





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      							=   logging.get_logger(__name__)




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		MobileViTConfig()

       # size of the architecture
       if "mobilevit_s" in mobilevit_name:
              _lowerCamelCase				:  Optional[int]						=		[1_44, 1_92, 2_40]
              _lowerCamelCase				:  List[Any]						=		[16, 32, 64, 96, 1_28, 1_60, 6_40]
       elif "mobilevit_xs" in mobilevit_name:
              _lowerCamelCase				:  List[Any]						=		[96, 1_20, 1_44]
              _lowerCamelCase				:  Tuple						=		[16, 32, 48, 64, 80, 96, 3_84]
       elif "mobilevit_xxs" in mobilevit_name:
              _lowerCamelCase				:  List[str]						=		[64, 80, 96]
              _lowerCamelCase				:  Dict						=		[16, 16, 24, 48, 64, 80, 3_20]
              _lowerCamelCase				:  List[Any]						=		0.0_5
              _lowerCamelCase				:  List[str]						=		2.0

       if mobilevit_name.startswith('''deeplabv3_'''   ):
              _lowerCamelCase				:  Dict						=		5_12
              _lowerCamelCase				:  Tuple						=		16
              _lowerCamelCase				:  List[str]						=		21
              _lowerCamelCase				:  Optional[int]						=		'''pascal-voc-id2label.json'''
       else:
              _lowerCamelCase				:  List[str]						=		10_00
              _lowerCamelCase				:  List[Any]						=		'''imagenet-1k-id2label.json'''

       _lowerCamelCase				:  int						=		'''huggingface/label-files'''
       _lowerCamelCase				:  List[str]						=		json.load(open(hf_hub_download(lowercase_			,     lowercase_			,     repo_type='''dataset'''   )			,     '''r'''   )   )
       _lowerCamelCase				:  int						=		{int(lowercase_   ): v for k, v in idalabel.items()}
       _lowerCamelCase				:  Optional[Any]						=		idalabel
       _lowerCamelCase				:  Tuple						=		{v: k for k, v in idalabel.items()}

       return config




def  __UpperCAmelCase(   lowercase_			,     lowercase_=False   ):
       for i in range(1			,     6   ):
              if F"""layer_{i}.""" in name:
                     _lowerCamelCase				:  List[str]						=		name.replace(F"""layer_{i}."""			,     F"""encoder.layer.{i - 1}."""   )

       if "conv_1." in name:
              _lowerCamelCase				:  int						=		name.replace('''conv_1.'''			,     '''conv_stem.'''   )
       if ".block." in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''.block.'''			,     '''.'''   )

       if "exp_1x1" in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''exp_1x1'''			,     '''expand_1x1'''   )
       if "red_1x1" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''red_1x1'''			,     '''reduce_1x1'''   )
       if ".local_rep.conv_3x3." in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''.local_rep.conv_3x3.'''			,     '''.conv_kxk.'''   )
       if ".local_rep.conv_1x1." in name:
              _lowerCamelCase				:  str						=		name.replace('''.local_rep.conv_1x1.'''			,     '''.conv_1x1.'''   )
       if ".norm." in name:
              _lowerCamelCase				:  Dict						=		name.replace('''.norm.'''			,     '''.normalization.'''   )
       if ".conv." in name:
              _lowerCamelCase				:  Any						=		name.replace('''.conv.'''			,     '''.convolution.'''   )
       if ".conv_proj." in name:
              _lowerCamelCase				:  Any						=		name.replace('''.conv_proj.'''			,     '''.conv_projection.'''   )

       for i in range(0			,     2   ):
              for j in range(0			,     4   ):
                     if F""".{i}.{j}.""" in name:
                            _lowerCamelCase				:  Tuple						=		name.replace(F""".{i}.{j}."""			,     F""".{i}.layer.{j}."""   )

       for i in range(2			,     6   ):
              for j in range(0			,     4   ):
                     if F""".{i}.{j}.""" in name:
                            _lowerCamelCase				:  int						=		name.replace(F""".{i}.{j}."""			,     F""".{i}."""   )
                            if "expand_1x1" in name:
                                   _lowerCamelCase				:  List[Any]						=		name.replace('''expand_1x1'''			,     '''downsampling_layer.expand_1x1'''   )
                            if "conv_3x3" in name:
                                   _lowerCamelCase				:  Tuple						=		name.replace('''conv_3x3'''			,     '''downsampling_layer.conv_3x3'''   )
                            if "reduce_1x1" in name:
                                   _lowerCamelCase				:  Optional[Any]						=		name.replace('''reduce_1x1'''			,     '''downsampling_layer.reduce_1x1'''   )

       for i in range(2			,     5   ):
              if F""".global_rep.{i}.weight""" in name:
                     _lowerCamelCase				:  Tuple						=		name.replace(F""".global_rep.{i}.weight"""			,     '''.layernorm.weight'''   )
              if F""".global_rep.{i}.bias""" in name:
                     _lowerCamelCase				:  Optional[int]						=		name.replace(F""".global_rep.{i}.bias"""			,     '''.layernorm.bias'''   )

       if ".global_rep." in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''.global_rep.'''			,     '''.transformer.'''   )
       if ".pre_norm_mha.0." in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''.pre_norm_mha.0.'''			,     '''.layernorm_before.'''   )
       if ".pre_norm_mha.1.out_proj." in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''.pre_norm_mha.1.out_proj.'''			,     '''.attention.output.dense.'''   )
       if ".pre_norm_ffn.0." in name:
              _lowerCamelCase				:  Any						=		name.replace('''.pre_norm_ffn.0.'''			,     '''.layernorm_after.'''   )
       if ".pre_norm_ffn.1." in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''.pre_norm_ffn.1.'''			,     '''.intermediate.dense.'''   )
       if ".pre_norm_ffn.4." in name:
              _lowerCamelCase				:  int						=		name.replace('''.pre_norm_ffn.4.'''			,     '''.output.dense.'''   )
       if ".transformer." in name:
              _lowerCamelCase				:  str						=		name.replace('''.transformer.'''			,     '''.transformer.layer.'''   )

       if ".aspp_layer." in name:
              _lowerCamelCase				:  Dict						=		name.replace('''.aspp_layer.'''			,     '''.'''   )
       if ".aspp_pool." in name:
              _lowerCamelCase				:  str						=		name.replace('''.aspp_pool.'''			,     '''.'''   )
       if "seg_head." in name:
              _lowerCamelCase				:  str						=		name.replace('''seg_head.'''			,     '''segmentation_head.'''   )
       if "segmentation_head.classifier.classifier." in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''segmentation_head.classifier.classifier.'''			,     '''segmentation_head.classifier.'''   )

       if "classifier.fc." in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''classifier.fc.'''			,     '''classifier.'''   )
       elif (not base_model) and ("segmentation_head." not in name):
              _lowerCamelCase				:  List[str]						=		'''mobilevit.''' + name

       return name




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_=False   ):
       if base_model:
              _lowerCamelCase				:  Dict						=		''''''
       else:
              _lowerCamelCase				:  Optional[int]						=		'''mobilevit.'''

       for key in orig_state_dict.copy().keys():
              _lowerCamelCase				:  List[Any]						=		orig_state_dict.pop(lowercase_   )

              if key[:8] == "encoder.":
                     _lowerCamelCase				:  Optional[int]						=		key[8:]

              if "qkv" in key:
                     _lowerCamelCase				:  int						=		key.split('''.'''   )
                     _lowerCamelCase				:  List[Any]						=		int(key_split[0][6:]   ) - 1
                     _lowerCamelCase				:  Union[str, Any]						=		int(key_split[3]   )
                     _lowerCamelCase				:  str						=		model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""   )
                     _lowerCamelCase				:  Tuple						=		layer.transformer.layer[transformer_num].attention.attention.all_head_size
                     _lowerCamelCase				:  Any						=		(
                         F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
                     )
                     if "weight" in key:
                            _lowerCamelCase				:  Optional[Any]						=		val[:dim, :]
                            _lowerCamelCase				:  List[Any]						=		val[dim : dim * 2, :]
                            _lowerCamelCase				:  Optional[int]						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  Any						=		val[:dim]
                            _lowerCamelCase				:  Optional[int]						=		val[dim : dim * 2]
                            _lowerCamelCase				:  Optional[Any]						=		val[-dim:]
              else:
                     _lowerCamelCase				:  Optional[Any]						=		val

       return orig_state_dict




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  List[str]						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase				:  Any						=		Image.open(requests.get(lowercase_			,     stream=lowercase_   ).raw   )
       return im




@torch.no_grad()
def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_=False   ):
       _lowerCamelCase				:  List[Any]						=		get_mobilevit_config(lowercase_   )

       # load original state_dict
       _lowerCamelCase				:  Union[str, Any]						=		torch.load(lowercase_			,     map_location='''cpu'''   )

       # load 🤗 model
       if mobilevit_name.startswith('''deeplabv3_'''   ):
              _lowerCamelCase				:  Any						=		MobileViTForSemanticSegmentation(lowercase_   ).eval()
       else:
              _lowerCamelCase				:  List[str]						=		MobileViTForImageClassification(lowercase_   ).eval()

       _lowerCamelCase				:  str						=		convert_state_dict(lowercase_			,     lowercase_   )
       model.load_state_dict(lowercase_   )

       # Check outputs on an image, prepared by MobileViTImageProcessor
       _lowerCamelCase				:  Optional[int]						=		MobileViTImageProcessor(crop_size=config.image_size			,     size=config.image_size + 32   )
       _lowerCamelCase				:  Tuple						=		image_processor(images=prepare_img()			,     return_tensors='''pt'''   )
       _lowerCamelCase				:  List[str]						=		model(**lowercase_   )
       _lowerCamelCase				:  Optional[int]						=		outputs.logits

       if mobilevit_name.startswith('''deeplabv3_'''   ):
              assert logits.shape == (1, 21, 32, 32)

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     _lowerCamelCase				:  Optional[Any]						=		torch.tensor(
                         [
                             [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                             [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                             [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                         ]   )
              elif mobilevit_name == "deeplabv3_mobilevit_xs":
                     _lowerCamelCase				:  Any						=		torch.tensor(
                         [
                             [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                             [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                             [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                         ]   )
              elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                     _lowerCamelCase				:  Any						=		torch.tensor(
                         [
                             [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                             [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                             [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                         ]   )
              else:
                     raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""   )

              assert torch.allclose(logits[0, :3, :3, :3]			,     lowercase_			,     atol=1e-4   )
       else:
              assert logits.shape == (1, 10_00)

              if mobilevit_name == "mobilevit_s":
                     _lowerCamelCase				:  int						=		torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]   )
              elif mobilevit_name == "mobilevit_xs":
                     _lowerCamelCase				:  str						=		torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]   )
              elif mobilevit_name == "mobilevit_xxs":
                     _lowerCamelCase				:  Union[str, Any]						=		torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]   )
              else:
                     raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""   )

              assert torch.allclose(logits[0, :3]			,     lowercase_			,     atol=1e-4   )

       Path(lowercase_   ).mkdir(exist_ok=lowercase_   )
       print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""   )
       model.save_pretrained(lowercase_   )
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
       image_processor.save_pretrained(lowercase_   )

       if push_to_hub:
              _lowerCamelCase				:  str						=		{
                  '''mobilevit_s''': '''mobilevit-small''',
                  '''mobilevit_xs''': '''mobilevit-x-small''',
                  '''mobilevit_xxs''': '''mobilevit-xx-small''',
                  '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
                  '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
                  '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
              }

              print('''Pushing to the hub...'''   )
              _lowerCamelCase				:  List[Any]						=		model_mapping[mobilevit_name]
              image_processor.push_to_hub(lowercase_			,     organization='''apple'''   )
              model.push_to_hub(lowercase_			,     organization='''apple'''   )


if __name__ == "__main__":
       _lowerCamelCase      							=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--mobilevit_name',
           default='mobilevit_s',
           type=str,
           help=(
               'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
               ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
           ),
       )
       parser.add_argument(
           '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       _lowerCamelCase      							=   parser.parse_args()
       convert_movilevit_checkpoint(
           args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )








import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_lowerCamelCase      							=   '\\n\n'

_lowerCamelCase      							=   '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

_lowerCamelCase      							=   '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION	)
class       __A							(   datasets.Metric	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





             return datasets.MetricInfo(
                 description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
                     {
                         '''input_texts''': datasets.Value('''string'''),
                     })   ,				reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']   ,				)



      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__ = 16   ,				a__ = True   ,				a__=None):



             """simple docstring"""





             if device is not None:
                    assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
                    if device == "gpu":
                           _lowerCamelCase				:  List[Any]						=		'''cuda'''
             else:
                    _lowerCamelCase				:  Dict						=		'''cuda''' if torch.cuda.is_available() else '''cpu'''

             _lowerCamelCase				:  List[str]						=		AutoModelForCausalLM.from_pretrained(a__)
             _lowerCamelCase				:  Union[str, Any]						=		model.to(a__)

             _lowerCamelCase				:  Tuple						=		AutoTokenizer.from_pretrained(a__)

             # if batch_size > 1 (which generally leads to padding being required), and
             # if there is not an already assigned pad_token, assign an existing
             # special token to also be the padding token
             if tokenizer.pad_token is None and batch_size > 1:
                    _lowerCamelCase				:  Any						=		list(tokenizer.special_tokens_map_extended.values())
                    # check that the model already has at least one special token defined
                    assert (
                        len(a__) > 0
                    ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
                    # assign one of the special tokens to also be the pad token
                    tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]})

             if add_start_token:
                    # leave room for <BOS> token to be added:
                    assert (
                        tokenizer.bos_token is not None
                    ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length - 1
             else:
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length

             _lowerCamelCase				:  Union[str, Any]						=		tokenizer(
                 a__   ,				add_special_tokens=a__   ,				padding=a__   ,				truncation=a__   ,				max_length=a__   ,				return_tensors='''pt'''   ,				return_attention_mask=a__   ,				).to(a__)

             _lowerCamelCase				:  Any						=		encodings['''input_ids''']
             _lowerCamelCase				:  Dict						=		encodings['''attention_mask''']

             # check that each input is long enough:
             if add_start_token:
                    assert torch.all(torch.ge(attn_masks.sum(1)   ,				1)), "Each input text must be at least one token long."
             else:
                    assert torch.all(
                        torch.ge(attn_masks.sum(1)   ,				2)), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

             _lowerCamelCase				:  Dict						=		[]
             _lowerCamelCase				:  Union[str, Any]						=		CrossEntropyLoss(reduction='''none''')

             for start_index in logging.tqdm(range(0   ,				len(a__)   ,				a__)):
                    _lowerCamelCase				:  Any						=		min(start_index + batch_size   ,				len(a__))
                    _lowerCamelCase				:  List[Any]						=		encoded_texts[start_index:end_index]
                    _lowerCamelCase				:  Optional[int]						=		attn_masks[start_index:end_index]

                    if add_start_token:
                           _lowerCamelCase				:  Tuple						=		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(a__)
                           _lowerCamelCase				:  str						=		torch.cat([bos_tokens_tensor, encoded_batch]   ,				dim=1)
                           _lowerCamelCase				:  str						=		torch.cat(
                               [torch.ones(bos_tokens_tensor.size()   ,				dtype=torch.intaa).to(a__), attn_mask]   ,				dim=1)

                    _lowerCamelCase				:  Dict						=		encoded_batch

                    with torch.no_grad():
                           _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__).logits

                    _lowerCamelCase				:  List[str]						=		out_logits[..., :-1, :].contiguous()
                    _lowerCamelCase				:  Dict						=		labels[..., 1:].contiguous()
                    _lowerCamelCase				:  List[Any]						=		attn_mask[..., 1:].contiguous()

                    _lowerCamelCase				:  str						=		torch.expa(
                        (loss_fct(shift_logits.transpose(1   ,				2)   ,				a__) * shift_attention_mask_batch).sum(1)
                        / shift_attention_mask_batch.sum(1))

                    ppls += perplexity_batch.tolist()

             return {"perplexities": ppls, "mean_perplexity": np.mean(a__)}

