


import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class  SCREAMING_SNAKE_CASE__  :
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Tuple:
             return self.get_dummy_input()
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             if self.block_type == "down":
                    return (4, 3_2, 1_6, 1_6)
             elif self.block_type == "mid":
                    return (4, 3_2, 3_2, 3_2)
             elif self.block_type == "up":
                    return (4, 3_2, 6_4, 6_4)

             raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[str]=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       str=False     ,							SCREAMING_SNAKE_CASE__				:       Any=False     ,							)		->			Tuple:
             a_					:		List[Any]							=  4
             a_					:		Any							=  3_2
             a_					:		List[str]							=  (3_2, 3_2)

             a_					:		Any							=  torch.manual_seed(0     )
             a_					:		Dict							=  torch.device(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  (batch_size, num_channels) + sizes
             a_					:		Optional[int]							=  randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  {'hidden_states': hidden_states}

             if include_temb:
                    a_					:		Any							=  1_2_8
                    a_					:		Optional[int]							=  randn_tensor((batch_size, temb_channels)     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     )

             if include_res_hidden_states_tuple:
                    a_					:		int							=  torch.manual_seed(1     )
                    a_					:		List[str]							=  (randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     ),)

             if include_encoder_hidden_states:
                    a_					:		str							=  floats_tensor((batch_size, 3_2, 3_2)     ).to(SCREAMING_SNAKE_CASE__     )

             if include_skip_sample:
                    a_					:		Union[str, Any]							=  randn_tensor(((batch_size, 3) + sizes)     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     )

             return dummy_input
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Optional[int]:
             a_					:		Union[str, Any]							=  {
                 'in_channels': 3_2,
                 'out_channels': 3_2,
                 'temb_channels': 1_2_8,
             }
             if self.block_type == "up":
                    a_					:		Dict							=  3_2

             if self.block_type == "mid":
                    init_dict.pop('out_channels'     )

             a_					:		Union[str, Any]							=  self.dummy_input
             return init_dict, inputs_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[int]:
             a_							,		a_					:		Union[str, Any]							=  self.prepare_init_args_and_inputs_for_common()
             a_					:		Tuple							=  self.block_class(**SCREAMING_SNAKE_CASE__     )
             unet_block.to(SCREAMING_SNAKE_CASE__     )
             unet_block.eval()

             with torch.no_grad():
                    a_					:		Tuple							=  unet_block(**SCREAMING_SNAKE_CASE__     )

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  output[0]

             self.assertEqual(output.shape     ,							self.output_shape     )

             a_					:		int							=  output[0, -1, -3:, -3:]
             a_					:		str							=  torch.tensor(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
             assert torch_all_close(output_slice.flatten()     ,							SCREAMING_SNAKE_CASE__     ,							atol=5E-3     )

      @unittest.skipIf(torch_device == 'mps'     ,							'Training is not supported in mps'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[str]:
             a_							,		a_					:		Optional[Any]							=  self.prepare_init_args_and_inputs_for_common()
             a_					:		Tuple							=  self.block_class(**SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.train()
             a_					:		str							=  model(**SCREAMING_SNAKE_CASE__     )

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Dict							=  output[0]

             a_					:		Optional[int]							=  torch.device(SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  randn_tensor(output.shape     ,							device=SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             loss.backward()






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

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

UpperCAmelCase_      : List[str]  					=       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[str]  					=       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_      : Union[str, Any]  					=       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_      : Dict  					=       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_      : List[str]  					=       OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

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

UpperCAmelCase_      : Union[str, Any]  					=       OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_      : List[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Optional[int]  					=       _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_      : int  					=       _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_      : Tuple  					=       _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  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModel)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Any        =						FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_      : Union[str, Any]  					=       auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[int]  					=       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[str]        =						FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_      : int  					=       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_      : Dict  					=       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						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'
)


