
"""simple docstring"""
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_a   :      List[Any]       =				get_logger(__name__)

# Datasets
_a   :      Optional[Any]       =				"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
_a   :      Union[str, Any]       =				"""https://cdn-datasets.huggingface.co/datasets/datasets"""
_a   :      List[str]       =				"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

# Metrics
_a   :      Tuple       =				"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
_a   :      int       =				"""https://cdn-datasets.huggingface.co/datasets/metric"""
_a   :      List[str]       =				"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

# Hub
_a   :      int       =				os.environ.get("""HF_ENDPOINT""", """https://huggingface.co""")
_a   :      Dict       =				HF_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
_a   :      Dict       =				"""main"""

_a   :      List[str]       =				version.parse(platform.python_version())

if PY_VERSION < version.parse("""3.8"""):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
_a   :      Optional[Any]       =				{"""1""", """ON""", """YES""", """TRUE"""}
_a   :      Dict       =				ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
_a   :      str       =				version.parse(importlib_metadata.version("""dill"""))
_a   :      Tuple       =				version.parse(importlib_metadata.version("""pandas"""))
_a   :      int       =				version.parse(importlib_metadata.version("""pyarrow"""))

_a   :      Tuple       =				os.environ.get("""USE_TF""", """AUTO""").upper()
_a   :      Optional[Any]       =				os.environ.get("""USE_TORCH""", """AUTO""").upper()
_a   :      Tuple       =				os.environ.get("""USE_JAX""", """AUTO""").upper()

_a   :      int       =				"""N/A"""
_a   :      str       =				False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
    _a   :      List[str]       =				importlib.util.find_spec("""torch""") is not None
    if TORCH_AVAILABLE:
        try:
            _a   :      str       =				version.parse(importlib_metadata.version("""torch"""))
            logger.info(f'PyTorch version {TORCH_VERSION} available.')
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("""Disabling PyTorch because USE_TF is set""")

_a   :      Optional[int]       =				"""N/A"""
_a   :      str       =				False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
    _a   :      List[Any]       =				importlib.util.find_spec("""tensorflow""") is not None
    if TF_AVAILABLE:
        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
        for package in [
            "tensorflow",
            "tensorflow-cpu",
            "tensorflow-gpu",
            "tf-nightly",
            "tf-nightly-cpu",
            "tf-nightly-gpu",
            "intel-tensorflow",
            "tensorflow-rocm",
            "tensorflow-macos",
        ]:
            try:
                _a   :      Optional[int]       =				version.parse(importlib_metadata.version(package))
            except importlib_metadata.PackageNotFoundError:
                continue
            else:
                break
        else:
            _a   :      int       =				False
    if TF_AVAILABLE:
        if TF_VERSION.major < 2:
            logger.info(f'TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.')
            _a   :      List[str]       =				False
        else:
            logger.info(f'TensorFlow version {TF_VERSION} available.')
else:
    logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_a   :      Union[str, Any]       =				"""N/A"""
_a   :      List[Any]       =				False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    _a   :      int       =				importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
    if JAX_AVAILABLE:
        try:
            _a   :      Any       =				version.parse(importlib_metadata.version("""jax"""))
            logger.info(f'JAX version {JAX_VERSION} available.')
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("""Disabling JAX because USE_JAX is set to False""")


_a   :      int       =				os.environ.get("""USE_BEAM""", """AUTO""").upper()
_a   :      str       =				"""N/A"""
_a   :      List[str]       =				False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
    try:
        _a   :      List[Any]       =				version.parse(importlib_metadata.version("""apache_beam"""))
        _a   :      int       =				True
        logger.info(f'Apache Beam version {BEAM_VERSION} available.')
    except importlib_metadata.PackageNotFoundError:
        pass
else:
    logger.info("""Disabling Apache Beam because USE_BEAM is set to False""")


# Optional tools for data loading
_a   :      Union[str, Any]       =				importlib.util.find_spec("""sqlalchemy""") is not None

# Optional tools for feature decoding
_a   :      Tuple       =				importlib.util.find_spec("""PIL""") is not None
_a   :      Tuple       =				importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
_a   :      Dict       =				importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.1.0""")

# Optional compression tools
_a   :      Dict       =				importlib.util.find_spec("""rarfile""") is not None
_a   :      Union[str, Any]       =				importlib.util.find_spec("""zstandard""") is not None
_a   :      Tuple       =				importlib.util.find_spec("""lz4""") is not None
_a   :      str       =				importlib.util.find_spec("""py7zr""") is not None


# Cache location
_a   :      int       =				"""~/.cache"""
_a   :      Optional[Any]       =				os.getenv("""XDG_CACHE_HOME""", DEFAULT_XDG_CACHE_HOME)
_a   :      List[Any]       =				os.path.join(XDG_CACHE_HOME, """huggingface""")
_a   :      Union[str, Any]       =				os.path.expanduser(os.getenv("""HF_HOME""", DEFAULT_HF_CACHE_HOME))

_a   :      Optional[Any]       =				os.path.join(HF_CACHE_HOME, """datasets""")
_a   :      Tuple       =				Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

_a   :      List[Any]       =				os.path.join(HF_CACHE_HOME, """metrics""")
_a   :      Union[str, Any]       =				Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

_a   :      Optional[int]       =				os.path.join(HF_CACHE_HOME, """modules""")
_a   :      Dict       =				Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

_a   :      Optional[int]       =				"""downloads"""
_a   :      List[Any]       =				os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_a   :      List[str]       =				Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

_a   :      Tuple       =				"""extracted"""
_a   :      List[str]       =				os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_a   :      Union[str, Any]       =				Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_a   :      Optional[Any]       =				(
    os.environ.get("""HF_UPDATE_DOWNLOAD_COUNTS""", """AUTO""").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
_a   :      Optional[int]       =				1_000

# Size of the preloaded record batch in `Dataset.__iter__`
_a   :      Dict       =				10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
_a   :      List[str]       =				4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
_a   :      List[Any]       =				"""500MB"""

# Parquet configuration
_a   :      Optional[Any]       =				100
_a   :      Any       =				100
_a   :      Tuple       =				100

# Offline mode
_a   :      Union[str, Any]       =				os.environ.get("""HF_DATASETS_OFFLINE""", """AUTO""").upper() in ENV_VARS_TRUE_VALUES

# In-memory
_a   :      str       =				0  # Disabled
_a   :      str       =				float(os.environ.get("""HF_DATASETS_IN_MEMORY_MAX_SIZE""", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
_a   :      List[Any]       =				"""dataset.arrow"""
_a   :      List[Any]       =				"""indices.arrow"""
_a   :      Optional[int]       =				"""state.json"""
_a   :      str       =				"""dataset_info.json"""
_a   :      Optional[Any]       =				"""dataset_infos.json"""
_a   :      List[Any]       =				"""LICENSE"""
_a   :      Union[str, Any]       =				"""metric_info.json"""
_a   :      List[Any]       =				"""dataset_dict.json"""

_a   :      str       =				"""datasets_modules"""

_a   :      List[Any]       =				255

# Streaming
_a   :      Any       =				20
_a   :      Union[str, Any]       =				5

# Datasets without script
_a   :      Union[str, Any]       =				200
_a   :      List[str]       =				10
_a   :      Dict       =				200

# Progress bars
_a   :      Dict       =				0.05  # 20 progress updates per sec



"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,     reason="XFormers attention is only available with CUDA and `xformers` installed"    ,     )
    def        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


    def        lowerCamelCase__						(     self  ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2    ,     )

