
"""simple docstring"""
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 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       TimesformerConfig()

						if "large" in model_name:
												__lowerCAmelCase        =       96

						if "hr" in model_name:
												__lowerCAmelCase        =       16
												__lowerCAmelCase        =       448

						__lowerCAmelCase        =       """huggingface/label-files"""
						if "k400" in model_name:
												__lowerCAmelCase        =       400
												__lowerCAmelCase        =       """kinetics400-id2label.json"""
						elif "k600" in model_name:
												__lowerCAmelCase        =       600
												__lowerCAmelCase        =       """kinetics600-id2label.json"""
						elif "ssv2" in model_name:
												__lowerCAmelCase        =       174
												__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 				lowercase      (_lowerCAmelCase		):
						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 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						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 				lowercase      ():
						__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 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						__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, 400]		)
												__lowerCAmelCase        =       torch.tensor([-0.3_016, -0.7_713, -0.4_205]		)
						elif model_name == "timesformer-base-finetuned-k600":
												__lowerCAmelCase        =       torch.Size([1, 600]		)
												__lowerCAmelCase        =       torch.tensor([-0.7_267, -0.7_466, 3.2_404]		)
						elif model_name == "timesformer-base-finetuned-ssv2":
												__lowerCAmelCase        =       torch.Size([1, 174]		)
												__lowerCAmelCase        =       torch.tensor([-0.9_059, 0.6_433, -3.1_457]		)
						elif model_name == "timesformer-large-finetuned-k400":
												__lowerCAmelCase        =       torch.Size([1, 400]		)
												__lowerCAmelCase        =       torch.tensor([0, 0, 0]		)
						elif model_name == "timesformer-large-finetuned-k600":
												__lowerCAmelCase        =       torch.Size([1, 600]		)
												__lowerCAmelCase        =       torch.tensor([0, 0, 0]		)
						elif model_name == "timesformer-large-finetuned-ssv2":
												__lowerCAmelCase        =       torch.Size([1, 174]		)
												__lowerCAmelCase        =       torch.tensor([0, 0, 0]		)
						elif model_name == "timesformer-hr-finetuned-k400":
												__lowerCAmelCase        =       torch.Size([1, 400]		)
												__lowerCAmelCase        =       torch.tensor([-0.9_617, -3.7_311, -3.7_708]		)
						elif model_name == "timesformer-hr-finetuned-k600":
												__lowerCAmelCase        =       torch.Size([1, 600]		)
												__lowerCAmelCase        =       torch.tensor([2.5_273, 0.7_127, 1.8_848]		)
						elif model_name == "timesformer-hr-finetuned-ssv2":
												__lowerCAmelCase        =       torch.Size([1, 174]		)
												__lowerCAmelCase        =       torch.tensor([-3.6_756, -0.7_513, 0.7_180]		)
						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_			      =					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_			      =					parser.parse_args()
			convert_timesformer_checkpoint(
			    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
			)


"""simple docstring"""
import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
			import torch
class      lowerCAmelCase_						(							unittest.TestCase							,							A__				):



							'''simple docstring'''





							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       load_tool("""speech-to-text"""							)
													self.tool.setup()




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       self.tool(torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)




							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.tool(audio=torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)
