'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=13	,   __lowerCAmelCase=10	,   __lowerCAmelCase=3	,   __lowerCAmelCase=2	,   __lowerCAmelCase=2	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=32	,   __lowerCAmelCase=5	,   __lowerCAmelCase=4	,   __lowerCAmelCase=37	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=10	,   __lowerCAmelCase=0.0_2	,   __lowerCAmelCase="divided_space_time"	,   __lowerCAmelCase=None	,   )  ->  Union[str, Any]:
        lowercase__				:    Any  =					parent
        lowercase__				:    int  =					batch_size
        lowercase__				:    Any  =					image_size
        lowercase__				:    Dict  =					num_channels
        lowercase__				:    Optional[Any]  =					patch_size
        lowercase__				:    int  =					num_frames
        lowercase__				:    Dict  =					is_training
        lowercase__				:    Optional[int]  =					use_labels
        lowercase__				:    Optional[Any]  =					hidden_size
        lowercase__				:    Union[str, Any]  =					num_hidden_layers
        lowercase__				:    List[Any]  =					num_attention_heads
        lowercase__				:    Optional[int]  =					intermediate_size
        lowercase__				:    Optional[Any]  =					hidden_act
        lowercase__				:    List[str]  =					hidden_dropout_prob
        lowercase__				:    Any  =					attention_probs_dropout_prob
        lowercase__				:    Tuple  =					attention_type
        lowercase__				:    Tuple  =					initializer_range
        lowercase__				:    str  =					scope
        lowercase__				:    Tuple  =					num_labels

        # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
        lowercase__				:    List[str]  =					(image_size // patch_size) ** 2
        lowercase__				:    List[str]  =					(num_frames) * self.num_patches_per_frame + 1

       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    int  =					floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )

        lowercase__				:    List[Any]  =					None
        if self.use_labels:
         lowercase__				:    List[Any]  =					ids_tensor([self.batch_size]	,   self.num_labels )

        lowercase__				:    str  =					self.get_config()

        return config, pixel_values, labels

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Tuple  =					TimesformerConfig(
            image_size=self.image_size	,   patch_size=self.patch_size	,   num_channels=self.num_channels	,   num_frames=self.num_frames	,   hidden_size=self.hidden_size	,   num_hidden_layers=self.num_hidden_layers	,   num_attention_heads=self.num_attention_heads	,   intermediate_size=self.intermediate_size	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   initializer_range=self.initializer_range	,   attention_type=self.attention_type	,   )
        lowercase__				:    List[str]  =					self.num_labels
        return config

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Tuple  =					TimesformerModel(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    List[Any]  =					model(__lowerCAmelCase )
        self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    List[Any]  =					TimesformerForVideoClassification(__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()

        lowercase__				:    Any  =					model(__lowerCAmelCase )

        # verify the logits shape
        lowercase__				:    Optional[Any]  =					torch.Size((self.batch_size, self.num_labels) )
        self.parent.assertEqual(result.logits.shape	,   __lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Tuple  =					self.prepare_config_and_inputs()
        lowercase__ ,							lowercase__ ,							lowercase__				:    Any  =					config_and_inputs
        lowercase__				:    List[Any]  =					{'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class 			UpperCAmelCase  (						a__    ,						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE							=    (
           {"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
           if is_torch_available()
           else {}
       )

       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    str  =					TimesformerModelTester(self )
        lowercase__				:    Optional[int]  =					ConfigTester(
            self	,   config_class=__lowerCAmelCase	,   has_text_modality=__lowerCAmelCase	,   hidden_size=37 )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=False )  ->  str:
        lowercase__				:    Any  =					copy.deepcopy(__lowerCAmelCase )

        if return_labels:
         if model_class in get_values(__lowerCAmelCase ):
          lowercase__				:    List[str]  =					torch.zeros(
              self.model_tester.batch_size	,   dtype=torch.long	,   device=__lowerCAmelCase )

        return inputs_dict

       def _lowerCAmelCase(						self )  ->  Any:
        self.config_tester.run_common_tests()

       @unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        pass

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__ ,							lowercase__				:    List[Any]  =					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__				:    Optional[Any]  =					model_class(__lowerCAmelCase )
         self.assertIsInstance(model.get_input_embeddings()	,   (nn.Module) )
         lowercase__				:    List[str]  =					model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__lowerCAmelCase	,   nn.Linear ) )

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__ ,							lowercase__				:    Any  =					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__				:    int  =					model_class(__lowerCAmelCase )
         lowercase__				:    Any  =					inspect.signature(model.forward )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowercase__				:    Tuple  =					[*signature.parameters.keys()]

         lowercase__				:    List[Any]  =					['''pixel_values''']
         self.assertListEqual(arg_names[:1]	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Dict  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Tuple  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_video_classification(*__lowerCAmelCase )

       @slow
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__				:    List[str]  =					TimesformerModel.from_pretrained(__lowerCAmelCase )
         self.assertIsNotNone(__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Tuple:
        if not self.has_attentions:
         pass

        else:
         lowercase__ ,							lowercase__				:    Tuple  =					self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__				:    Optional[int]  =					True

         for model_class in self.all_model_classes:
          lowercase__				:    int  =					self.model_tester.seq_length
          lowercase__				:    List[str]  =					self.model_tester.num_frames

          lowercase__				:    Optional[int]  =					True
          lowercase__				:    int  =					False
          lowercase__				:    Tuple  =					True
          lowercase__				:    Dict  =					model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
           lowercase__				:    int  =					model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase ) )
          lowercase__				:    Dict  =					outputs.attentions
          self.assertEqual(len(__lowerCAmelCase )	,   self.model_tester.num_hidden_layers )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          lowercase__				:    Tuple  =					True
          lowercase__				:    Any  =					model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
           lowercase__				:    Optional[Any]  =					model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase ) )
          lowercase__				:    Any  =					outputs.attentions
          self.assertEqual(len(__lowerCAmelCase )	,   self.model_tester.num_hidden_layers )

          # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
          self.assertListEqual(
              list(attentions[0].shape[-3:] )	,   [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]	,   )
          lowercase__				:    Dict  =					len(__lowerCAmelCase )

          # Check attention is always last and order is fine
          lowercase__				:    Dict  =					True
          lowercase__				:    int  =					True
          lowercase__				:    Optional[int]  =					model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
           lowercase__				:    List[Any]  =					model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase ) )

          self.assertEqual(out_len + 1	,   len(__lowerCAmelCase ) )

          lowercase__				:    List[str]  =					outputs.attentions

          self.assertEqual(len(__lowerCAmelCase )	,   self.model_tester.num_hidden_layers )

          # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
          self.assertListEqual(
              list(self_attentions[0].shape[-3:] )	,   [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]	,   )


       def _lowerCAmelCase(						self )  ->  List[Any]:
        def check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase ):
         lowercase__				:    int  =					model_class(__lowerCAmelCase )
         model.to(__lowerCAmelCase )
         model.eval()

         with torch.no_grad():
          lowercase__				:    List[str]  =					model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase ) )

         lowercase__				:    Optional[Any]  =					outputs.hidden_states
         lowercase__				:    Union[str, Any]  =					self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(__lowerCAmelCase )	,   __lowerCAmelCase )

         lowercase__				:    Dict  =					self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[0].shape[-2:] )	,   [seq_length, self.model_tester.hidden_size]	,   )

        lowercase__ ,							lowercase__				:    Optional[Any]  =					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__				:    Optional[int]  =					True
         check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowercase__				:    Union[str, Any]  =					True

         check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )


def 			__UpperCamelCase       (   ):
 lowercase__				:    Dict  =					hf_hub_download(
     repo_id='''hf-internal-testing/spaghetti-video'''		,     filename='''eating_spaghetti.npy'''		,     repo_type='''dataset'''     )
 lowercase__				:    int  =					np.load(UpperCAmelCase     )
 return list(UpperCAmelCase     )





@require_torch
@require_vision
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @cached_property
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        # logits were tested with a different mean and std, so we use the same here
        return (
            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,   image_std=[0.5, 0.5, 0.5] )
            if is_vision_available()
            else None
        )


       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    str  =					TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
            __lowerCAmelCase )

        lowercase__				:    Tuple  =					self.default_image_processor
        lowercase__				:    str  =					prepare_video()
        lowercase__				:    Optional[int]  =					image_processor(video[:8]	,   return_tensors='''pt''' ).to(__lowerCAmelCase )

        # forward pass
        with torch.no_grad():
         lowercase__				:    Optional[Any]  =					model(**__lowerCAmelCase )

        # verify the logits
        lowercase__				:    Tuple  =					torch.Size((1, 400) )
        self.assertEqual(outputs.logits.shape	,   __lowerCAmelCase )

        lowercase__				:    Any  =					torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(__lowerCAmelCase )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]	,   __lowerCAmelCase	,   atol=1E-4 ) )


'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					LongformerForQuestionAnswering.from_pretrained(UpperCAmelCase     )

 # transfer weights
 longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()     )
 longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()     )
 longformer_for_qa.eval()

 # save model
 longformer_for_qa.save_pretrained(UpperCAmelCase     )

 print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""     )


if __name__ == "__main__":
  __a: List[Any]          =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

