


"""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    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Tuple											=		DanceDiffusionPipeline
      a :     Optional[Any]											=		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
      a :     str											=		PipelineTesterMixin.required_optional_params - {
          """callback""",
          """latents""",
          """callback_steps""",
          """output_type""",
          """num_images_per_prompt""",
      }
      a :     List[Any]											=		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
      a :     Union[str, Any]											=		False
      a :     str											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       torch.manual_seed(0							)
       __lowercase									=	UNetaDModel(
           block_out_channels=(3_2, 3_2, 6_4)      ,       extra_in_channels=1_6      ,       sample_size=5_1_2      ,       sample_rate=1_6_0_0_0      ,       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""")      ,       )
       __lowercase									=	IPNDMScheduler()

       __lowercase									=	{
           """unet""": unet,
           """scheduler""": scheduler,
       }
       return components
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	Tuple      ,       A_	:	List[str]=0							):






       '''simple docstring'''


       if str(A_							).startswith("""mps"""							):
        __lowercase									=	torch.manual_seed(A_							)
       else:
        __lowercase									=	torch.Generator(device=A_							).manual_seed(A_							)
       __lowercase									=	{
           """batch_size""": 1,
           """generator""": generator,
           """num_inference_steps""": 4,
       }
       return inputs
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
       __lowercase									=	self.get_dummy_components()
       __lowercase									=	DanceDiffusionPipeline(**A_							)
       __lowercase									=	pipe.to(A_							)
       pipe.set_progress_bar_config(disable=A_							)

       __lowercase									=	self.get_dummy_inputs(A_							)
       __lowercase									=	pipe(**A_							)
       __lowercase									=	output.audios

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

       assert audio.shape == (1, 2, components["unet"].sample_size)
       __lowercase									=	np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]							)
       assert np.abs(audio_slice.flatten() - expected_slice							).max() < 1e-2
      @skip_mps
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       return super().test_save_load_local()
      @skip_mps
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


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






       '''simple docstring'''


       return super().test_save_load_optional_components()
      @skip_mps
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return super().test_attention_slicing_forward_pass()






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       super().test_inference_batch_single_identical(expected_max_diff=3e-3							)






@slow
@require_torch_gpu
class    lowerCamelCase__						(		unittest.TestCase    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	torch_device

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

       __lowercase									=	torch.manual_seed(0							)
       __lowercase									=	pipe(generator=A_      ,       num_inference_steps=1_0_0      ,       audio_length_in_s=4.0_96							)
       __lowercase									=	output.audios

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

       assert audio.shape == (1, 2, pipe.unet.sample_size)
       __lowercase									=	np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]							)

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






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	torch_device

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

       __lowercase									=	torch.manual_seed(0							)
       __lowercase									=	pipe(generator=A_      ,       num_inference_steps=1_0_0      ,       audio_length_in_s=4.0_96							)
       __lowercase									=	output.audios

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

       assert audio.shape == (1, 2, pipe.unet.sample_size)
       __lowercase									=	np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]							)

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




"""simple docstring"""

from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


UpperCAmelCase__  							=logging.get_logger(__name__)


UpperCAmelCase__  							=OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

UpperCAmelCase__  							=OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

UpperCAmelCase__  							=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase__  							=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase__  							=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase__  							=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase__  							=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     List[Any]											=		FLAX_MODEL_MAPPING






UpperCAmelCase__  							=auto_class_update(FlaxAutoModel)





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     int											=		FLAX_MODEL_FOR_PRETRAINING_MAPPING






UpperCAmelCase__  							=auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     Tuple											=		FLAX_MODEL_FOR_CAUSAL_LM_MAPPING






UpperCAmelCase__  							=auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     List[str]											=		FLAX_MODEL_FOR_MASKED_LM_MAPPING






UpperCAmelCase__  							=auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     str											=		FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






UpperCAmelCase__  							=auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     str											=		FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






UpperCAmelCase__  							=auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     Tuple											=		FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING






UpperCAmelCase__  							=auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     Union[str, Any]											=		FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING






UpperCAmelCase__  							=auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     Optional[Any]											=		FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING






UpperCAmelCase__  							=auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     Optional[int]											=		FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING






UpperCAmelCase__  							=auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     Union[str, Any]											=		FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






UpperCAmelCase__  							=auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     Dict											=		FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING






UpperCAmelCase__  							=auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")





class    lowerCamelCase__						(		_BaseAutoModelClass    ):
      a :     List[str]											=		FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING






UpperCAmelCase__  							=auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)
