        '''simple docstring'''






        __snake_case      : Optional[int]						=				(
            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
            " field, close up, split lighting, cinematic"
        )

        __snake_case      : List[str]						=				jax.device_count()
        __snake_case      : Optional[Any]						=				num_samples * [prompt]
        __snake_case      : int						=				jax.random.split(jax.random.PRNGKey(0      ) ,  UpperCAmelCase__      )

        __snake_case			,      __snake_case      : Dict						=				FlaxStableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4" ,  revision="bf16" ,  dtype=jnp.bfloataa ,  safety_checker=UpperCAmelCase__ ,  )

        __snake_case      : int						=				replicate(UpperCAmelCase__      )
        __snake_case      : Optional[int]						=				pipeline.prepare_inputs(UpperCAmelCase__      )
        __snake_case      : Union[str, Any]						=				shard(UpperCAmelCase__      )
        __snake_case      : Optional[int]						=				pipeline(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  jit=UpperCAmelCase__      ).images
        assert images.shape == (num_samples, 1, 512, 512, 3)
        __snake_case      : Optional[int]						=				images[2, 0, 256, 10:17, 1]

        # With memory efficient attention
        __snake_case			,      __snake_case      : Tuple						=				FlaxStableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4" ,  revision="bf16" ,  dtype=jnp.bfloataa ,  safety_checker=UpperCAmelCase__ ,  use_memory_efficient_attention=UpperCAmelCase__ ,  )

        __snake_case      : str						=				replicate(UpperCAmelCase__      )
        __snake_case      : Tuple						=				pipeline.prepare_inputs(UpperCAmelCase__      )
        __snake_case      : Dict						=				shard(UpperCAmelCase__      )
        __snake_case      : Dict						=				pipeline(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__ ,  jit=UpperCAmelCase__      ).images
        assert images_eff.shape == (num_samples, 1, 512, 512, 3)
        __snake_case      : List[str]						=				images[2, 0, 256, 10:17, 1]

        # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
        # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
        assert abs(slice_eff - slice      ).max() < 1E-2




"""simple docstring"""





import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


_a       =							datasets.utils.logging.get_logger(__name__)

_a       =							['names', 'prefix']
_a       =							['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
_a       =							['encoding_errors', 'on_bad_lines']
_a       =							['date_format']




@dataclass
class 					_lowerCAmelCase  (	datasets.BuilderConfig   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	","
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, List[int], str]]   		=	"infer"
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, str, List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[Union[List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[Literal["c", "python", "pyarrow"]]   		=	None
 __UpperCAmelCase	:					Dict[Union[int, str], Callable[[Any], Any]]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[Union[int, List[int]]]   		=	None
 __UpperCAmelCase	:					Optional[int]   		=	None
 __UpperCAmelCase	:					Optional[Union[str, List[str]]]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	"."
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	'"'
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					int   		=	1_0_0_0_0
 __UpperCAmelCase	:					Optional[datasets.Features]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	"strict"
 __UpperCAmelCase	:					Literal["error", "warn", "skip"]   		=	"error"
 __UpperCAmelCase	:					Optional[str]   		=	None


 def 					_lowercase (			self							:			Tuple	):
    if self.delimiter is not None:
       __lowercase								=						self.delimiter
    if self.column_names is not None:
       __lowercase								=						self.column_names



 @property
 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						{
        "sep": self.sep,
        "header": self.header,
        "names": self.names,
        "index_col": self.index_col,
        "usecols": self.usecols,
        "prefix": self.prefix,
        "mangle_dupe_cols": self.mangle_dupe_cols,
        "engine": self.engine,
        "converters": self.converters,
        "true_values": self.true_values,
        "false_values": self.false_values,
        "skipinitialspace": self.skipinitialspace,
        "skiprows": self.skiprows,
        "nrows": self.nrows,
        "na_values": self.na_values,
        "keep_default_na": self.keep_default_na,
        "na_filter": self.na_filter,
        "verbose": self.verbose,
        "skip_blank_lines": self.skip_blank_lines,
        "thousands": self.thousands,
        "decimal": self.decimal,
        "lineterminator": self.lineterminator,
        "quotechar": self.quotechar,
        "quoting": self.quoting,
        "escapechar": self.escapechar,
        "comment": self.comment,
        "encoding": self.encoding,
        "dialect": self.dialect,
        "error_bad_lines": self.error_bad_lines,
        "warn_bad_lines": self.warn_bad_lines,
        "skipfooter": self.skipfooter,
        "doublequote": self.doublequote,
        "memory_map": self.memory_map,
        "float_precision": self.float_precision,
        "chunksize": self.chunksize,
        "encoding_errors": self.encoding_errors,
        "on_bad_lines": self.on_bad_lines,
        "date_format": self.date_format,
    }

    # some kwargs must not be passed if they don't have a default value
    # some others are deprecated and we can also not pass them if they are the default value
    for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
       if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig(),					UpperCAmelCase__	):
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 2):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

    return pd_read_csv_kwargs







class 					_lowerCAmelCase  (	datasets.ArrowBasedBuilder   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	CsvConfig


 def 					_lowercase (			self							:			List[str]	):
    return datasets.DatasetInfo(features=self.config.features	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			Dict	):
    if not self.config.data_files:
       raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""	)
    __lowercase								=						dl_manager.download_and_extract(self.config.data_files	)
    if isinstance(UpperCAmelCase__,					(str, list, tuple)	):
       __lowercase								=						data_files
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN,					gen_kwargs={"files": files}	)]
    __lowercase								=						[]
    for split_name, files in data_files.items():
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       splits.append(datasets.SplitGenerator(name=UpperCAmelCase__,					gen_kwargs={"files": files}	)	)
    return splits


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			pa.Table	):
    if self.config.features is not None:
       __lowercase								=						self.config.features.arrow_schema
       if all(not require_storage_cast(UpperCAmelCase__	) for feature in self.config.features.values()	):
          # cheaper cast
          __lowercase								=						pa.Table.from_arrays([pa_table[field.name] for field in schema],					schema=UpperCAmelCase__	)
       else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          __lowercase								=						table_cast(UpperCAmelCase__,					UpperCAmelCase__	)
    return pa_table



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __lowercase								=						(
        {
            name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCAmelCase__	) else object
            for name, dtype, feature in zip(schema.names,					schema.types,					self.config.features.values()	)
        }
        if schema is not None
        else None
    )
    for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__	)	):
       __lowercase								=						pd.read_csv(UpperCAmelCase__,					iterator=UpperCAmelCase__,					dtype=UpperCAmelCase__,					**self.config.pd_read_csv_kwargs	)
       try:
          for batch_idx, df in enumerate(UpperCAmelCase__	):
             __lowercase								=						pa.Table.from_pandas(UpperCAmelCase__	)
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase__	)
       except ValueError as e:
          logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase__	)}: {e}"""	)
          raise




