

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFLEDForConditionalGeneration, TFLEDModel


@require_tf
class 				SCREAMING_SNAKE_CASE__ :
    __lowerCamelCase						:	List[str]														=					LEDConfig
    __lowerCamelCase						:	Tuple														=					{}
    __lowerCamelCase						:	str														=					"""gelu"""





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=False						,       a=99						,       a=32						,       a=2						,       a=4						,       a=37						,       a=0.1						,       a=0.1						,       a=20						,       a=2						,       a=1						,       a=0						,       a=4						,       ):
         lowercase__   :       List[Any]				=	parent
         lowercase__   :       List[str]				=	batch_size
         lowercase__   :       int				=	seq_length
         lowercase__   :       Optional[int]				=	is_training
         lowercase__   :       Optional[int]				=	use_labels
         lowercase__   :       Optional[Any]				=	vocab_size
         lowercase__   :       Tuple				=	hidden_size
         lowercase__   :       Union[str, Any]				=	num_hidden_layers
         lowercase__   :       str				=	num_attention_heads
         lowercase__   :       str				=	intermediate_size

         lowercase__   :       Union[str, Any]				=	hidden_dropout_prob
         lowercase__   :       List[str]				=	attention_probs_dropout_prob
         lowercase__   :       Optional[Any]				=	max_position_embeddings
         lowercase__   :       Any				=	eos_token_id
         lowercase__   :       List[Any]				=	pad_token_id
         lowercase__   :       Union[str, Any]				=	bos_token_id
         lowercase__   :       int				=	attention_window

         # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
         # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
         # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
         # because its local attention only attends to `self.attention_window` and one before and one after
         lowercase__   :       Optional[int]				=	self.attention_window + 2

         # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
         # the `test_attention_outputs` and `test_hidden_states_output` tests
         lowercase__   :       Union[str, Any]				=	(
             self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
         )





    def 				snake_case_    (				self):
         lowercase__   :       int				=	ids_tensor([self.batch_size, self.seq_length - 1]						,       self.vocab_size)
         lowercase__   :       Any				=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)						,       1)
         lowercase__   :       List[str]				=	tf.concat([input_ids, eos_tensor]						,       axis=1)

         lowercase__   :       Dict				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       Dict				=	self.config_cls(
             vocab_size=self.vocab_size						,       d_model=self.hidden_size						,       encoder_layers=self.num_hidden_layers						,       decoder_layers=self.num_hidden_layers						,       encoder_attention_heads=self.num_attention_heads						,       decoder_attention_heads=self.num_attention_heads						,       encoder_ffn_dim=self.intermediate_size						,       decoder_ffn_dim=self.intermediate_size						,       dropout=self.hidden_dropout_prob						,       attention_dropout=self.attention_probs_dropout_prob						,       max_position_embeddings=self.max_position_embeddings						,       eos_token_ids=[2]						,       bos_token_id=self.bos_token_id						,       pad_token_id=self.pad_token_id						,       decoder_start_token_id=self.pad_token_id						,       attention_window=self.attention_window						,       **self.config_updates						,       )
         lowercase__   :       Optional[Any]				=	prepare_led_inputs_dict(a						,       a						,       a)
         lowercase__   :       List[str]				=	tf.concat(
             [tf.zeros_like(a)[:, :-1], tf.ones_like(a)[:, -1:]]						,       axis=-1						,       )
         lowercase__   :       List[str]				=	global_attention_mask
         return config, inputs_dict





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       Optional[int]				=	TFLEDModel(config=a).get_decoder()
         lowercase__   :       Union[str, Any]				=	inputs_dict['input_ids']

         lowercase__   :       Dict				=	input_ids[:1, :]
         lowercase__   :       int				=	inputs_dict['attention_mask'][:1, :]
         lowercase__   :       List[str]				=	1

         # first forward pass
         lowercase__   :       Dict				=	model(a						,       attention_mask=a						,       use_cache=a)

         lowercase__				,			lowercase__   :       Optional[int]				=	outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         lowercase__   :       List[Any]				=	ids_tensor((self.batch_size, 3)						,       config.vocab_size)
         lowercase__   :       Union[str, Any]				=	tf.cast(ids_tensor((self.batch_size, 3)						,       2)						,       tf.inta)

         # append to next input_ids and
         lowercase__   :       str				=	tf.concat([input_ids, next_tokens]						,       axis=-1)
         lowercase__   :       Optional[Any]				=	tf.concat([attention_mask, next_attn_mask]						,       axis=-1)

         lowercase__   :       Optional[int]				=	model(a						,       attention_mask=a)[0]
         lowercase__   :       List[str]				=	model(a						,       attention_mask=a						,       past_key_values=a)[0]

         self.parent.assertEqual(next_tokens.shape[1]						,       output_from_past.shape[1])

         # select random slice
         lowercase__   :       Union[str, Any]				=	int(ids_tensor((1,)						,       output_from_past.shape[-1]))
         lowercase__   :       List[str]				=	output_from_no_past[:, -3:, random_slice_idx]
         lowercase__   :       List[Any]				=	output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(a						,       a						,       rtol=1e-3)



def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict=None     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   Dict=None     ,					SCREAMING_SNAKE_CASE_		:   int=None     ,					):



     '''simple docstring'''





     if attention_mask is None:
          lowercase__   :       Any				=	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_     ,					config.pad_token_id      )     ,					tf.inta      )
     if decoder_attention_mask is None:
          lowercase__   :       Dict				=	tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape     ,					dtype=tf.inta      ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,					config.pad_token_id      )     ,					tf.inta      ),
              ]     ,					axis=-1     ,					)
     if head_mask is None:
          lowercase__   :       str				=	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          lowercase__   :       Optional[Any]				=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
         "decoder_input_ids": decoder_input_ids,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
     }


@require_tf
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	int														=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    __lowerCamelCase						:	str														=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
    __lowerCamelCase						:	str														=					(
        {
            """conversational""": TFLEDForConditionalGeneration,
            """feature-extraction""": TFLEDModel,
            """summarization""": TFLEDForConditionalGeneration,
            """text2text-generation""": TFLEDForConditionalGeneration,
            """translation""": TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __lowerCamelCase						:	Union[str, Any]														=					True
    __lowerCamelCase						:	Optional[Any]														=					False
    __lowerCamelCase						:	Optional[Any]														=					False
    __lowerCamelCase						:	int														=					False





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	TFLEDModelTester(self)
         lowercase__   :       List[Any]				=	ConfigTester(self						,       config_class=a)





    def 				snake_case_    (				self):
         self.config_tester.run_common_tests()





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*a)





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       Union[str, Any]				=	self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__   :       Optional[int]				=	tf.zeros_like(inputs_dict['attention_mask'])
         lowercase__   :       Tuple				=	2
         lowercase__   :       int				=	tf.where(
             tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices						,       1						,       inputs_dict['global_attention_mask']						,       )

         lowercase__   :       Optional[Any]				=	True
         lowercase__   :       List[str]				=	self.model_tester.seq_length
         lowercase__   :       List[Any]				=	self.model_tester.encoder_seq_length

         def check_decoder_attentions_output(a):
              lowercase__   :       List[str]				=	outputs.decoder_attentions
              self.assertEqual(len(a)						,       self.model_tester.num_hidden_layers)
              self.assertListEqual(
                  list(decoder_attentions[0].shape[-3:])						,       [self.model_tester.num_attention_heads, seq_length, seq_length]						,       )

         def check_encoder_attentions_output(a):
              lowercase__   :       Dict				=	[t.numpy() for t in outputs.encoder_attentions]
              lowercase__   :       Dict				=	[t.numpy() for t in outputs.encoder_global_attentions]
              self.assertEqual(len(a)						,       self.model_tester.num_hidden_layers)
              self.assertEqual(len(a)						,       self.model_tester.num_hidden_layers)
              self.assertListEqual(
                  list(attentions[0].shape[-3:])						,       [self.model_tester.num_attention_heads, seq_length, seq_length]						,       )
              self.assertListEqual(
                  list(global_attentions[0].shape[-3:])						,       [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]						,       )

         for model_class in self.all_model_classes:
              lowercase__   :       Any				=	True
              lowercase__   :       Optional[int]				=	False
              lowercase__   :       List[Any]				=	False
              lowercase__   :       List[Any]				=	model_class(a)
              lowercase__   :       Optional[Any]				=	model(self._prepare_for_class(a						,       a))
              lowercase__   :       Optional[int]				=	len(a)
              self.assertEqual(config.output_hidden_states						,       a)
              check_encoder_attentions_output(a)

              if self.is_encoder_decoder:
                   lowercase__   :       Optional[int]				=	model_class(a)
                   lowercase__   :       int				=	model(self._prepare_for_class(a						,       a))
                   self.assertEqual(config.output_hidden_states						,       a)
                   check_decoder_attentions_output(a)

              # Check that output attentions can also be changed via the config
              del inputs_dict["output_attentions"]
              lowercase__   :       str				=	True
              lowercase__   :       str				=	model_class(a)
              lowercase__   :       str				=	model(self._prepare_for_class(a						,       a))
              self.assertEqual(config.output_hidden_states						,       a)
              check_encoder_attentions_output(a)

              # Check attention is always last and order is fine
              lowercase__   :       Union[str, Any]				=	True
              lowercase__   :       Optional[int]				=	True
              lowercase__   :       str				=	model_class(a)
              lowercase__   :       Tuple				=	model(self._prepare_for_class(a						,       a))

              self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,       len(a))
              self.assertEqual(model.config.output_hidden_states						,       a)
              check_encoder_attentions_output(a)





    @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.')
    def 				snake_case_    (				self):
         pass





    def 				snake_case_    (				self):
         # TODO: Head-masking not yet implement
         pass



def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     return tf.constant(SCREAMING_SNAKE_CASE_     ,					dtype=tf.intaa      )


snake_case_        =			1e-4


@slow
@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384').led

         # change to intended input here
         lowercase__   :       Any				=	_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
         lowercase__   :       Dict				=	_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
         lowercase__   :       Union[str, Any]				=	prepare_led_inputs_dict(model.config						,       a						,       a)
         lowercase__   :       Tuple				=	model(**a)[0]
         lowercase__   :       List[Any]				=	(1, 1024, 768)
         self.assertEqual(output.shape						,       a)
         # change to expected output here
         lowercase__   :       Optional[Any]				=	tf.convert_to_tensor(
             [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]						,       )
         tf.debugging.assert_near(output[:, :3, :3]						,       a						,       atol=1e-3)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')

         # change to intended input here
         lowercase__   :       Dict				=	_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
         lowercase__   :       List[str]				=	_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]])
         lowercase__   :       Tuple				=	prepare_led_inputs_dict(model.config						,       a						,       a)
         lowercase__   :       Tuple				=	model(**a)[0]
         lowercase__   :       Union[str, Any]				=	(1, 1024, model.config.vocab_size)
         self.assertEqual(output.shape						,       a)
         # change to expected output here
         lowercase__   :       Optional[int]				=	tf.convert_to_tensor(
             [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]						,       )
         tf.debugging.assert_near(output[:, :3, :3]						,       a						,       atol=1e-3						,       rtol=1e-3)




from collections import OrderedDict

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


snake_case_        =			logging.get_logger(__name__)


snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

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

snake_case_        =			OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

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


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModel)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_PRETRAINING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[Any]														=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_MASKED_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



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


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[str]														=					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)

