


"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Union[str, Any]          =DanceDiffusionPipeline
							__UpperCAmelCase		:					Optional[Any]          =UNCONDITIONAL_AUDIO_GENERATION_PARAMS
							__UpperCAmelCase		:					Optional[int]          =PipelineTesterMixin.required_optional_params - {
							    """callback""",
							    """latents""",
							    """callback_steps""",
							    """output_type""",
							    """num_images_per_prompt""",
							}
							__UpperCAmelCase		:					Tuple          =UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
							__UpperCAmelCase		:					List[str]          =False
							__UpperCAmelCase		:					Optional[int]          =False

							def     snake_case       (  self			):
										torch.manual_seed(0			)
										__lowerCAmelCase       					=    UNetaDModel(
										    block_out_channels=(32, 32, 64)					,     extra_in_channels=16					,     sample_size=5_12					,     sample_rate=1_60_00					,     in_channels=2					,     out_channels=2					,     flip_sin_to_cos=__a					,     use_timestep_embedding=__a					,     time_embedding_type="fourier"					,     mid_block_type="UNetMidBlock1D"					,     down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,     up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,     )
										__lowerCAmelCase       					=    IPNDMScheduler()

										__lowerCAmelCase       					=    {
										    "unet": unet,
										    "scheduler": scheduler,
										}
										return components

							def     snake_case       (  self					,     __a					,     __a=0			):
										if str(__a			).startswith("mps"			):
													__lowerCAmelCase       					=    torch.manual_seed(__a			)
										else:
													__lowerCAmelCase       					=    torch.Generator(device=__a			).manual_seed(__a			)
										__lowerCAmelCase       					=    {
										    "batch_size": 1,
										    "generator": generator,
										    "num_inference_steps": 4,
										}
										return inputs

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "cpu"  # ensure determinism for the device-dependent torch.Generator
										__lowerCAmelCase       					=    self.get_dummy_components()
										__lowerCAmelCase       					=    DanceDiffusionPipeline(**__a			)
										__lowerCAmelCase       					=    pipe.to(__a			)
										pipe.set_progress_bar_config(disable=__a			)

										__lowerCAmelCase       					=    self.get_dummy_inputs(__a			)
										__lowerCAmelCase       					=    pipe(**__a			)
										__lowerCAmelCase       					=    output.audios

										__lowerCAmelCase       					=    audio[0, -3:, -3:]

										assert audio.shape == (1, 2, components["unet"].sample_size)
										__lowerCAmelCase       					=    np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]			)
										assert np.abs(audio_slice.flatten() - expected_slice			).max() < 1e-2

							@skip_mps
							def     snake_case       (  self			):
										return super().test_save_load_local()

							@skip_mps
							def     snake_case       (  self			):
										return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)

							@skip_mps
							def     snake_case       (  self			):
										return super().test_save_load_optional_components()

							@skip_mps
							def     snake_case       (  self			):
										return super().test_attention_slicing_forward_pass()



							def     snake_case       (  self			):
										super().test_inference_batch_single_identical(expected_max_diff=3e-3			)
@slow
@require_torch_gpu
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    torch_device

										__lowerCAmelCase       					=    DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"			)
										__lowerCAmelCase       					=    pipe.to(__a			)
										pipe.set_progress_bar_config(disable=__a			)

										__lowerCAmelCase       					=    torch.manual_seed(0			)
										__lowerCAmelCase       					=    pipe(generator=__a					,     num_inference_steps=1_00					,     audio_length_in_s=4.0_9_6			)
										__lowerCAmelCase       					=    output.audios

										__lowerCAmelCase       					=    audio[0, -3:, -3:]

										assert audio.shape == (1, 2, pipe.unet.sample_size)
										__lowerCAmelCase       					=    np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]			)

										assert np.abs(audio_slice.flatten() - expected_slice			).max() < 1e-2



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    torch_device

										__lowerCAmelCase       					=    DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"					,     torch_dtype=torch.floataa			)
										__lowerCAmelCase       					=    pipe.to(__a			)
										pipe.set_progress_bar_config(disable=__a			)

										__lowerCAmelCase       					=    torch.manual_seed(0			)
										__lowerCAmelCase       					=    pipe(generator=__a					,     num_inference_steps=1_00					,     audio_length_in_s=4.0_9_6			)
										__lowerCAmelCase       					=    output.audios

										__lowerCAmelCase       					=    audio[0, -3:, -3:]

										assert audio.shape == (1, 2, pipe.unet.sample_size)
										__lowerCAmelCase       					=    np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]			)

										assert np.abs(audio_slice.flatten() - expected_slice			).max() < 1e-2








"""simple docstring"""


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 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =[
							    # (model_name, model_kwargs)
							    ("""bert-base-cased""", {}),
							    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
							]

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "tf"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "pt"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t"			) as vocab_file:
													vocab_file.write("\n".join(__a			)			)
													vocab_file.flush()
													__lowerCAmelCase       					=    BertTokenizerFast(vocab_file.name			)

										with TemporaryDirectory() as bert_save_dir:
													__lowerCAmelCase       					=    BertModel(BertConfig(vocab_size=len(__a			)			)			)
													model.save_pretrained(__a			)
													self._test_export(__a					,     "pt"					,     12					,     __a			)

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "tf"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(Path(__a			)			)

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

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "pt"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(__a			)

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

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																__lowerCAmelCase       					=    Path(__a			).joinpath("model.onnx"			)

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

													# Export
													convert(__a					,     __a					,     __a					,     __a					,     __a					,     **__a			)

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

							@require_torch
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "pt"			)

							@require_tf
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import TFBertModel

										__lowerCAmelCase       					=    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "tf"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    FeatureExtractionPipeline(__a					,     __a			)

										__lowerCAmelCase       					=    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    infer_shapes(__a					,     __a			)

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

										# 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     snake_case       (  self			):
										__lowerCAmelCase       					=    ["input_ids", "attention_mask", "token_type_ids"]
										__lowerCAmelCase       					=    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncContiguousArgs()					,     __a					,     __a			)

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

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

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

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncNonContiguousArgs()					,     __a					,     __a			)

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

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



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)					,     "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"					,     generated.as_posix()			)




