
import os
import sys
import unittest


lowercase  : str						   =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase  : Dict						   =							os.path.join(git_repo_path, """src""", """diffusers""")

class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				find_backend("""    if not is_torch_available():"""				)
           self.assertEqual(snake_case		,"""torch"""				)

           # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
           # self.assertEqual(backend_with_underscore, "tensorflow_text")

           lowercase							:					List[str]	     =				find_backend("""    if not (is_torch_available() and is_transformers_available()):"""				)
           self.assertEqual(snake_case		,"""torch_and_transformers"""				)

           # double_backend_with_underscore = find_backend(
           #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
           # )
           # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

           lowercase							:					Optional[int]	     =				find_backend(
               """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"""				)
           self.assertEqual(snake_case		,"""torch_and_transformers_and_onnx"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn("""torch"""		,snake_case				)
           self.assertIn("""torch_and_transformers"""		,snake_case				)
           self.assertIn("""flax_and_transformers"""		,snake_case				)
           self.assertIn("""torch_and_transformers_and_onnx"""		,snake_case				)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("""UNet2DModel"""		,objects["""torch"""]				)
           self.assertIn("""FlaxUNet2DConditionModel"""		,objects["""flax"""]				)
           self.assertIn("""StableDiffusionPipeline"""		,objects["""torch_and_transformers"""]				)
           self.assertIn("""FlaxStableDiffusionPipeline"""		,objects["""flax_and_transformers"""]				)
           self.assertIn("""LMSDiscreteScheduler"""		,objects["""torch_and_scipy"""]				)
           self.assertIn("""OnnxStableDiffusionPipeline"""		,objects["""torch_and_transformers_and_onnx"""]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				create_dummy_object("""CONSTANT"""		,"""'torch'"""				)
           self.assertEqual(snake_case		,"""\nCONSTANT = None\n"""				)

           lowercase							:					List[str]	     =				create_dummy_object("""function"""		,"""'torch'"""				)
           self.assertEqual(
               snake_case		,"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""				)

           lowercase							:					Union[str, Any]	     =				"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
           lowercase							:					List[str]	     =				create_dummy_object("""FakeClass"""		,"""'torch'"""				)
           self.assertEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
           lowercase							:					Dict	     =				create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}				)
           self.assertEqual(dummy_files["""torch"""]		,snake_case				)


from ...processing_utils import ProcessorMixin

class __snake_case		(				lowerCAmelCase					):
    _a		:						Union[str, Any]=    "WhisperFeatureExtractor"
    _a		:						int=    "WhisperTokenizer"
    def __init__(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case		,snake_case				)
           lowercase							:					Optional[int]	     =				self.feature_extractor
           lowercase							:					Tuple	     =				False
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=None		,snake_case=None		,snake_case=True				):



           '''simple docstring'''







           return self.tokenizer.get_decoder_prompt_ids(task=snake_case		,language=snake_case		,no_timestamps=snake_case				)
    def __call__(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor(*snake_case		,**snake_case				)

           lowercase							:					Optional[Any]	     =				kwargs.pop("""audio"""		,snake_case				)
           lowercase							:					str	     =				kwargs.pop("""sampling_rate"""		,snake_case				)
           lowercase							:					Dict	     =				kwargs.pop("""text"""		,snake_case				)
           if len(snake_case				) > 0:
                  lowercase							:					List[Any]	     =				args[0]
                  lowercase							:					Tuple	     =				args[1:]

           if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

           if audio is not None:
                  lowercase							:					Any	     =				self.feature_extractor(snake_case		,*snake_case		,sampling_rate=snake_case		,**snake_case				)
           if text is not None:
                  lowercase							:					str	     =				self.tokenizer(snake_case		,**snake_case				)

           if text is None:
                  return inputs

           elif audio is None:
                  return encodings
           else:
                  lowercase							:					List[Any]	     =				encodings["""input_ids"""]
                  return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case="np"				):



           '''simple docstring'''







           return self.tokenizer.get_prompt_ids(snake_case		,return_tensors=snake_case				)
