





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.eager import context
		from tensorflow.python.framework import ops

		from transformers import GradientAccumulator, create_optimizer






@require_tf
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   Dict , lowerCamelCase			:   List[Any] , lowerCamelCase			:   str , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				self.assertEqual(len(a_		) , len(a_		)		)
				for a, b in zip(a_ , a_		):
							self.assertAlmostEqual(a_ , a_ , delta=a_		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = GradientAccumulator()
				accumulator([tf.constant([1.0, 2.0]		)]		)
				accumulator([tf.constant([-2.0, 1.0]		)]		)
				accumulator([tf.constant([-1.0, 2.0]		)]		)
				with self.assertRaises(a_		):
							accumulator([tf.constant([1.0, 1.0]		), tf.constant([2.0, 2.0]		)]		)
				self.assertEqual(accumulator.step , 3		)
				self.assertEqual(len(accumulator.gradients		) , 1		)
				self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2		)
				accumulator.reset()
				self.assertEqual(accumulator.step , 0		)
				self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = None
				ops.enable_eager_execution_internal()
				a__     = tf.config.list_physical_devices("CPU"		)
				if len(a_		) == 1:
							tf.config.set_logical_device_configuration(
							    physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
				a__     = tf.config.list_logical_devices(device_type="CPU"		)
				a__     = tf.distribute.MirroredStrategy(devices=devices[:2]		)

				with strategy.scope():
							a__     = GradientAccumulator()
							a__     = tf.Variable([4.0, 3.0]		)
							a__     = create_optimizer(5e-5 , 1_0 , 5		)
							a__     = tf.Variable([0.0, 0.0] , trainable=a_		)

				def accumulate_on_replica(lowerCamelCase			:   Any		):
							accumulator([gradient]		)

				def apply_on_replica():
							optimizer.apply_gradients(list(zip(accumulator.gradients , [variable]		)		)		)

				@tf.function
				def accumulate(lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   str		):
							with strategy.scope():
										a__     = strategy.experimental_local_results(a_		)
										local_variables[0].assign(a_		)
										local_variables[1].assign(a_		)
										strategy.run(a_ , args=(gradient_placeholder,)		)

				@tf.function
				def apply_grad():
							with strategy.scope():
										strategy.run(a_		)

				def _check_local_values(lowerCamelCase			:   str , lowerCamelCase			:   Optional[int]		):
							a__     = strategy.experimental_local_results(accumulator._gradients[0]		)
							self.assertListAlmostEqual(values[0].value() , a_ , tol=1e-2		)
							self.assertListAlmostEqual(values[1].value() , a_ , tol=1e-2		)

				accumulate([1.0, 2.0] , [-1.0, 1.0]		)
				accumulate([3.0, -1.0] , [-1.0, -1.0]		)
				accumulate([-2.0, 2.0] , [3.0, -2.0]		)
				self.assertEqual(accumulator.step , 3		)
				_check_local_values([2.0, 3.0] , [1.0, -2.0]		)
				apply_grad()
				self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2		)
				accumulator.reset()
				self.assertEqual(accumulator.step , 0		)
				_check_local_values([0.0, 0.0] , [0.0, 0.0]		)










'''simple docstring'''


import argparse

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

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Dict     )   ->    Tuple:
			a__     = XCLIPTextConfig()

			# derive patch size from model name
			a__     = model_name.find("patch"     )
			a__     = int(model_name[start_idx + len("patch"     ) : start_idx + len("patch"     ) + 2]     )
			a__     = XCLIPVisionConfig(patch_size=__lowerCamelCase					,  num_frames=__lowerCamelCase     )

			if "large" in model_name:
						a__     = 768
						a__     = 3072
						a__     = 12

						a__     = 1024
						a__     = 4096
						a__     = 16
						a__     = 24
						a__     = 768
						a__     = 3072

			if model_name == "xclip-large-patch14-16-frames":
						a__     = 336

			a__     = XCLIPConfig.from_text_vision_configs(__lowerCamelCase					,  __lowerCamelCase     )

			if "large" in model_name:
						a__     = 768

			return config







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]     )   ->    List[str]:
			# text encoder
			if name == "token_embedding.weight":
						a__     = name.replace("token_embedding.weight"					,  "text_model.embeddings.token_embedding.weight"     )
			if name == "positional_embedding":
						a__     = name.replace("positional_embedding"					,  "text_model.embeddings.position_embedding.weight"     )
			if "ln_1" in name:
						a__     = name.replace("ln_1"					,  "layer_norm1"     )
			if "ln_2" in name:
						a__     = name.replace("ln_2"					,  "layer_norm2"     )
			if "c_fc" in name:
						a__     = name.replace("c_fc"					,  "fc1"     )
			if "c_proj" in name:
						a__     = name.replace("c_proj"					,  "fc2"     )
			if name.startswith("transformer.resblocks"     ):
						a__     = name.replace("transformer.resblocks"					,  "text_model.encoder.layers"     )
			if "attn.out_proj" in name and "message" not in name:
						a__     = name.replace("attn.out_proj"					,  "self_attn.out_proj"     )
			if "ln_final" in name:
						a__     = name.replace("ln_final"					,  "text_model.final_layer_norm"     )
			# visual encoder
			if name == "visual.class_embedding":
						a__     = name.replace("visual.class_embedding"					,  "vision_model.embeddings.class_embedding"     )
			if name == "visual.positional_embedding":
						a__     = name.replace("visual.positional_embedding"					,  "vision_model.embeddings.position_embedding.weight"     )
			if name.startswith("visual.transformer.resblocks"     ):
						a__     = name.replace("visual.transformer.resblocks"					,  "vision_model.encoder.layers"     )
			if "visual.conv1" in name:
						a__     = name.replace("visual.conv1"					,  "vision_model.embeddings.patch_embedding"     )
			if "visual.ln_pre" in name:
						a__     = name.replace("visual.ln_pre"					,  "vision_model.pre_layernorm"     )
			if "visual.ln_post" in name:
						a__     = name.replace("visual.ln_post"					,  "vision_model.post_layernorm"     )
			if "visual.proj" in name:
						a__     = name.replace("visual.proj"					,  "visual_projection.weight"     )
			if "text_projection" in name:
						a__     = name.replace("text_projection"					,  "text_projection.weight"     )
			# things on top
			if "prompts_visual_proj" in name:
						a__     = name.replace("prompts_visual_proj"					,  "prompts_visual_projection"     )
			if "prompts_visual_ln" in name:
						a__     = name.replace("prompts_visual_ln"					,  "prompts_visual_layernorm"     )
			# mit
			if name == "mit.positional_embedding":
						a__     = name.replace("positional"					,  "position"     )
			if name.startswith("mit.resblocks"     ):
						a__     = name.replace("mit.resblocks"					,  "mit.encoder.layers"     )
			# prompts generator
			if name.startswith("prompts_generator.norm"     ):
						a__     = name.replace("prompts_generator.norm"					,  "prompts_generator.layernorm"     )

			return name







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     str     )   ->    Optional[Any]:
			for key in orig_state_dict.copy().keys():
						a__     = orig_state_dict.pop(__lowerCamelCase     )

						if "attn.in_proj" in key:
									a__     = key.split("."     )
									if key.startswith("visual"     ):
												a__     = key_split[3]
												a__     = config.vision_config.hidden_size
												if "message_attn" in key:
															if "weight" in key:
																		a__     = val[
																		    :dim, :
																		]
																		a__     = val[
																		    dim : dim * 2, :
																		]
																		a__     = val[
																		    -dim:, :
																		]
															else:
																		a__     = val[
																		    :dim
																		]
																		a__     = val[
																		    dim : dim * 2
																		]
																		a__     = val[
																		    -dim:
																		]
												else:
															if "weight" in key:
																		a__     = val[
																		    :dim, :
																		]
																		a__     = val[
																		    dim : dim * 2, :
																		]
																		a__     = val[
																		    -dim:, :
																		]
															else:
																		a__     = val[:dim]
																		a__     = val[
																		    dim : dim * 2
																		]
																		a__     = val[-dim:]
									elif key.startswith("mit"     ):
												a__     = key_split[2]
												a__     = config.vision_config.mit_hidden_size
												if "weight" in key:
															a__     = val[:dim, :]
															a__     = val[dim : dim * 2, :]
															a__     = val[-dim:, :]
												else:
															a__     = val[:dim]
															a__     = val[dim : dim * 2]
															a__     = val[-dim:]
									else:
												a__     = key_split[2]
												a__     = config.text_config.hidden_size
												if "weight" in key:
															a__     = val[:dim, :]
															a__     = val[
															    dim : dim * 2, :
															]
															a__     = val[-dim:, :]
												else:
															a__     = val[:dim]
															a__     = val[
															    dim : dim * 2
															]
															a__     = val[-dim:]
						else:
									a__     = rename_key(__lowerCamelCase     )
									if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
												a__     = val.T
									a__     = val

			return orig_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    List[str]:
			if num_frames == 8:
						a__     = "eating_spaghetti_8_frames.npy"
			elif num_frames == 16:
						a__     = "eating_spaghetti.npy"
			elif num_frames == 32:
						a__     = "eating_spaghetti_32_frames.npy"
			a__     = hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"					,  filename=__lowerCamelCase					,  repo_type="dataset"					,  )
			a__     = np.load(__lowerCamelCase     )
			return list(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Any=None					,  __lowerCamelCase  :     Optional[Any]=False     )   ->    Optional[Any]:
			a__     = {
			    # fully supervised kinetics-400 checkpoints
			    "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
			    "xclip-base-patch32-16-frames": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
			    ),
			    "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
			    "xclip-base-patch16-16-frames": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
			    ),
			    "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
			    "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
			    # fully supervised kinetics-600 checkpoints
			    "xclip-base-patch16-kinetics-600": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
			    ),
			    "xclip-base-patch16-kinetics-600-16-frames": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
			    ),
			    "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
			    # few shot
			    "xclip-base-patch16-hmdb-2-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
			    ),
			    "xclip-base-patch16-hmdb-4-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
			    ),
			    "xclip-base-patch16-hmdb-8-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
			    ),
			    "xclip-base-patch16-hmdb-16-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
			    ),
			    "xclip-base-patch16-ucf-2-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
			    ),
			    "xclip-base-patch16-ucf-4-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
			    ),
			    "xclip-base-patch16-ucf-8-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
			    ),
			    "xclip-base-patch16-ucf-16-shot": (
			        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
			    ),
			    # zero shot
			    "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
			}

			a__     = model_to_url[model_name]
			a__     = 8
			if "16-frames" in model_name:
						a__     = 16
			elif "shot" in model_name:
						a__     = 32

			a__     = get_xclip_config(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = XCLIPModel(__lowerCamelCase     )
			model.eval()

			if "drive" in checkpoint_url:
						a__     = "pytorch_model.bin"
						gdown.cached_download(__lowerCamelCase					,  __lowerCamelCase					,  quiet=__lowerCamelCase     )
						a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )["model"]
			else:
						a__     = torch.hub.load_state_dict_from_url(__lowerCamelCase     )["model"]

			a__     = convert_state_dict(__lowerCamelCase					,  __lowerCamelCase     )

			a__     = XCLIPModel(__lowerCamelCase     )
			a__   ,				a__     = model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
			model.eval()

			a__     = 336 if model_name == "xclip-large-patch14-16-frames" else 224
			a__     = VideoMAEImageProcessor(size=__lowerCamelCase     )
			a__     = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"     )
			a__     = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"     )
			a__     = XCLIPProcessor(image_processor=__lowerCamelCase					,  tokenizer=__lowerCamelCase     )

			a__     = prepare_video(__lowerCamelCase     )
			a__     = processor(
			    text=["playing sports", "eating spaghetti", "go shopping"]					,  videos=__lowerCamelCase					,  return_tensors="pt"					,  padding=__lowerCamelCase     )

			print("Shape of pixel values:"					,  inputs.pixel_values.shape     )

			with torch.no_grad():
						a__     = model(**__lowerCamelCase     )

			# Verify outputs
			a__     = outputs.logits_per_video
			a__     = logits_per_video.softmax(dim=1     )
			print("Probs:"					,  __lowerCamelCase     )
			# kinetics-400
			if model_name == "xclip-base-patch32":
						a__     = torch.tensor([[0.0_019, 0.9_951, 0.0_030]]     )
			elif model_name == "xclip-base-patch32-16-frames":
						a__     = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]     )
			elif model_name == "xclip-base-patch16":
						a__     = torch.tensor([[0.0_083, 0.9_681, 0.0_236]]     )
			elif model_name == "xclip-base-patch16-16-frames":
						a__     = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]     )
			elif model_name == "xclip-large-patch14":
						a__     = torch.tensor([[0.0_062, 0.9_864, 0.0_075]]     )
			elif model_name == "xclip-large-patch14-16-frames":
						a__     = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]     )
			# kinetics-600
			elif model_name == "xclip-base-patch16-kinetics-600":
						a__     = torch.tensor([[0.0_555, 0.8_914, 0.0_531]]     )
			elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
						a__     = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]     )
			elif model_name == "xclip-large-patch14-kinetics-600":
						a__     = torch.tensor([[0.0_036, 0.9_920, 0.0_045]]     )
			# few shot
			elif model_name == "xclip-base-patch16-hmdb-2-shot":
						a__     = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]     )
			elif model_name == "xclip-base-patch16-hmdb-4-shot":
						a__     = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]     )
			elif model_name == "xclip-base-patch16-hmdb-8-shot":
						a__     = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]     )
			elif model_name == "xclip-base-patch16-hmdb-16-shot":
						a__     = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]     )
			elif model_name == "xclip-base-patch16-ucf-2-shot":
						a__     = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]     )
			elif model_name == "xclip-base-patch16-ucf-4-shot":
						a__     = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]     )
			elif model_name == "xclip-base-patch16-ucf-8-shot":
						a__     = torch.tensor([[0.0_027, 0.9_904, 0.0_070]]     )
			elif model_name == "xclip-base-patch16-ucf-16-shot":
						a__     = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]     )
			# zero shot
			elif model_name == "xclip-base-patch16-zero-shot":
						a__     = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]     )
			else:
						raise ValueError(f'''Model name {model_name} not supported'''     )
			assert torch.allclose(__lowerCamelCase					,  __lowerCamelCase					,  atol=1e-3     )
			print("Looks ok!"     )

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''     )
						model.save_pretrained(__lowerCamelCase     )

			if push_to_hub:
						print("Pushing model, processor and slow tokenizer files to the hub..."     )
						model.push_to_hub(__lowerCamelCase					,  organization="nielsr"     )
						processor.push_to_hub(__lowerCamelCase					,  organization="nielsr"     )
						slow_tokenizer.push_to_hub(__lowerCamelCase					,  organization="nielsr"     )


if __name__ == "__main__":
		lowerCAmelCase_   :   Any       =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default="xclip-base-patch32",
		    type=str,
		    help="Name of the model.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, 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_   :   Dict       =					parser.parse_args()
		convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



