

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def       lowercase					(	__A :					List[str] ,    __A :					int ,    __A :					str		)				->			List[str]:





    '''simple docstring'''




    return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]







def       lowercase					(	__A :					List[str] ,    __A :					Tuple ,    __A :					List[Any] ,    __A :					List[Any]="attention"		)				->			Optional[int]:





    '''simple docstring'''




    snake_case							:		Dict        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]		)
    snake_case							:		Any        =		k_tmp.reshape(k_tmp.shape[0] ,    k_tmp.shape[1] * k_tmp.shape[2]		)
    snake_case							:		Optional[Any]        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]		)
    snake_case							:		Union[str, Any]        =		o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,    o_tmp.shape[2]		)
    snake_case							:		List[str]        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]		)
    snake_case							:		Union[str, Any]        =		q_tmp.reshape(q_tmp.shape[0] ,    q_tmp.shape[1] * q_tmp.shape[2]		)
    snake_case							:		Union[str, Any]        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]		)
    snake_case							:		Any        =		v_tmp.reshape(v_tmp.shape[0] ,    v_tmp.shape[1] * v_tmp.shape[2]		)
    return k, o, q, v







def       lowercase					(	__A :					str ,    __A :					List[str] ,    __A :					Dict ,    __A :					Union[str, Any]=False		)				->			Optional[Any]:





    '''simple docstring'''




    if split_mlp_wi:
        snake_case							:		List[Any]        =		params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
        snake_case							:		Optional[int]        =		params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
        snake_case							:		Any        =		(wi_a, wi_a)
    else:
        snake_case							:		Optional[int]        =		params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

    snake_case							:		Union[str, Any]        =		params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
    return wi, wo







def       lowercase					(	__A :					Dict ,    __A :					List[Any] ,    __A :					Union[str, Any] ,    __A :					int		)				->			Any:





    '''simple docstring'''




    return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]







def       lowercase					(	__A :					dict ,    *, __A :					int ,    __A :					bool ,    __A :					bool = False		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case							:		int        =		traverse_util.flatten_dict(variables["""target"""]		)
    snake_case							:		Dict        =		{"""/""".join(__A		): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    snake_case							:		Optional[int]        =		"""encoder/encoder/mlp/wi_0/kernel""" in old
    print("""Split MLP:""" ,    __A		)

    snake_case							:		List[Any]        =		collections.OrderedDict()

    # Shared embeddings.
    snake_case							:		Optional[Any]        =		old["""token_embedder/embedding"""]

    # Encoder.
    for i in range(__A		):
        # Block i, layer 0 (Self Attention).
        snake_case							:		Optional[Any]        =		tax_layer_norm_lookup(__A ,    __A ,    """encoder""" ,    """pre_attention_layer_norm"""		)
        snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Any        =		tax_attention_lookup(__A ,    __A ,    """encoder""" ,    """attention"""		)
        snake_case							:		Optional[int]        =		layer_norm
        snake_case							:		Tuple        =		k.T
        snake_case							:		Optional[Any]        =		o.T
        snake_case							:		Dict        =		q.T
        snake_case							:		Dict        =		v.T

        # Block i, layer 1 (MLP).
        snake_case							:		Dict        =		tax_layer_norm_lookup(__A ,    __A ,    """encoder""" ,    """pre_mlp_layer_norm"""		)
        snake_case      ,		snake_case							:		Any        =		tax_mlp_lookup(__A ,    __A ,    """encoder""" ,    __A		)
        snake_case							:		Dict        =		layer_norm
        if split_mlp_wi:
            snake_case							:		Union[str, Any]        =		wi[0].T
            snake_case							:		str        =		wi[1].T
        else:
            snake_case							:		List[Any]        =		wi.T
        snake_case							:		Dict        =		wo.T
        if scalable_attention:
            # convert the rel_embedding of each layer
            snake_case							:		Optional[int]        =		tax_relpos_bias_lookup(
                __A ,    __A ,    """encoder"""		).T

    snake_case							:		Any        =		old["""encoder/encoder_norm/scale"""]

    if not scalable_attention:
        snake_case							:		int        =		tax_relpos_bias_lookup(
            __A ,    0 ,    """encoder"""		).T
        snake_case							:		Tuple        =		tax_relpos_bias_lookup(
            __A ,    0 ,    """decoder"""		).T

    if not is_encoder_only:
        # Decoder.
        for i in range(__A		):
            # Block i, layer 0 (Self Attention).
            snake_case							:		int        =		tax_layer_norm_lookup(__A ,    __A ,    """decoder""" ,    """pre_self_attention_layer_norm"""		)
            snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Any        =		tax_attention_lookup(__A ,    __A ,    """decoder""" ,    """self_attention"""		)
            snake_case							:		Union[str, Any]        =		layer_norm
            snake_case							:		str        =		k.T
            snake_case							:		Dict        =		o.T
            snake_case							:		Optional[Any]        =		q.T
            snake_case							:		int        =		v.T

            # Block i, layer 1 (Cross Attention).
            snake_case							:		int        =		tax_layer_norm_lookup(__A ,    __A ,    """decoder""" ,    """pre_cross_attention_layer_norm"""		)
            snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Optional[Any]        =		tax_attention_lookup(__A ,    __A ,    """decoder""" ,    """encoder_decoder_attention"""		)
            snake_case							:		Union[str, Any]        =		layer_norm
            snake_case							:		int        =		k.T
            snake_case							:		Dict        =		o.T
            snake_case							:		Optional[Any]        =		q.T
            snake_case							:		Tuple        =		v.T

            # Block i, layer 2 (MLP).
            snake_case							:		int        =		tax_layer_norm_lookup(__A ,    __A ,    """decoder""" ,    """pre_mlp_layer_norm"""		)
            snake_case      ,		snake_case							:		Union[str, Any]        =		tax_mlp_lookup(__A ,    __A ,    """decoder""" ,    __A		)
            snake_case							:		List[Any]        =		layer_norm
            if split_mlp_wi:
                snake_case							:		Tuple        =		wi[0].T
                snake_case							:		Any        =		wi[1].T
            else:
                snake_case							:		Union[str, Any]        =		wi.T
            snake_case							:		Union[str, Any]        =		wo.T

            if scalable_attention:
                # convert the rel_embedding of each layer
                snake_case							:		int        =		tax_relpos_bias_lookup(__A ,    __A ,    """decoder"""		).T

        snake_case							:		Union[str, Any]        =		old["""decoder/decoder_norm/scale"""]

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            snake_case							:		Dict        =		old["""decoder/logits_dense/kernel"""].T

    return new







def       lowercase					(	__A :					str ,    __A :					bool		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        snake_case							:		Optional[int]        =		state_dict["""shared.weight"""]

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            snake_case							:		str        =		state_dict["""shared.weight"""]

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print("""Using shared word embeddings as lm_head."""		)
            snake_case							:		Any        =		state_dict["""shared.weight"""]

    return state_dict







def       lowercase					(	__A :					int ,    __A :					Dict ,    __A :					Union[str, Any] ,    __A :					Union[str, Any] ,    __A :					List[Any]		)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		int        =		checkpoints.load_tax_checkpoint(__A		)
    snake_case							:		Any        =		convert_tax_to_pytorch(
        __A ,    num_layers=config.num_layers ,    is_encoder_only=__A ,    scalable_attention=__A		)
    snake_case							:		Optional[int]        =		make_state_dict(__A ,    __A		)
    model.load_state_dict(__A ,    strict=__A		)







def       lowercase					(	__A :					str ,    __A :					int ,    __A :					Optional[int] ,    __A :					bool = False ,    __A :					bool = False ,    )				->			List[str]:





    '''simple docstring'''




    snake_case							:		List[str]        =		MTaConfig.from_json_file(__A		)
    print(f"""Building PyTorch model from configuration: {config}"""		)
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        snake_case							:		Tuple        =		UMTaEncoderModel(__A		)
    else:
        snake_case							:		str        =		UMTaForConditionalGeneration(__A		)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(__A ,    __A ,    __A ,    __A ,    __A		)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
    model.save_pretrained(__A		)

    # Verify that we can load the checkpoint.
    model.from_pretrained(__A		)
    print("""Done"""		)


if __name__ == "__main__":
     __lowercase     : str       =						argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
     )
     parser.add_argument(
         '''--scalable_attention''',
         action='''store_true''',
         help='''Whether the model uses scaled attention (umt5 model)''',
         default=False,
     )
     __lowercase     : List[str]       =						parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path,
         args.config_file,
         args.pytorch_dump_path,
         args.is_encoder_only,
         args.scalable_attention,
     )











"""simple docstring"""
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    MobileBertForMaskedLM,
							    MobileBertForMultipleChoice,
							    MobileBertForNextSentencePrediction,
							    MobileBertForPreTraining,
							    MobileBertForQuestionAnswering,
							    MobileBertForSequenceClassification,
							    MobileBertForTokenClassification,
							    MobileBertModel,
							)



class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=13  ,  lowerCAmelCase_=7  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=99  ,  lowerCAmelCase_=64  ,  lowerCAmelCase_=32  ,  lowerCAmelCase_=5  ,  lowerCAmelCase_=4  ,  lowerCAmelCase_=37  ,  lowerCAmelCase_="gelu"  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=512  ,  lowerCAmelCase_=16  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=0.02  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=4  ,  lowerCAmelCase_=None  ,  ):
		A_		:    Optional[Any]						     = parent
		A_		:    List[str]						     = batch_size
		A_		:    List[str]						     = seq_length
		A_		:    List[str]						     = is_training
		A_		:    Optional[Any]						     = use_input_mask
		A_		:    Dict						     = use_token_type_ids
		A_		:    Any						     = use_labels
		A_		:    Tuple						     = vocab_size
		A_		:    Optional[Any]						     = hidden_size
		A_		:    Tuple						     = embedding_size
		A_		:    Tuple						     = num_hidden_layers
		A_		:    Any						     = num_attention_heads
		A_		:    Optional[int]						     = intermediate_size
		A_		:    Union[str, Any]						     = hidden_act
		A_		:    Optional[int]						     = hidden_dropout_prob
		A_		:    Optional[Any]						     = attention_probs_dropout_prob
		A_		:    str						     = max_position_embeddings
		A_		:    str						     = type_vocab_size
		A_		:    Optional[Any]						     = type_sequence_label_size
		A_		:    List[str]						     = initializer_range
		A_		:    str						     = num_labels
		A_		:    Dict						     = num_choices
		A_		:    Any						     = scope





	def  lowerCamelCase(self				):
		A_		:    Any						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size				)

		A_		:    Optional[int]						     = None
		if self.use_input_mask:
			A_		:    str						     = random_attention_mask([self.batch_size, self.seq_length]				)

		A_		:    int						     = None
		if self.use_token_type_ids:
			A_		:    Union[str, Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size				)

		A_		:    Union[str, Any]						     = None
		A_		:    Union[str, Any]						     = None
		A_		:    List[str]						     = None
		if self.use_labels:
			A_		:    Dict						     = ids_tensor([self.batch_size]  ,  self.type_sequence_label_size				)
			A_		:    Union[str, Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels				)
			A_		:    Dict						     = ids_tensor([self.batch_size]  ,  self.num_choices				)

		A_		:    Tuple						     = self.get_config()

		return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





	def  lowerCamelCase(self				):
		return MobileBertConfig(
		    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  intermediate_size=self.intermediate_size  ,  embedding_size=self.embedding_size  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  type_vocab_size=self.type_vocab_size  ,  is_decoder=lowerCAmelCase_  ,  initializer_range=self.initializer_range  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = MobileBertModel(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = model(lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_				)
		A_		:    int						     = model(lowerCAmelCase_				)

		self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)				)
		self.parent.assertEqual(result.pooler_output.shape  ,  (self.batch_size, self.hidden_size)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[int]						     = MobileBertForMaskedLM(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    List[Any]						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[int]						     = MobileBertForNextSentencePrediction(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Tuple						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_  ,  )
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, 2)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    int						     = MobileBertForPreTraining(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Union[str, Any]						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_  ,  next_sentence_label=lowerCAmelCase_  ,  )
		self.parent.assertEqual(result.prediction_logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)				)
		self.parent.assertEqual(result.seq_relationship_logits.shape  ,  (self.batch_size, 2)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = MobileBertForQuestionAnswering(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Tuple						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  start_positions=lowerCAmelCase_  ,  end_positions=lowerCAmelCase_  ,  )
		self.parent.assertEqual(result.start_logits.shape  ,  (self.batch_size, self.seq_length)				)
		self.parent.assertEqual(result.end_logits.shape  ,  (self.batch_size, self.seq_length)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    int						     = self.num_labels
		A_		:    Optional[int]						     = MobileBertForSequenceClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    int						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = self.num_labels
		A_		:    int						     = MobileBertForTokenClassification(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    str						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = self.num_choices
		A_		:    int						     = MobileBertForMultipleChoice(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Tuple						     = input_ids.unsqueeze(1				).expand(-1  ,  self.num_choices  ,  -1				).contiguous()
		A_		:    str						     = token_type_ids.unsqueeze(1				).expand(-1  ,  self.num_choices  ,  -1				).contiguous()
		A_		:    Optional[int]						     = input_mask.unsqueeze(1				).expand(-1  ,  self.num_choices  ,  -1				).contiguous()
		A_		:    Union[str, Any]						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_  ,  )
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.prepare_config_and_inputs()
		(
		    (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  
		)		:    Dict						     = config_and_inputs
		A_		:    Any						     = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
		return config, inputs_dict






@require_torch
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Dict 			=					(
	    (
	        MobileBertModel,
	        MobileBertForMaskedLM,
	        MobileBertForMultipleChoice,
	        MobileBertForNextSentencePrediction,
	        MobileBertForPreTraining,
	        MobileBertForQuestionAnswering,
	        MobileBertForSequenceClassification,
	        MobileBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_A							:					Union[str, Any] 			=					(
	    {
	        """feature-extraction""": MobileBertModel,
	        """fill-mask""": MobileBertForMaskedLM,
	        """question-answering""": MobileBertForQuestionAnswering,
	        """text-classification""": MobileBertForSequenceClassification,
	        """token-classification""": MobileBertForTokenClassification,
	        """zero-shot""": MobileBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_A							:					Union[str, Any] 			=					True





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		A_		:    Optional[int]						     = super()._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_  ,  return_labels=lowerCAmelCase_				)

		if return_labels:
			if model_class in get_values(lowerCAmelCase_				):
				A_		:    List[Any]						     = torch.zeros(
				    (self.model_tester.batch_size, self.model_tester.seq_length)  ,  dtype=torch.long  ,  device=lowerCAmelCase_				)
				A_		:    Optional[int]						     = torch.zeros(
				    self.model_tester.batch_size  ,  dtype=torch.long  ,  device=lowerCAmelCase_				)
		return inputs_dict





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = MobileBertModelTester(self				)
		A_		:    Optional[Any]						     = ConfigTester(self  ,  config_class=lowerCAmelCase_  ,  hidden_size=37				)





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





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Any						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase_				)






def       __UpperCamelCase						(		snake_case__       ):
	return torch.tensor(
	    snake_case__ , dtype=torch.long , device=snake_case__ , )


_lowerCAmelCase       =				1e-3




@require_torch
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	@slow
	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = MobileBertModel.from_pretrained("""google/mobilebert-uncased"""				).to(lowerCAmelCase_				)
		A_		:    Any						     = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]				)
		with torch.no_grad():
			A_		:    Dict						     = model(lowerCAmelCase_				)[0]
		A_		:    Any						     = torch.Size((1, 9, 512)				)
		self.assertEqual(output.shape  ,  lowerCAmelCase_				)
		A_		:    Optional[int]						     = torch.tensor(
		    [
		        [
		            [-2.4_7_3_6_5_2_6e0_7, 8.2_6_9_1_6_5_6e0_4, 1.6_5_2_1_8_3_8e0_5],
		            [-5.7_5_4_1_7_0_4e-0_1, 3.9_0_5_6_0_2_2e0_0, 4.4_0_1_1_5_0_7e0_0],
		            [2.6_0_4_7_3_5_9e0_0, 1.5_6_7_7_6_5_2e0_0, -1.7_3_2_4_1_8_8e-0_1],
		        ]
		    ]  ,  device=lowerCAmelCase_  ,  )

		# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
		# ~1 difference, it's therefore not a good idea to measure using addition.
		# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
		# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
		A_		:    List[str]						     = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE				)
		A_		:    Any						     = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE				)

		self.assertTrue(lower_bound and upper_bound				)

