
'''simple docstring'''



import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Tuple:  # picklable for multiprocessing




			"""simple docstring"""
			return x.sum()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:  # picklable for multiprocessing




			"""simple docstring"""
			return i + 1

@dataclass
class    _a				:





							"""simple docstring"""




							SCREAMING_SNAKE_CASE      =  42
							SCREAMING_SNAKE_CASE      =  42






class    _a				(UpperCAmelCase__):





							"""simple docstring"""




							def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
										_SCREAMING_SNAKE_CASE      =				{}
										_SCREAMING_SNAKE_CASE      =				[]
										_SCREAMING_SNAKE_CASE      =				1
										_SCREAMING_SNAKE_CASE      =				[1, 2]
										_SCREAMING_SNAKE_CASE      =				{"""a""": 1, """b""": 2}
										_SCREAMING_SNAKE_CASE      =				{"""a""": [1, 2], """b""": [3, 4]}
										_SCREAMING_SNAKE_CASE      =				{"""a""": {"""1""": 1}, """b""": 2}
										_SCREAMING_SNAKE_CASE      =				{"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
										_SCREAMING_SNAKE_CASE      =				{}
										_SCREAMING_SNAKE_CASE      =				[]
										_SCREAMING_SNAKE_CASE      =				2
										_SCREAMING_SNAKE_CASE      =				[2, 3]
										_SCREAMING_SNAKE_CASE      =				{"""a""": 2, """b""": 3}
										_SCREAMING_SNAKE_CASE      =				{"""a""": [2, 3], """b""": [4, 5]}
										_SCREAMING_SNAKE_CASE      =				{"""a""": {"""1""": 2}, """b""": 3}
										_SCREAMING_SNAKE_CASE      =				{"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase      )       ,							__lowerCAmelCase      )

										_SCREAMING_SNAKE_CASE      =				2
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )

										_SCREAMING_SNAKE_CASE      =				{"""a""": np.eye(2      ), """b""": np.zeros(3      ), """c""": np.ones(2      )}
										_SCREAMING_SNAKE_CASE      =				{"""a""": 2, """b""": 0, """c""": 2}
										_SCREAMING_SNAKE_CASE      =				{
										    """a""": np.eye(2      ).astype(__lowerCAmelCase      ),
										    """b""": np.zeros(3      ).astype(__lowerCAmelCase      ),
										    """c""": np.ones(2      ).astype(__lowerCAmelCase      ),
										}
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							map_numpy=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(
										    {k: v.tolist() for k, v in map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							map_numpy=__lowerCAmelCase      ).items()}       ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,							)
										self.assertEqual(map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							map_numpy=__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )       ,							__lowerCAmelCase      )
										self.assertEqual(
										    {k: v.tolist() for k, v in map_nested(__lowerCAmelCase       ,							__lowerCAmelCase       ,							map_numpy=__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      ).items()}       ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,							)
										with self.assertRaises(__lowerCAmelCase      ):  # can't pickle a local lambda
													map_nested(lambda A__      : x + 1       ,							__lowerCAmelCase       ,							num_proc=__lowerCAmelCase      )






							def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
										_SCREAMING_SNAKE_CASE      =				{"""a""": 1, """b""": 2}
										_SCREAMING_SNAKE_CASE      =				{"""a""": 3, """b""": 4}
										_SCREAMING_SNAKE_CASE      =				{"""a""": 5, """b""": 6}
										_SCREAMING_SNAKE_CASE      =				sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]      )
										self.assertEqual(sorted(zip_dict(__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase      )      )       ,							__lowerCAmelCase      )






							def        UpperCamelCase				(							self      )    ->			Dict:

										class    _a				:





																	"""simple docstring"""




																	SCREAMING_SNAKE_CASE      =  'bar'






										_SCREAMING_SNAKE_CASE      =				Foo()
										self.assertEqual(foo.my_attr       ,							"""bar"""      )
										with temporary_assignment(__lowerCAmelCase       ,							"""my_attr"""       ,							"""BAR"""      ):
													self.assertEqual(foo.my_attr       ,							"""BAR"""      )
										self.assertEqual(foo.my_attr       ,							"""bar"""      )






@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""    ,      [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




			"""simple docstring"""

			with patch("""datasets.utils.py_utils._single_map_nested"""						) as mock_single_map_nested, patch(
			    """datasets.parallel.parallel.Pool"""						) as mock_multiprocessing_pool:
						_SCREAMING_SNAKE_CASE      =				{F"{i}": i for i in range(UpperCAmelCase__						)}
						_SCREAMING_SNAKE_CASE      =				map_nested(lambda SCREAMING_SNAKE_CASE_						: x + 10    ,      UpperCAmelCase__    ,      num_proc=UpperCAmelCase__    ,      parallel_min_length=16						)
						if expected_num_proc == 1:
									assert mock_single_map_nested.called
									assert not mock_multiprocessing_pool.called
						else:
									assert not mock_single_map_nested.called
									assert mock_multiprocessing_pool.called
									assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc

class    _a				(UpperCAmelCase__):





							"""simple docstring"""




							@require_tf
							def        UpperCamelCase				(							self      )    ->			Optional[Any]:
										import tensorflow as tf
										from tensorflow.keras import layers

										_SCREAMING_SNAKE_CASE      =				layers.Dense(2      )

										def gen_random_output():
													_SCREAMING_SNAKE_CASE      =				tf.random.uniform((1, 3)      )
													return model(__lowerCAmelCase      ).numpy()

										with temp_seed(42       ,							set_tensorflow=__lowerCAmelCase      ):
													_SCREAMING_SNAKE_CASE      =				gen_random_output()
										with temp_seed(42       ,							set_tensorflow=__lowerCAmelCase      ):
													_SCREAMING_SNAKE_CASE      =				gen_random_output()
										_SCREAMING_SNAKE_CASE      =				gen_random_output()

										np.testing.assert_equal(__lowerCAmelCase       ,							__lowerCAmelCase      )
										self.assertGreater(np.abs(outa - outa      ).sum()       ,							0      )






							@require_torch
							def        UpperCamelCase				(							self      )    ->			Tuple:
										import torch

										def gen_random_output():
													_SCREAMING_SNAKE_CASE      =				torch.nn.Linear(3       ,							2      )
													_SCREAMING_SNAKE_CASE      =				torch.rand(1       ,							3      )
													return model(__lowerCAmelCase      ).detach().numpy()

										with temp_seed(42       ,							set_pytorch=__lowerCAmelCase      ):
													_SCREAMING_SNAKE_CASE      =				gen_random_output()
										with temp_seed(42       ,							set_pytorch=__lowerCAmelCase      ):
													_SCREAMING_SNAKE_CASE      =				gen_random_output()
										_SCREAMING_SNAKE_CASE      =				gen_random_output()

										np.testing.assert_equal(__lowerCAmelCase       ,							__lowerCAmelCase      )
										self.assertGreater(np.abs(outa - outa      ).sum()       ,							0      )






							def        UpperCamelCase				(							self      )    ->			List[Any]:






										def gen_random_output():
													return np.random.rand(1       ,							3      )

										with temp_seed(42      ):
													_SCREAMING_SNAKE_CASE      =				gen_random_output()
										with temp_seed(42      ):
													_SCREAMING_SNAKE_CASE      =				gen_random_output()
										_SCREAMING_SNAKE_CASE      =				gen_random_output()

										np.testing.assert_equal(__lowerCAmelCase       ,							__lowerCAmelCase      )
										self.assertGreater(np.abs(outa - outa      ).sum()       ,							0      )






@pytest.mark.parametrize("""input_data"""    ,      [{}]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				NestedDataStructure(UpperCAmelCase__						).data
			assert output_data == input_data
@pytest.mark.parametrize(
    """data, expected_output"""    ,      [
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				NestedDataStructure(UpperCAmelCase__						).flatten()
			assert output == expected_output
def    lowerCAmelCase_	(				)     ->     int:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				A(x=1    ,      y="""foobar"""						)
			_SCREAMING_SNAKE_CASE      =				{"""x""": 1, """y""": """foobar"""}
			assert asdict(UpperCAmelCase__						) == expected_output

			_SCREAMING_SNAKE_CASE      =				{"""a""": {"""b""": A(x=10    ,      y="""foo"""						)}, """c""": [A(x=20    ,      y="""bar"""						)]}
			_SCREAMING_SNAKE_CASE      =				{"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
			assert asdict(UpperCAmelCase__						) == expected_output

			with pytest.raises(UpperCAmelCase__						):
						asdict([1, A(x=10    ,      y="""foo"""						)]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Dict:




			"""simple docstring"""
			return text.split()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




			"""simple docstring"""
			yield (time.time(), content)
			time.sleep(2						)
			yield (time.time(), content)







def    lowerCAmelCase_	(				)     ->     List[str]:




			"""simple docstring"""

			with Pool(2						) as pool:
						_SCREAMING_SNAKE_CASE      =				list(iflatmap_unordered(UpperCAmelCase__    ,      _split_text    ,      kwargs_iterable=[{"""text""": """hello there"""}] * 10						)						)
						assert out.count("""hello"""						) == 10
						assert out.count("""there"""						) == 10
						assert len(UpperCAmelCase__						) == 20

			# check multiprocess from pathos (uses dill for pickling)
			with multiprocess.Pool(2						) as pool:
						_SCREAMING_SNAKE_CASE      =				list(iflatmap_unordered(UpperCAmelCase__    ,      _split_text    ,      kwargs_iterable=[{"""text""": """hello there"""}] * 10						)						)
						assert out.count("""hello"""						) == 10
						assert out.count("""there"""						) == 10
						assert len(UpperCAmelCase__						) == 20

			# check that we get items as fast as possible
			with Pool(2						) as pool:
						_SCREAMING_SNAKE_CASE      =				[]
						for yield_time, content in iflatmap_unordered(
						    UpperCAmelCase__    ,      _aseconds_generator_of_aitems_with_timing    ,      kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]						):
									assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
									out.append(UpperCAmelCase__						)
						assert out.count("""a"""						) == 2
						assert out.count("""b"""						) == 2
						assert len(UpperCAmelCase__						) == 4








'''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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				XCLIPTextConfig()

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

   if "large" in model_name:
      _SCREAMING_SNAKE_CASE      =				7_68
      _SCREAMING_SNAKE_CASE      =				30_72
      _SCREAMING_SNAKE_CASE      =				12

      _SCREAMING_SNAKE_CASE      =				10_24
      _SCREAMING_SNAKE_CASE      =				40_96
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				24
      _SCREAMING_SNAKE_CASE      =				7_68
      _SCREAMING_SNAKE_CASE      =				30_72

   if model_name == "xclip-large-patch14-16-frames":
      _SCREAMING_SNAKE_CASE      =				3_36

   _SCREAMING_SNAKE_CASE      =				XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   if "large" in model_name:
      _SCREAMING_SNAKE_CASE      =				7_68

   return config
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Dict:




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

   return name
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      _SCREAMING_SNAKE_CASE      =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_						)

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

   return orig_state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if num_frames == 8:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti_8_frames.npy"""
   elif num_frames == 16:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti.npy"""
   elif num_frames == 32:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti_32_frames.npy"""
   _SCREAMING_SNAKE_CASE      =				hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""    ,      filename=SCREAMING_SNAKE_CASE_    ,      repo_type="""dataset"""    ,      )
   _SCREAMING_SNAKE_CASE      =				np.load(SCREAMING_SNAKE_CASE_						)
   return list(SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				{
       # 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""",
   }

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

   _SCREAMING_SNAKE_CASE      =				get_xclip_config(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				XCLIPModel(SCREAMING_SNAKE_CASE_						)
   model.eval()

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

   _SCREAMING_SNAKE_CASE      =				convert_state_dict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

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

   _SCREAMING_SNAKE_CASE      =				3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
   _SCREAMING_SNAKE_CASE      =				VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""						)
   _SCREAMING_SNAKE_CASE      =				CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""						)
   _SCREAMING_SNAKE_CASE      =				XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_						)

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

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

   with torch.no_grad():
      _SCREAMING_SNAKE_CASE      =				model(**SCREAMING_SNAKE_CASE_						)

   # Verify outputs
   _SCREAMING_SNAKE_CASE      =				outputs.logits_per_video
   _SCREAMING_SNAKE_CASE      =				logits_per_video.softmax(dim=1						)
   print("""Probs:"""    ,      SCREAMING_SNAKE_CASE_						)
   # kinetics-400
   if model_name == "xclip-base-patch32":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0019, 0.9951, 0.0030]]						)
   elif model_name == "xclip-base-patch32-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]						)
   elif model_name == "xclip-base-patch16":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0083, 0.9681, 0.0236]]						)
   elif model_name == "xclip-base-patch16-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]						)
   elif model_name == "xclip-large-patch14":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0062, 0.9864, 0.0075]]						)
   elif model_name == "xclip-large-patch14-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]						)
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0555, 0.8914, 0.0531]]						)
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]						)
   elif model_name == "xclip-large-patch14-kinetics-600":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0036, 0.9920, 0.0045]]						)
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0027, 0.9904, 0.0070]]						)
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]						)
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]						)
   else:
      raise ValueError(F"Model name {model_name} not supported"						)
   assert torch.allclose(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      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(SCREAMING_SNAKE_CASE_						)

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


if __name__ == "__main__":
     UpperCamelCase__      :		Union[str, 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."
     )

     UpperCamelCase__      :		str       							=  parser.parse_args()
     convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






