from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase			:							str     ={
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase			:							List[Any]     =[
        "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XLMForMultipleChoice",
        "XLMForQuestionAnswering",
        "XLMForQuestionAnsweringSimple",
        "XLMForSequenceClassification",
        "XLMForTokenClassification",
        "XLMModel",
        "XLMPreTrainedModel",
        "XLMWithLMHeadModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase			:							int     =[
        "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFXLMForMultipleChoice",
        "TFXLMForQuestionAnsweringSimple",
        "TFXLMForSequenceClassification",
        "TFXLMForTokenClassification",
        "TFXLMMainLayer",
        "TFXLMModel",
        "TFXLMPreTrainedModel",
        "TFXLMWithLMHeadModel",
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

    lowerCAmelCase			:							List[Any]     =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





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 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       IFPipeline
      _snake_case    		=       TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
      _snake_case    		=       TEXT_TO_IMAGE_BATCH_PARAMS
      _snake_case    		=       PipelineTesterMixin.required_optional_params - {'latents'}



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Optional[Any]:


          """simple docstring"""


          return self._get_dummy_components()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[Any]=0)				->Optional[Any]:


          """simple docstring"""


          if str(_UpperCamelCase).startswith("""mps"""):
              _lowerCamelCase     :		int 						=						torch.manual_seed(_UpperCamelCase)
          else:
              _lowerCamelCase     :		List[Any] 						=						torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """output_type""": """numpy""",
          }

          return inputs



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


          """simple docstring"""


          self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != """cuda"""    ,  reason="""float16 requires CUDA""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          super().test_save_load_floataa(expected_max_diff=1E-1)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Any:


          """simple docstring"""


          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



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


          """simple docstring"""


          self._test_save_load_local()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Dict:


          """simple docstring"""


          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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->int:


          """simple docstring"""


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)





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





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""    ,  variant="""fp16"""    ,  torch_dtype=torch.floataa)

          _lowerCamelCase     :		Tuple 						=						IFSuperResolutionPipeline.from_pretrained(
              """DeepFloyd/IF-II-L-v1.0"""    ,  variant="""fp16"""    ,  torch_dtype=torch.floataa    ,  text_encoder=_UpperCamelCase    ,  tokenizer=_UpperCamelCase)

          # pre compute text embeddings and remove T5 to save memory

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

          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						pipe_a.encode_prompt("""anime turtle"""    ,  device="""cuda""")

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

          _lowerCamelCase     :		str 						=						None
          _lowerCamelCase     :		str 						=						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(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          _lowerCamelCase     :		Optional[Any] 						=						IFImgaImgPipeline(**pipe_a.components)
          _lowerCamelCase     :		Optional[Any] 						=						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(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          _lowerCamelCase     :		Any 						=						IFInpaintingPipeline(**pipe_a.components)
          _lowerCamelCase     :		Dict 						=						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(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				str)				->Tuple:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		Optional[int] 						=						torch.Generator(device="""cpu""").manual_seed(0)
          _lowerCamelCase     :		Optional[Any] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

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

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

          _lowerCamelCase     :		Dict 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          _lowerCamelCase     :		Dict 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

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

          _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		str 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Any 						=						output.images[0]

          assert image.shape == (256, 256, 3)

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

          _lowerCamelCase     :		int 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						torch.Generator(device="""cpu""").manual_seed(0)

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

          _lowerCamelCase     :		Union[str, Any] 						=						output.images[0]

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

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

          _lowerCamelCase     :		List[Any] 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

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

          _lowerCamelCase     :		List[str] 						=						floats_tensor((1, 3, 256, 256)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  original_image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		List[Any] 						=						output.images[0]

          assert image.shape == (256, 256, 3)

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

          _lowerCamelCase     :		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(_UpperCamelCase    ,  _UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Tuple)				->Optional[int]:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(1)).to(_UpperCamelCase)

          _lowerCamelCase     :		int 						=						torch.Generator(device="""cpu""").manual_seed(0)
          _lowerCamelCase     :		Any 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  mask_image=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Any 						=						output.images[0]

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

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

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

          # pipeline 2

          _start_torch_memory_measurement()

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

          _lowerCamelCase     :		Union[str, Any] 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						floats_tensor((1, 3, 256, 256)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						floats_tensor((1, 3, 256, 256)    ,  rng=random.Random(1)).to(_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  mask_image=_UpperCamelCase    ,  original_image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Optional[Any] 						=						output.images[0]

          assert image.shape == (256, 256, 3)

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

          _lowerCamelCase     :		int 						=						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(_UpperCamelCase    ,  _UpperCamelCase)







def 						A__  (					):







    '''simple docstring'''
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()




