



"""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


lowercase__			   =  logging.get_logger(__name__)


lowercase__			   =  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"),
    ]
)

lowercase__			   =  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"),
    ]
)

lowercase__			   =  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"),
    ]
)

lowercase__			   =  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"),
    ]
)

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

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

lowercase__			   =  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"),
    ]
)

lowercase__			   =  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"),
    ]
)

lowercase__			   =  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"),
    ]
)

lowercase__			   =  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"),
    ]
)

lowercase__			   =  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"),
    ]
)

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

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

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

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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_MAPPING







lowercase__			   =  auto_class_update(FlaxAutoModel)




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_PRETRAINING_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_MASKED_LM_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING







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




class SCREAMING_SNAKE_CASE__					(		_BaseAutoModelClass      ):
						_lowerCAmelCase							=					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING







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






"""simple docstring"""

from manim import *




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       Rectangle(height=0.5						,     width=0.5							)
													__a		:		Union[str, Any]									=       Rectangle(height=0.25						,     width=0.25							)
													__a		:		Dict									=       Rectangle(height=0.46						,     width=0.46							).set_stroke(width=0							)

													__a		:		Dict									=       [mem.copy() for i in range(6							)]
													__a		:		str									=       [mem.copy() for i in range(6							)]
													__a		:		Tuple									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[Any]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[Any]									=       VGroup(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		Union[str, Any]									=       Text("""CPU"""						,     font_size=24							)
													__a		:		Dict									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													cpu.move_to([-2.5, -0.5, 0]							)
													self.add(_lowercase							)

													__a		:		Optional[Any]									=       [mem.copy() for i in range(4							)]
													__a		:		Dict									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[str]									=       Text("""GPU"""						,     font_size=24							)
													__a		:		Any									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													gpu.move_to([-1, -1, 0]							)
													self.add(_lowercase							)

													__a		:		List[Any]									=       [mem.copy() for i in range(6							)]
													__a		:		Any									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)

													__a		:		Optional[Any]									=       Text("""Model"""						,     font_size=24							)
													__a		:		Any									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													model.move_to([3, -1.0, 0]							)
													self.add(_lowercase							)

													__a		:		Tuple									=       []
													__a		:		Tuple									=       []
													__a		:		Optional[int]									=       []

													for i, rect in enumerate(_lowercase							):
																				rect.set_stroke(_lowercase							)

																				__a		:		str									=       Rectangle(height=0.46 / 4						,     width=0.46 / 3							).set_stroke(width=0.0							).set_fill(_lowercase						,     opacity=0.7							)

																				if i == 0:
																											cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)						,     buff=0.02						,     direction=_lowercase							)
																											cpu_target.set_x(cpu_target.get_x() + 0.1							)
																				elif i == 3:
																											cpu_target.next_to(model_cpu_arr[0]						,     direction=_lowercase						,     buff=0.0							)
																				else:
																											cpu_target.next_to(model_cpu_arr[i - 1]						,     direction=_lowercase						,     buff=0.0							)
																				self.add(_lowercase							)
																				model_cpu_arr.append(_lowercase							)

													self.add(*_lowercase						,     *_lowercase						,     *_lowercase							)

													__a		:		Optional[Any]									=       [mem.copy() for i in range(6							)]
													__a		:		Union[str, Any]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)

													__a		:		Any									=       Text("""Loaded Checkpoint"""						,     font_size=24							)
													__a		:		str									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													checkpoint.move_to([3, 0.5, 0]							)

													self.add(_lowercase							)

													__a		:		Dict									=       []
													__a		:		int									=       []

													for i, rect in enumerate(_lowercase							):
																				__a		:		List[str]									=       fill.copy().set_fill(_lowercase						,     opacity=0.7							)
																				target.move_to(_lowercase							)
																				ckpt_arr.append(_lowercase							)

																				__a		:		Union[str, Any]									=       target.copy()
																				if i < 5:
																											cpu_target.move_to(cpu_left_col_base[i + 1]							)
																				else:
																											cpu_target.move_to(cpu_right_col_base[i - 5]							)
																				ckpt_cpu_arr.append(_lowercase							)
													self.add(*_lowercase						,     *_lowercase							)

													__a		:		List[str]									=       Square(side_length=2.2							)
													key.move_to([-5, 2, 0]							)

													__a		:		List[Any]									=       MarkupText(
													    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''						,     font_size=18						,     )

													key_text.move_to([-5, 2.4, 0]							)

													self.add(_lowercase						,     _lowercase							)

													__a		:		str									=       MarkupText(
													    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''						,     font_size=18						,     )

													blue_text.next_to(_lowercase						,     DOWN * 2.4						,     aligned_edge=key_text.get_left()							)
													self.add(_lowercase							)

													__a		:		Optional[int]									=       MarkupText(
													    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'''						,     font_size=24						,     )
													step_a.move_to([2, 2, 0]							)

													__a		:		List[Any]									=       [meta_mem.copy() for i in range(6							)]
													__a		:		Optional[int]									=       [meta_mem.copy() for i in range(6							)]
													__a		:		List[Any]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[str]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		Tuple									=       VGroup(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		Dict									=       Text("""Disk"""						,     font_size=24							)
													__a		:		Dict									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													disk.move_to([-4.0, -1.25, 0]							)
													self.play(Write(_lowercase						,     run_time=3							)						,     Write(_lowercase						,     run_time=1							)						,     Create(_lowercase						,     run_time=1							)							)

													__a		:		Optional[Any]									=       []
													for i, rect in enumerate(_lowercase							):
																				__a		:		List[str]									=       rect.copy()
																				target.generate_target()
																				target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
																				animations.append(MoveToTarget(_lowercase						,     run_time=1.5							)							)
													self.play(*_lowercase							)

													self.play(FadeOut(_lowercase							)							)

													__a		:		List[str]									=       MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''						,     font_size=24							)
													step_a.move_to([2, 2, 0]							)

													self.play(Write(_lowercase						,     run_time=3							)							)

													self.play(
													    FadeOut(_lowercase						,     _lowercase						,     *_lowercase						,     *_lowercase							)						,     )

													self.wait()

