
"""simple docstring"""
from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


lowercase		:    Any			=       AttentionProcessor




class  _snake_case	(    _lowercase							):






							def __init__(       self    :      List[str]    ,			*snake_case__    :      Optional[int]    ,			**snake_case__    :      str     ):






												"""simple docstring"""



												__a:    Tuple		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )






class  _snake_case	(    _lowercase							):






							def __init__(       self    :      Optional[int]    ,			*snake_case__    :      Tuple    ,			**snake_case__    :      int     ):






												"""simple docstring"""



												__a:    List[str]		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )






class  _snake_case	(    _lowercase							):






							def __init__(       self    :      str    ,			*snake_case__    :      List[Any]    ,			**snake_case__    :      Tuple     ):






												"""simple docstring"""



												__a:    Tuple		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )






class  _snake_case	(    _lowercase							):






							def __init__(       self    :      int    ,			*snake_case__    :      List[Any]    ,			**snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



												__a:    Any		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )






class  _snake_case	(    _lowercase							):






							def __init__(       self    :      Optional[Any]    ,			*snake_case__    :      Any    ,			**snake_case__    :      Tuple     ):






												"""simple docstring"""



												__a:    Any		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )






class  _snake_case	(    _lowercase							):






							def __init__(       self    :      List[str]    ,			*snake_case__    :      str    ,			**snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )






class  _snake_case	(    _lowercase							):






							def __init__(       self    :      Dict    ,			*snake_case__    :      int    ,			**snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )






class  _snake_case	(    _lowercase							):






							def __init__(       self    :      Dict    ,			*snake_case__    :      Union[str, Any]    ,			**snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Dict		     =       f"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead."""
												deprecate("""cross_attention"""    ,			"""0.20.0"""    ,			snake_case__    ,			standard_warn=snake_case__     )
												super().__init__(*snake_case__    ,			**snake_case__     )


"""simple docstring"""
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch




class  _snake_case	(    unittest.TestCase							):






							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """env"""]     )
							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												with CaptureStd() as cs:
																	transformers.commands.transformers_cli.main()
												self.assertIn("""Python version"""    ,			cs.out     )
												self.assertIn("""Platform"""    ,			cs.out     )
												self.assertIn("""Using distributed or parallel set-up in script?"""    ,			cs.out     )






							@is_pt_tf_cross_test
							@patch(
							    """sys.argv"""    ,			["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]     )
							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""    ,			ignore_errors=snake_case__     )  # cleans potential past runs
												transformers.commands.transformers_cli.main()

												# The original repo has no TF weights -- if they exist, they were created by the CLI
												self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""     )     )






							@require_torch
							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]     )
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""     )     )






							@require_torch
							@patch(
							    """sys.argv"""    ,			[
							        """fakeprogrampath""",
							        """download""",
							        """hf-internal-testing/test_dynamic_model_with_tokenizer""",
							        """--trust-remote-code""",
							        """--cache-dir""",
							        """/tmp""",
							    ]    ,			)
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""     )     )
												self.assertTrue(
												    os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""     )     )
