






'''simple docstring'''

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin




@skip_mps
class     A    (      SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Dict      =						IFPipeline
 __a			:						Union[str, Any]      =						TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
 __a			:						Tuple      =						TEXT_TO_IMAGE_BATCH_PARAMS
 __a			:						Dict      =						PipelineTesterMixin.required_optional_params - {'''latents'''}
 def   _UpperCAmelCase       (							self ):
       return self._get_dummy_components()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=0 ):
       if str(__lowerCAmelCase ).startswith("""mps""" ):
             UpperCamelCase_				:   Dict					    =							torch.manual_seed(__lowerCAmelCase )
       else:
             UpperCamelCase_				:   List[Any]					    =							torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )

       UpperCamelCase_				:   Optional[Any]					    =							{
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }

       return inputs
 def   _UpperCAmelCase       (							self ):
       self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""			,     reason="""float16 requires CUDA""" )
 def   _UpperCAmelCase       (							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   _UpperCAmelCase       (							self ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
 def   _UpperCAmelCase       (							self ):
       self._test_save_load_local()
 def   _UpperCAmelCase       (							self ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2			,     )





 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()			,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,     )
 def   _UpperCAmelCase       (							self ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )






@slow
@require_torch_gpu
class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def   _UpperCAmelCase       (							self ):
       # if

       UpperCamelCase_				:   Tuple					    =							IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""			,     variant="""fp16"""			,     torch_dtype=torch.floataa )

       UpperCamelCase_				:   Union[str, Any]					    =							IFSuperResolutionPipeline.from_pretrained(
           """DeepFloyd/IF-II-L-v1.0"""			,     variant="""fp16"""			,     torch_dtype=torch.floataa			,     text_encoder=__lowerCAmelCase			,     tokenizer=__lowerCAmelCase )

       # pre compute text embeddings and remove T5 to save memory

       pipe_a.text_encoder.to("""cuda""" )

       UpperCamelCase_    ,			UpperCamelCase_				:   Optional[int]					    =							pipe_a.encode_prompt("""anime turtle"""			,     device="""cuda""" )

       del pipe_a.tokenizer
       del pipe_a.text_encoder
       gc.collect()

       UpperCamelCase_				:   Any					    =							None
       UpperCamelCase_				:   Union[str, Any]					    =							None

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )

       self._test_if(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       UpperCamelCase_				:   int					    =							IFImgaImgPipeline(**pipe_a.components )
       UpperCamelCase_				:   int					    =							IFImgaImgSuperResolutionPipeline(**pipe_a.components )

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )

       self._test_if_imgaimg(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       UpperCamelCase_				:   str					    =							IFInpaintingPipeline(**pipe_a.components )
       UpperCamelCase_				:   int					    =							IFInpaintingSuperResolutionPipeline(**pipe_a.components )

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )

       self._test_if_inpainting(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       # pipeline 1

       _start_torch_memory_measurement()

       UpperCamelCase_				:   List[str]					    =							torch.Generator(device="""cpu""" ).manual_seed(0 )
       UpperCamelCase_				:   Dict					    =							pipe_a(
           prompt_embeds=__lowerCAmelCase			,     negative_prompt_embeds=__lowerCAmelCase			,     num_inference_steps=2			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )

       UpperCamelCase_				:   Dict					    =							output.images[0]

       assert image.shape == (64, 64, 3)

       UpperCamelCase_				:   List[str]					    =							torch.cuda.max_memory_allocated()
       assert mem_bytes < 13 * 10**9

       UpperCamelCase_				:   Optional[int]					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
       assert_mean_pixel_difference(__lowerCAmelCase			,     __lowerCAmelCase )

       # pipeline 2

       _start_torch_memory_measurement()

       UpperCamelCase_				:   Optional[int]					    =							torch.Generator(device="""cpu""" ).manual_seed(0 )

       UpperCamelCase_				:   Optional[Any]					    =							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )

       UpperCamelCase_				:   Dict					    =							pipe_a(
           prompt_embeds=__lowerCAmelCase			,     negative_prompt_embeds=__lowerCAmelCase			,     image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     num_inference_steps=2			,     output_type="""np"""			,     )

       UpperCamelCase_				:   List[str]					    =							output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       UpperCamelCase_				:   Tuple					    =							torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       UpperCamelCase_				:   List[str]					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" )
       assert_mean_pixel_difference(__lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       # pipeline 1

       _start_torch_memory_measurement()

       UpperCamelCase_				:   Union[str, Any]					    =							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							torch.Generator(device="""cpu""" ).manual_seed(0 )

       UpperCamelCase_				:   Union[str, Any]					    =							pipe_a(
           prompt_embeds=__lowerCAmelCase			,     negative_prompt_embeds=__lowerCAmelCase			,     image=__lowerCAmelCase			,     num_inference_steps=2			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )

       UpperCamelCase_				:   int					    =							output.images[0]

       assert image.shape == (64, 64, 3)

       UpperCamelCase_				:   Any					    =							torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       UpperCamelCase_				:   str					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
       assert_mean_pixel_difference(__lowerCAmelCase			,     __lowerCAmelCase )

       # pipeline 2

       _start_torch_memory_measurement()

       UpperCamelCase_				:   str					    =							torch.Generator(device="""cpu""" ).manual_seed(0 )

       UpperCamelCase_				:   List[str]					    =							floats_tensor((1, 3, 2_56, 2_56)			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )
       UpperCamelCase_				:   int					    =							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )

       UpperCamelCase_				:   str					    =							pipe_a(
           prompt_embeds=__lowerCAmelCase			,     negative_prompt_embeds=__lowerCAmelCase			,     image=__lowerCAmelCase			,     original_image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     num_inference_steps=2			,     output_type="""np"""			,     )

       UpperCamelCase_				:   Optional[int]					    =							output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       UpperCamelCase_				:   Optional[int]					    =							torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       UpperCamelCase_				:   Optional[int]					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" )
       assert_mean_pixel_difference(__lowerCAmelCase			,     __lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       # pipeline 1

       _start_torch_memory_measurement()

       UpperCamelCase_				:   str					    =							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(1 ) ).to(__lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							torch.Generator(device="""cpu""" ).manual_seed(0 )
       UpperCamelCase_				:   Optional[Any]					    =							pipe_a(
           prompt_embeds=__lowerCAmelCase			,     negative_prompt_embeds=__lowerCAmelCase			,     image=__lowerCAmelCase			,     mask_image=__lowerCAmelCase			,     num_inference_steps=2			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )

       UpperCamelCase_				:   List[str]					    =							output.images[0]

       assert image.shape == (64, 64, 3)

       UpperCamelCase_				:   Optional[Any]					    =							torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       UpperCamelCase_				:   Any					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
       assert_mean_pixel_difference(__lowerCAmelCase			,     __lowerCAmelCase )

       # pipeline 2

       _start_torch_memory_measurement()

       UpperCamelCase_				:   List[Any]					    =							torch.Generator(device="""cpu""" ).manual_seed(0 )

       UpperCamelCase_				:   Tuple					    =							floats_tensor((1, 3, 64, 64)			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							floats_tensor((1, 3, 2_56, 2_56)			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							floats_tensor((1, 3, 2_56, 2_56)			,     rng=random.Random(1 ) ).to(__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							pipe_a(
           prompt_embeds=__lowerCAmelCase			,     negative_prompt_embeds=__lowerCAmelCase			,     image=__lowerCAmelCase			,     mask_image=__lowerCAmelCase			,     original_image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     num_inference_steps=2			,     output_type="""np"""			,     )

       UpperCamelCase_				:   int					    =							output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       UpperCamelCase_				:   str					    =							torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       UpperCamelCase_				:   Any					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" )
       assert_mean_pixel_difference(__lowerCAmelCase			,     __lowerCAmelCase )






def 							snake_case					(     )       ->				List[Any]:





      """simple docstring"""

      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()














'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase							=logging.get_logger(__name__)  # pylint: disable=invalid-name



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  return ext

      raise Exception(
          f"Unable to determine file format from file extension {path}. "
          f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"	)



def 							snake_case					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							parser.add_parser("""run"""			,     help="""Run a pipeline through the CLI""" )
       run_parser.add_argument("""--task"""			,     choices=get_supported_tasks()			,     help="""Task to run""" )
       run_parser.add_argument("""--input"""			,     type=__lowerCAmelCase			,     help="""Path to the file to use for inference""" )
       run_parser.add_argument("""--output"""			,     type=__lowerCAmelCase			,     help="""Path to the file that will be used post to write results.""" )
       run_parser.add_argument("""--model"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model to instantiate.""" )
       run_parser.add_argument("""--config"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model's config to instantiate.""" )
       run_parser.add_argument(
           """--tokenizer"""			,     type=__lowerCAmelCase			,     help="""Name of the tokenizer to use. (default: same as the model name)""" )
       run_parser.add_argument(
           """--column"""			,     type=__lowerCAmelCase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
       run_parser.add_argument(
           """--format"""			,     type=__lowerCAmelCase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
       run_parser.add_argument(
           """--device"""			,     type=__lowerCAmelCase			,     default=-1			,     help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""			,     )
       run_parser.add_argument("""--overwrite"""			,     action="""store_true"""			,     help="""Allow overwriting the output file.""" )
       run_parser.set_defaults(func=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							nlp(**__lowerCAmelCase ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase )
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   outputs.append(__lowerCAmelCase )
             else:
                   outputs += output

        # Saving data
       if self._nlp.binary_output:
             UpperCamelCase_				:   int					    =							self._reader.save_binary(__lowerCAmelCase )
             logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}" )
       else:
             self._reader.save(__lowerCAmelCase )






