




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor


def      lowerCAmelCase_     (					lowerCamelCase:    Any  )   ->     str:


      '''simple docstring'''




      lowerCamelCase__ 					=			TimesformerConfig()

      if "large" in model_name:
            lowerCamelCase__ 					=			96

      if "hr" in model_name:
            lowerCamelCase__ 					=			16
            lowerCamelCase__ 					=			4_48

      lowerCamelCase__ 					=			'huggingface/label-files'
      if "k400" in model_name:
            lowerCamelCase__ 					=			4_00
            lowerCamelCase__ 					=			'kinetics400-id2label.json'
      elif "k600" in model_name:
            lowerCamelCase__ 					=			6_00
            lowerCamelCase__ 					=			'kinetics600-id2label.json'
      elif "ssv2" in model_name:
            lowerCamelCase__ 					=			1_74
            lowerCamelCase__ 					=			'something-something-v2-id2label.json'
      else:
            raise ValueError('Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'  )
      lowerCamelCase__ 					=			json.load(open(hf_hub_download(lowerCamelCase       ,			lowerCamelCase       ,			repo_type='dataset'  )       ,			'r'  )  )
      lowerCamelCase__ 					=			{int(lowerCamelCase  ): v for k, v in idalabel.items()}
      lowerCamelCase__ 					=			idalabel
      lowerCamelCase__ 					=			{v: k for k, v in idalabel.items()}

      return config


def      lowerCAmelCase_     (					lowerCamelCase:    Dict  )   ->     Optional[int]:


      '''simple docstring'''




      if "encoder." in name:
            lowerCamelCase__ 					=			name.replace('encoder.'       ,			''  )
      if "cls_token" in name:
            lowerCamelCase__ 					=			name.replace('cls_token'       ,			'timesformer.embeddings.cls_token'  )
      if "pos_embed" in name:
            lowerCamelCase__ 					=			name.replace('pos_embed'       ,			'timesformer.embeddings.position_embeddings'  )
      if "time_embed" in name:
            lowerCamelCase__ 					=			name.replace('time_embed'       ,			'timesformer.embeddings.time_embeddings'  )
      if "patch_embed.proj" in name:
            lowerCamelCase__ 					=			name.replace('patch_embed.proj'       ,			'timesformer.embeddings.patch_embeddings.projection'  )
      if "patch_embed.norm" in name:
            lowerCamelCase__ 					=			name.replace('patch_embed.norm'       ,			'timesformer.embeddings.norm'  )
      if "blocks" in name:
            lowerCamelCase__ 					=			name.replace('blocks'       ,			'timesformer.encoder.layer'  )
      if "attn.proj" in name:
            lowerCamelCase__ 					=			name.replace('attn.proj'       ,			'attention.output.dense'  )
      if "attn" in name and "bias" not in name and "temporal" not in name:
            lowerCamelCase__ 					=			name.replace('attn'       ,			'attention.self'  )
      if "attn" in name and "temporal" not in name:
            lowerCamelCase__ 					=			name.replace('attn'       ,			'attention.attention'  )
      if "temporal_norm1" in name:
            lowerCamelCase__ 					=			name.replace('temporal_norm1'       ,			'temporal_layernorm'  )
      if "temporal_attn.proj" in name:
            lowerCamelCase__ 					=			name.replace('temporal_attn'       ,			'temporal_attention.output.dense'  )
      if "temporal_fc" in name:
            lowerCamelCase__ 					=			name.replace('temporal_fc'       ,			'temporal_dense'  )
      if "norm1" in name and "temporal" not in name:
            lowerCamelCase__ 					=			name.replace('norm1'       ,			'layernorm_before'  )
      if "norm2" in name:
            lowerCamelCase__ 					=			name.replace('norm2'       ,			'layernorm_after'  )
      if "mlp.fc1" in name:
            lowerCamelCase__ 					=			name.replace('mlp.fc1'       ,			'intermediate.dense'  )
      if "mlp.fc2" in name:
            lowerCamelCase__ 					=			name.replace('mlp.fc2'       ,			'output.dense'  )
      if "norm.weight" in name and "fc" not in name and "temporal" not in name:
            lowerCamelCase__ 					=			name.replace('norm.weight'       ,			'timesformer.layernorm.weight'  )
      if "norm.bias" in name and "fc" not in name and "temporal" not in name:
            lowerCamelCase__ 					=			name.replace('norm.bias'       ,			'timesformer.layernorm.bias'  )
      if "head" in name:
            lowerCamelCase__ 					=			name.replace('head'       ,			'classifier'  )

      return name


def      lowerCAmelCase_     (					lowerCamelCase:    int       ,			lowerCamelCase:    Optional[Any]  )   ->     List[Any]:


      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCamelCase__ 					=			orig_state_dict.pop(lowerCamelCase  )

            if key.startswith('model.'  ):
                  lowerCamelCase__ 					=			key.replace('model.'       ,			''  )

            if "qkv" in key:
                  lowerCamelCase__ 					=			key.split('.'  )
                  lowerCamelCase__ 					=			int(key_split[1]  )
                  lowerCamelCase__ 					=			'timesformer.encoder.layer.'
                  if "temporal" in key:
                        lowerCamelCase__ 					=			'.temporal_attention.attention.qkv.'
                  else:
                        lowerCamelCase__ 					=			'.attention.attention.qkv.'
                  if "weight" in key:
                        lowerCamelCase__ 					=			val
                  else:
                        lowerCamelCase__ 					=			val
            else:
                  lowerCamelCase__ 					=			val

      return orig_state_dict


def      lowerCAmelCase_     (					)   ->     Optional[int]:


      '''simple docstring'''




      lowerCamelCase__ 					=			hf_hub_download(
          repo_id='hf-internal-testing/spaghetti-video'       ,			filename='eating_spaghetti.npy'       ,			repo_type='dataset'  )
      lowerCamelCase__ 					=			np.load(lowerCamelCase  )
      return list(lowerCamelCase  )


def      lowerCAmelCase_     (					lowerCamelCase:    Dict       ,			lowerCamelCase:    List[Any]       ,			lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    Optional[int]  )   ->     Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			get_timesformer_config(lowerCamelCase  )

      lowerCamelCase__ 					=			TimesformerForVideoClassification(lowerCamelCase  )

      # download original checkpoint, hosted on Google Drive
      lowerCamelCase__ 					=			'pytorch_model.bin'
      gdown.cached_download(lowerCamelCase       ,			lowerCamelCase       ,			quiet=lowerCamelCase  )
      lowerCamelCase__ 					=			torch.load(lowerCamelCase       ,			map_location='cpu'  )
      if "model" in files:
            lowerCamelCase__ 					=			files['model']
      elif "module" in files:
            lowerCamelCase__ 					=			files['module']
      else:
            lowerCamelCase__ 					=			files['model_state']
      lowerCamelCase__ 					=			convert_state_dict(lowerCamelCase       ,			lowerCamelCase  )

      model.load_state_dict(lowerCamelCase  )
      model.eval()

      # verify model on basic input
      lowerCamelCase__ 					=			VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]       ,			image_std=[0.5, 0.5, 0.5]  )
      lowerCamelCase__ 					=			prepare_video()
      lowerCamelCase__ 					=			image_processor(video[:8]       ,			return_tensors='pt'  )

      lowerCamelCase__ 					=			model(**lowerCamelCase  )
      lowerCamelCase__ 					=			outputs.logits

      lowerCamelCase__ 					=			[
          # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
          'timesformer-base-finetuned-k400',
          'timesformer-large-finetuned-k400',
          'timesformer-hr-finetuned-k400',
          # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
          'timesformer-base-finetuned-k600',
          'timesformer-large-finetuned-k600',
          'timesformer-hr-finetuned-k600',
          # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
          'timesformer-base-finetuned-ssv2',
          'timesformer-large-finetuned-ssv2',
          'timesformer-hr-finetuned-ssv2',
      ]

      # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
      if model_name == "timesformer-base-finetuned-k400":
            lowerCamelCase__ 					=			torch.Size([1, 4_00]  )
            lowerCamelCase__ 					=			torch.tensor([-0.3016, -0.7713, -0.4205]  )
      elif model_name == "timesformer-base-finetuned-k600":
            lowerCamelCase__ 					=			torch.Size([1, 6_00]  )
            lowerCamelCase__ 					=			torch.tensor([-0.7267, -0.7466, 3.2404]  )
      elif model_name == "timesformer-base-finetuned-ssv2":
            lowerCamelCase__ 					=			torch.Size([1, 1_74]  )
            lowerCamelCase__ 					=			torch.tensor([-0.9059, 0.6433, -3.1457]  )
      elif model_name == "timesformer-large-finetuned-k400":
            lowerCamelCase__ 					=			torch.Size([1, 4_00]  )
            lowerCamelCase__ 					=			torch.tensor([0, 0, 0]  )
      elif model_name == "timesformer-large-finetuned-k600":
            lowerCamelCase__ 					=			torch.Size([1, 6_00]  )
            lowerCamelCase__ 					=			torch.tensor([0, 0, 0]  )
      elif model_name == "timesformer-large-finetuned-ssv2":
            lowerCamelCase__ 					=			torch.Size([1, 1_74]  )
            lowerCamelCase__ 					=			torch.tensor([0, 0, 0]  )
      elif model_name == "timesformer-hr-finetuned-k400":
            lowerCamelCase__ 					=			torch.Size([1, 4_00]  )
            lowerCamelCase__ 					=			torch.tensor([-0.9617, -3.7311, -3.7708]  )
      elif model_name == "timesformer-hr-finetuned-k600":
            lowerCamelCase__ 					=			torch.Size([1, 6_00]  )
            lowerCamelCase__ 					=			torch.tensor([2.5273, 0.7127, 1.8848]  )
      elif model_name == "timesformer-hr-finetuned-ssv2":
            lowerCamelCase__ 					=			torch.Size([1, 1_74]  )
            lowerCamelCase__ 					=			torch.tensor([-3.6756, -0.7513, 0.7180]  )
      else:
            raise ValueError(f"""Model name not supported. Should be one of {model_names}"""  )

      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3]       ,			lowerCamelCase       ,			atol=1e-4  )
      print('Logits ok!'  )

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""  )
            image_processor.save_pretrained(lowerCamelCase  )
            model.save_pretrained(lowerCamelCase  )

      if push_to_hub:
            print('Pushing to the hub...'  )
            model.push_to_hub(f"""fcakyon/{model_name}"""  )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE      :				List[str]				      =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--checkpoint_url""",
          default="""https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download""",
          type=str,
          help=(
              """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
              """ download link."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default="""""",
          type=str,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument("""--model_name""", default="""timesformer-base-finetuned-k400""", type=str, help="""Name of the model.""")
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      __SCREAMING_SNAKE_CASE      :				Tuple				      =parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


__SCREAMING_SNAKE_CASE      :				Union[str, Any]				      =-1 if torch_device == """cpu""" else 0






@is_pipeline_test
class      A     (						unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Tuple       =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _snake_case:		int       =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




     def 	__snake_case				(      self					:				Any		,					A__					:				str		,					A__					:				int		,					A__					:				Dict     )			->    Any:
           lowerCamelCase__ 					=			SummarizationPipeline(model=A__		,					tokenizer=A__     )
           return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]




     def 	__snake_case				(      self					:				int		,					A__					:				int		,					A__					:				Tuple     )			->    Tuple:
           lowerCamelCase__ 					=			summarizer.model

           lowerCamelCase__ 					=			summarizer('(CNN)The Palestinian Authority officially became'     )
           self.assertEqual(A__		,					[{'summary_text': ANY(A__     )}]     )

           lowerCamelCase__ 					=			summarizer(
               '(CNN)The Palestinian Authority officially became '		,					num_beams=2		,					min_length=2		,					max_length=5		,					)
           self.assertEqual(A__		,					[{'summary_text': ANY(A__     )}]     )

           # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
           lowerCamelCase__ 					=			[
               'SwitchTransformersConfig',
               'T5Config',
               'LongT5Config',
               'LEDConfig',
               'PegasusXConfig',
               'FSMTConfig',
               'M2M100Config',
               'ProphetNetConfig',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
           ]
           if model.config.__class__.__name__ not in model_can_handle_longer_seq:
                 # Too long and exception is expected.
                 # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
                 # the embedding layer.
                 if not (
                     isinstance(A__		,					A__     )
                     and get_gpu_count() > 0
                     and len(summarizer.model.trainable_weights     ) > 0
                 ):
                       with self.assertRaises(A__     ):
                             lowerCamelCase__ 					=			summarizer('This ' * 1_0_0_0     )
           lowerCamelCase__ 					=			summarizer('This ' * 1_0_0_0		,					truncation=TruncationStrategy.ONLY_FIRST     )




     @require_torch
     def 	__snake_case				(      self					:				str     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			pipeline(task='summarization'		,					model='sshleifer/tiny-mbart'		,					framework='pt'     )
           lowerCamelCase__ 					=			summarizer('This is a small test'     )
           self.assertEqual(
               A__		,					[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]		,					)




     @require_tf
     def 	__snake_case				(      self					:				List[str]     )			->    List[str]:
           lowerCamelCase__ 					=			pipeline(task='summarization'		,					model='sshleifer/tiny-mbart'		,					framework='tf'     )
           lowerCamelCase__ 					=			summarizer('This is a small test'     )
           self.assertEqual(
               A__		,					[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]		,					)




     @require_torch
     @slow
     def 	__snake_case				(      self					:				List[str]     )			->    Tuple:
           lowerCamelCase__ 					=			pipeline(task='summarization'		,					device=A__     )
           lowerCamelCase__ 					=			(
               ' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'
               ' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'
               ' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'
               ' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'
               ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
               ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
               ' situation in Palestinian territories, paving the way for possible war crimes investigations against'
               ' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'
               ' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'
               ' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'
               ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
               ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
               ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
               ' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'
               ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
               ' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'
               ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
               ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
               ' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'
               ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
               ' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'
               ' decision to join a treaty to which over 100 countries around the world are members." In January, when'
               ' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'
               ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
               ' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'
               ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
               ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
               ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
               ' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'
               ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
               ' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'
               ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
               ' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'
               ' will include alleged war crimes committed since June. The International Criminal Court was set up in'
               ' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'
               ' and Faith Karimi contributed to this report.'
           )
           lowerCamelCase__ 					=			(
               ' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'
               ' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'
               ' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'
               ' says governments seeking to penalize Palestine should end pressure .'
           )
           lowerCamelCase__ 					=			summarizer(A__     )
           self.assertEqual(result[0]['summary_text']		,					A__     )