
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class       _snake_case   :


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   :


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   (	unittest.TestCase							):
 __lowerCAmelCase  :     Optional[Any]        =			[
     # (model_name, model_kwargs)
     ('bert-base-cased', {}),
     ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
 ]


 @require_tf
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     self._test_export(SCREAMING_SNAKE_CASE_					,  """tf"""					,  12					,  **SCREAMING_SNAKE_CASE_)


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     self._test_export(SCREAMING_SNAKE_CASE_					,  """pt"""					,  12					,  **SCREAMING_SNAKE_CASE_)


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

   lowercase__  :   Union[str, Any]    		=      ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
   with NamedTemporaryFile(mode="""w+t""") as vocab_file:
     vocab_file.write("""\n""".join(SCREAMING_SNAKE_CASE_))
     vocab_file.flush()
     lowercase__  :   Optional[int]    		=      BertTokenizerFast(vocab_file.name)

   with TemporaryDirectory() as bert_save_dir:
     lowercase__  :   Union[str, Any]    		=      BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE_)))
     model.save_pretrained(SCREAMING_SNAKE_CASE_)
     self._test_export(SCREAMING_SNAKE_CASE_					,  """pt"""					,  12					,  SCREAMING_SNAKE_CASE_)


 @require_tf
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Any    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """tf"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[Any]    		=      quantize(Path(SCREAMING_SNAKE_CASE_))

     # Ensure the actual quantized model is not bigger than the original one
     if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE_).stat().st_size:
       self.fail("""Quantized model is bigger than initial ONNX model""")


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Tuple    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """pt"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Dict    		=      quantize(SCREAMING_SNAKE_CASE_)

     # Ensure the actual quantized model is not bigger than the original one
     if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE_).stat().st_size:
       self.fail("""Quantized model is bigger than initial ONNX model""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   try:
     # Compute path
     with TemporaryDirectory() as tempdir:
       lowercase__  :   List[Any]    		=      Path(SCREAMING_SNAKE_CASE_).joinpath("""model.onnx""")

     # Remove folder if exists
     if path.parent.exists():
       path.parent.rmdir()

     # Export
     convert(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)

     return path
   except Exception as e:
     self.fail(SCREAMING_SNAKE_CASE_)


 @require_torch
 @require_tokenizers
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

   lowercase__  :   Dict    		=      BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Union[str, Any]    		=      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
   self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  """pt""")


 @require_tf
 @require_tokenizers
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   from transformers import TFBertModel

   lowercase__  :   Union[str, Any]    		=      TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Optional[int]    		=      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
   self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  """tf""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      FeatureExtractionPipeline(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   Union[str, Any]    		=      ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
   lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   List[str]    		=      infer_shapes(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   # Assert all variables are present
   self.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  len(SCREAMING_SNAKE_CASE_))
   self.assertTrue(all(var_name in shapes for var_name in variable_names))
   self.assertSequenceEqual(variable_names[:3]					,  SCREAMING_SNAKE_CASE_)
   self.assertSequenceEqual(variable_names[3:]					,  SCREAMING_SNAKE_CASE_)

   # Assert inputs are {0: batch, 1: sequence}
   for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
     self.assertDictEqual(shapes[var_name]					,  {0: """batch""", 1: """sequence"""})

   # Assert outputs are {0: batch, 1: sequence} and {0: batch}
   self.assertDictEqual(shapes["""output_0"""]					,  {0: """batch""", 1: """sequence"""})
   self.assertDictEqual(shapes["""output_1"""]					,  {0: """batch"""})


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      ["""input_ids""", """attention_mask""", """token_type_ids"""]
   lowercase__  :   Tuple    		=      {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
   lowercase__		,    lowercase__  :   Optional[Any]    		=      ensure_valid_input(FuncContiguousArgs()					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   # Should have exactly the same number of args (all are valid)
   self.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  3)

   # Should have exactly the same input names
   self.assertEqual(set(SCREAMING_SNAKE_CASE_)					,  set(SCREAMING_SNAKE_CASE_))

   # Parameter should be reordered according to their respective place in the function:
   # (input_ids, token_type_ids, attention_mask)
   self.assertEqual(SCREAMING_SNAKE_CASE_					,  (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]))

   # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
   lowercase__		,    lowercase__  :   Any    		=      ensure_valid_input(FuncNonContiguousArgs()					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   # Should have exactly the one arg (all before the one not provided "some_other_args")
   self.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  1)
   self.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  1)

   # Should have only "input_ids"
   self.assertEqual(inputs_args[0]					,  tokens["""input_ids"""])
   self.assertEqual(ordered_input_names[0]					,  """input_ids""")



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      generate_identified_filename(Path("""/home/something/my_fake_model.onnx""")					,  """-test""")
   self.assertEqual("""/home/something/my_fake_model-test.onnx"""					,  generated.as_posix())








lowerCamelCase__    :		Any          =   """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_onnx_objects import *  # noqa F403
else:
     from .pipelines import OnnxRuntimeModel

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_pt_objects import *  # noqa F403
else:
     from .models import (
         AutoencoderKL,
         ControlNetModel,
         ModelMixin,
         PriorTransformer,
         TaFilmDecoder,
         TransformeraDModel,
         UNetaDModel,
         UNetaDConditionModel,
         UNetaDModel,
         UNetaDConditionModel,
         VQModel,
     )
     from .optimization import (
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
         get_scheduler,
     )
     from .pipelines import (
         AudioPipelineOutput,
         ConsistencyModelPipeline,
         DanceDiffusionPipeline,
         DDIMPipeline,
         DDPMPipeline,
         DiffusionPipeline,
         DiTPipeline,
         ImagePipelineOutput,
         KarrasVePipeline,
         LDMPipeline,
         LDMSuperResolutionPipeline,
         PNDMPipeline,
         RePaintPipeline,
         ScoreSdeVePipeline,
     )
     from .schedulers import (
         CMStochasticIterativeScheduler,
         DDIMInverseScheduler,
         DDIMParallelScheduler,
         DDIMScheduler,
         DDPMParallelScheduler,
         DDPMScheduler,
         DEISMultistepScheduler,
         DPMSolverMultistepInverseScheduler,
         DPMSolverMultistepScheduler,
         DPMSolverSinglestepScheduler,
         EulerAncestralDiscreteScheduler,
         EulerDiscreteScheduler,
         HeunDiscreteScheduler,
         IPNDMScheduler,
         KarrasVeScheduler,
         KDPMaAncestralDiscreteScheduler,
         KDPMaDiscreteScheduler,
         PNDMScheduler,
         RePaintScheduler,
         SchedulerMixin,
         ScoreSdeVeScheduler,
         UnCLIPScheduler,
         UniPCMultistepScheduler,
         VQDiffusionScheduler,
     )
     from .training_utils import EMAModel

try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .schedulers import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .schedulers import DPMSolverSDEScheduler

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         AltDiffusionImgaImgPipeline,
         AltDiffusionPipeline,
         AudioLDMPipeline,
         CycleDiffusionPipeline,
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
         ImageTextPipelineOutput,
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
         SemanticStableDiffusionPipeline,
         ShapEImgaImgPipeline,
         ShapEPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPipelineSafe,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
         TextToVideoSDPipeline,
         TextToVideoZeroPipeline,
         UnCLIPImageVariationPipeline,
         UnCLIPPipeline,
         UniDiffuserModel,
         UniDiffuserPipeline,
         UniDiffuserTextDecoder,
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
         VideoToVideoSDPipeline,
         VQDiffusionPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionKDiffusionPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .pipelines import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .pipelines import AudioDiffusionPipeline, Mel

try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .pipelines import SpectrogramDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_objects import *  # noqa F403
else:
     from .models.controlnet_flax import FlaxControlNetModel
     from .models.modeling_flax_utils import FlaxModelMixin
     from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
     from .models.vae_flax import FlaxAutoencoderKL
     from .pipelines import FlaxDiffusionPipeline
     from .schedulers import (
         FlaxDDIMScheduler,
         FlaxDDPMScheduler,
         FlaxDPMSolverMultistepScheduler,
         FlaxKarrasVeScheduler,
         FlaxLMSDiscreteScheduler,
         FlaxPNDMScheduler,
         FlaxSchedulerMixin,
         FlaxScoreSdeVeScheduler,
     )


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         FlaxStableDiffusionControlNetPipeline,
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )

try:
     if not (is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_note_seq_objects import *  # noqa F403
else:
     from .pipelines import MidiProcessor






