






'''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     :   Union[str, Any]	      =		logging.get_logger(__name__)


UpperCamelCase     :   int	      =		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     :   Optional[Any]	      =		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     :   List[str]	      =		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     :   Optional[Any]	      =		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     :   Optional[Any]	      =		OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

UpperCamelCase     :   Optional[int]	      =		OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

UpperCamelCase     :   Dict	      =		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     :   List[Any]	      =		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     :   List[str]	      =		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     :   int	      =		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     :   Tuple	      =		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     :   Tuple	      =		OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

UpperCamelCase     :   str	      =		OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

UpperCamelCase     :   Optional[int]	      =		OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

UpperCamelCase     :   int	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCamelCase     :   List[str]	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCamelCase     :   str	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase     :   List[Any]	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCamelCase     :   Any	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCamelCase     :   List[str]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCamelCase     :   List[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase     :   List[str]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCamelCase     :   Tuple	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCamelCase     :   Union[str, Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(FlaxAutoModel)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_PRETRAINING_MAPPING



UpperCamelCase     :   Optional[int]	      =		auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



UpperCamelCase     :   Dict	      =		auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_MASKED_LM_MAPPING



UpperCamelCase     :   List[str]	      =		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



UpperCamelCase     :   Tuple	      =		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



UpperCamelCase     :   Any	      =		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



UpperCamelCase     :   Tuple	      =		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)







'''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 					_a (a__,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					DanceDiffusionPipeline
     lowerCAmelCase_  :     int          =					UNCONDITIONAL_AUDIO_GENERATION_PARAMS
     lowerCAmelCase_  :     Optional[int]          =					PipelineTesterMixin.required_optional_params - {
         """callback""",
         """latents""",
         """callback_steps""",
         """output_type""",
         """num_images_per_prompt""",
     }
     lowerCAmelCase_  :     Tuple          =					UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
     lowerCAmelCase_  :     Dict          =					False
     lowerCAmelCase_  :     Tuple          =					False







     def 			snake_case_      (				self							)      ->				List[Any]:
          torch.manual_seed(0							)
          snake_case			:      List[str]        =			UNetaDModel(
              block_out_channels=(32, 32, 64)							,extra_in_channels=16							,sample_size=512							,sample_rate=16_000							,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""")							,)
          snake_case			:      Dict        =			IPNDMScheduler()

          snake_case			:      int        =			{
              """unet""": unet,
              """scheduler""": scheduler,
          }
          return components







     def 			snake_case_      (				self							,__a							,__a=0							)      ->				Tuple:
          if str(__a							).startswith("""mps"""							):
               snake_case			:      Optional[Any]        =			torch.manual_seed(__a							)
          else:
               snake_case			:      Union[str, Any]        =			torch.Generator(device=__a							).manual_seed(__a							)
          snake_case			:      str        =			{
              """batch_size""": 1,
              """generator""": generator,
              """num_inference_steps""": 4,
          }
          return inputs







     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      Dict        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          snake_case			:      str        =			self.get_dummy_components()
          snake_case			:      List[str]        =			DanceDiffusionPipeline(**__a							)
          snake_case			:      Dict        =			pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      Union[str, Any]        =			self.get_dummy_inputs(__a							)
          snake_case			:      str        =			pipe(**__a							)
          snake_case			:      Union[str, Any]        =			output.audios

          snake_case			:      List[str]        =			audio[0, -3:, -3:]

          assert audio.shape == (1, 2, components["unet"].sample_size)
          snake_case			:      Optional[Any]        =			np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000]							)
          assert np.abs(audio_slice.flatten() - expected_slice							).max() < 1E-2







     @skip_mps
     def 			snake_case_      (				self							)      ->				List[str]:
          return super().test_save_load_local()







     @skip_mps
     def 			snake_case_      (				self							)      ->				Union[str, Any]:
          return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)







     @skip_mps
     def 			snake_case_      (				self							)      ->				Dict:
          return super().test_save_load_optional_components()







     @skip_mps
     def 			snake_case_      (				self							)      ->				Any:
          return super().test_attention_slicing_forward_pass()







     def 			snake_case_      (				self							)      ->				str:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







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





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Optional[int]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      str        =			torch_device

          snake_case			:      List[str]        =			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""							)
          snake_case			:      Any        =			pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      int        =			torch.manual_seed(0							)
          snake_case			:      int        =			pipe(generator=__a							,num_inference_steps=100							,audio_length_in_s=4.096							)
          snake_case			:      Dict        =			output.audios

          snake_case			:      Union[str, Any]        =			audio[0, -3:, -3:]

          assert audio.shape == (1, 2, pipe.unet.sample_size)
          snake_case			:      Tuple        =			np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020]							)

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







     def 			snake_case_      (				self							)      ->				str:
          snake_case			:      str        =			torch_device

          snake_case			:      List[Any]        =			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""							,torch_dtype=torch.floataa							)
          snake_case			:      str        =			pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      str        =			torch.manual_seed(0							)
          snake_case			:      List[str]        =			pipe(generator=__a							,num_inference_steps=100							,audio_length_in_s=4.096							)
          snake_case			:      Optional[int]        =			output.audios

          snake_case			:      Any        =			audio[0, -3:, -3:]

          assert audio.shape == (1, 2, pipe.unet.sample_size)
          snake_case			:      Optional[int]        =			np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341]							)

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



