import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 UpperCAmelCase      		=					'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]=None		,    lowercase_:						List[str]=None					):
       if attention_mask is None:
              _UpperCamelCase  :							Optional[Any]	    =	np.where(input_ids != config.pad_token_id		,    1		,    0					)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }




@require_flax
class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[Any]=1_3							,      lowerCamelCase							:		List[Any]=7							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		List[Any]=False							,      lowerCamelCase							:		Any=9_9							,      lowerCamelCase							:		Any=1_6							,      lowerCamelCase							:		Dict=2							,      lowerCamelCase							:		List[Any]=4							,      lowerCamelCase							:		Any=4							,      lowerCamelCase							:		List[str]="gelu"							,      lowerCamelCase							:		str=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		Optional[Any]=2_0							,      lowerCamelCase							:		Tuple=2							,      lowerCamelCase							:		int=1							,      lowerCamelCase							:		List[str]=0							,      lowerCamelCase							:		Tuple=1_6							,      lowerCamelCase							:		str=1_6							,      lowerCamelCase							:		Optional[Any]=0.0_2							,      )			->				List[str]:
        _UpperCamelCase  :							Optional[int]	    =	parent
        _UpperCamelCase  :							Optional[Any]	    =	batch_size
        _UpperCamelCase  :							Optional[Any]	    =	seq_length
        _UpperCamelCase  :							int	    =	is_training
        _UpperCamelCase  :							Optional[int]	    =	use_labels
        _UpperCamelCase  :							Optional[Any]	    =	vocab_size
        _UpperCamelCase  :							Optional[Any]	    =	hidden_size
        _UpperCamelCase  :							Optional[Any]	    =	num_hidden_layers
        _UpperCamelCase  :							Tuple	    =	num_attention_heads
        _UpperCamelCase  :							Optional[int]	    =	intermediate_size
        _UpperCamelCase  :							Any	    =	hidden_act
        _UpperCamelCase  :							Dict	    =	hidden_dropout_prob
        _UpperCamelCase  :							Optional[int]	    =	attention_probs_dropout_prob
        _UpperCamelCase  :							Optional[Any]	    =	max_position_embeddings
        _UpperCamelCase  :							Tuple	    =	eos_token_id
        _UpperCamelCase  :							Dict	    =	pad_token_id
        _UpperCamelCase  :							str	    =	bos_token_id
        _UpperCamelCase  :							Optional[int]	    =	embed_dim
        _UpperCamelCase  :							Tuple	    =	word_embed_proj_dim
        _UpperCamelCase  :							Optional[Any]	    =	initializer_range
        _UpperCamelCase  :							str	    =	False

 def        __snake_case       (      self							:		Optional[Any]	)			->				Tuple:
        _UpperCamelCase  :							Dict	    =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,      self.vocab_size	)							,      3							,      self.vocab_size	)
        _UpperCamelCase  :							Dict	    =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,      dtype=np.intaa	))							,      -1	)

        _UpperCamelCase  :							Optional[int]	    =	OPTConfig(
            vocab_size=self.vocab_size							,      hidden_size=self.hidden_size							,      num_hidden_layers=self.num_hidden_layers							,      num_attention_heads=self.num_attention_heads							,      ffn_dim=self.intermediate_size							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      eos_token_id=self.eos_token_id							,      bos_token_id=self.bos_token_id							,      pad_token_id=self.pad_token_id							,      embed_dim=self.embed_dim							,      is_encoder_decoder=lowerCamelCase							,      word_embed_proj_dim=self.word_embed_proj_dim							,      initializer_range=self.initializer_range							,      use_cache=lowerCamelCase							,      )
        _UpperCamelCase  :							Optional[Any]	    =	prepare_opt_inputs_dict(lowerCamelCase							,      lowerCamelCase	)
        return config, inputs_dict

 def        __snake_case       (      self							:		List[Any]	)			->				Dict:
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	self.prepare_config_and_inputs()
        return config, inputs_dict

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		List[str]	)			->				Optional[int]:
        _UpperCamelCase  :							Optional[int]	    =	2_0
        _UpperCamelCase  :							str	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase  :							str	    =	inputs_dict['input_ids']
        _UpperCamelCase  :							str	    =	inputs_dict['attention_mask']

        _UpperCamelCase  :							Dict	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	jnp.ones((input_ids.shape[0], max_length)							,      dtype='i4'	)

        _UpperCamelCase  :							Union[str, Any]	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)							,      )
        _UpperCamelCase  :							Optional[Any]	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Union[str, Any]	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							Optional[int]	    =	model(
            input_ids[:, -1:]							,      attention_mask=lowerCamelCase							,      past_key_values=outputs_cache.past_key_values							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							int	    =	model(lowerCamelCase	)

        _UpperCamelCase  :							str	    =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3							,      msg=f"Max diff is {diff}"	)

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		int							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Union[str, Any]	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	2_0
        _UpperCamelCase  :							Optional[int]	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	(
            inputs_dict['input_ids'],
            inputs_dict['attention_mask'],
        )

        _UpperCamelCase  :							Optional[Any]	    =	jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])	),
            ]							,      axis=-1							,      )

        _UpperCamelCase  :							List[Any]	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)							,      )

        _UpperCamelCase  :							int	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )
        _UpperCamelCase  :							Union[str, Any]	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							Optional[int]	    =	model(
            input_ids[:, -1:]							,      past_key_values=outputs_cache.past_key_values							,      attention_mask=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Any	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase	)

        _UpperCamelCase  :							List[str]	    =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3							,      msg=f"Max diff is {diff}"	)







@require_flax
class  __lowerCAmelCase	(							a_,						unittest.TestCase,						a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[str, Any]					     =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 _lowerCamelCase    :						List[Any]					     =			() if is_flax_available() else ()

 def        __snake_case       (      self							:		List[Any]	)			->				int:
        _UpperCamelCase  :							Optional[Any]	    =	FlaxOPTModelTester(self	)

 def        __snake_case       (      self							:		List[str]	)			->				Any:
        _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		List[str]	)			->				Union[str, Any]:
        _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

 @slow
 def        __snake_case       (      self							:		str	)			->				Union[str, Any]:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase  :							Any	    =	model_class_name.from_pretrained('facebook/opt-125m'	)
               _UpperCamelCase  :							List[str]	    =	np.ones((1, 1)	) * model.config.eos_token_id
               _UpperCamelCase  :							Union[str, Any]	    =	model(lowerCamelCase	)
               self.assertIsNotNone(lowerCamelCase	)







@require_sentencepiece
@require_flax
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 @slow
 def        __snake_case       (      self							:		Dict	)			->				Union[str, Any]:
        _UpperCamelCase  :							List[str]	    =	FlaxOPTModel.from_pretrained('facebook/opt-350m'	)
        _UpperCamelCase  :							str	    =	jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	)
        _UpperCamelCase  :							Tuple	    =	model(input_ids=lowerCamelCase	).last_hidden_state
        _UpperCamelCase  :							Any	    =	(1, 1_1, 5_1_2)
        self.assertEqual(output.shape							,      lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	jnp.array(
            [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]	)
        self.assertTrue(jnp.allclose(output[:, :3, :3]							,      lowerCamelCase							,      atol=4e-2	)	)







@require_flax
@slow
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		str	)			->				str:
        super().setUp()
        _UpperCamelCase  :							int	    =	'facebook/opt-350m'

 def        __snake_case       (      self							:		List[str]	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	FlaxOPTForCausalLM.from_pretrained(self.path_model	)
        _UpperCamelCase  :							List[Any]	    =	GPTaTokenizer.from_pretrained(self.path_model	)

        _UpperCamelCase  :							Any	    =	[
            'Today is a beautiful day and I want to',
            'In the city of',
            'Paris is the capital of France and',
            'Computers and mobile phones have taken',
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'							,      padding=lowerCamelCase							,      add_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							int	    =	model(inputs.input_ids							,      attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
        _UpperCamelCase  :							Any	    =	jnp.array(
            [
                [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
            ]	)
        self.assertTrue(jnp.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=4e-2	)	)

        _UpperCamelCase  :							Dict	    =	jax.jit(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	model(inputs.input_ids							,      attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
        self.assertTrue(jnp.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=4e-2	)	)







@require_flax
@slow
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 @property
 def        __snake_case       (      self							:		List[str]	)			->				Optional[Any]:
        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]

 def        __snake_case       (      self							:		List[Any]	)			->				List[Any]:
        _UpperCamelCase  :							str	    =	'facebook/opt-125m'

        _UpperCamelCase  :							Any	    =	[
            'Today is a beautiful day and I want to',
            'In the city of New York, the city',
            'Paris is the capital of France and the capital',
            'Computers and mobile phones have taken over the',
        ]

        _UpperCamelCase  :							Tuple	    =	[]

        _UpperCamelCase  :							List[Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)

        for prompt in self.prompts:
               _UpperCamelCase  :							List[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'	).input_ids

               _UpperCamelCase  :							Optional[Any]	    =	model.generate(lowerCamelCase							,      max_length=1_0	)
               _UpperCamelCase  :							int	    =	generated_ids[0]

               _UpperCamelCase  :							Dict	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)
               predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Optional[int]:
        _UpperCamelCase  :							Tuple	    =	'facebook/opt-350m'

        _UpperCamelCase  :							Dict	    =	[
            'Today is a beautiful day and I want to',
            'In the city of San Francisco, the city',
            'Paris is the capital of France and the capital',
            'Computers and mobile phones have taken over the',
        ]

        _UpperCamelCase  :							str	    =	[]
        _UpperCamelCase  :							Tuple	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)

        for prompt in self.prompts:
               _UpperCamelCase  :							Optional[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'	).input_ids

               _UpperCamelCase  :							Tuple	    =	model.generate(lowerCamelCase							,      max_length=1_0	)
               _UpperCamelCase  :							List[Any]	    =	generated_ids[0]

               _UpperCamelCase  :							Any	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)
               predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	'facebook/opt-125m'
        _UpperCamelCase  :							List[Any]	    =	[
            'Today is a beautiful day and I want to thank',
            'In the city of Rome Canaver Canaver Canaver Canaver',
        ]
        _UpperCamelCase  :							List[Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							str	    =	tokenizer(
            [
                'Today is a beautiful day and I want to',
                'In the city of',
            ]							,      return_tensors='jax'							,      padding=lowerCamelCase							,      )

        _UpperCamelCase  :							Optional[Any]	    =	jax.jit(model.generate	)

        _UpperCamelCase  :							str	    =	jit_generate(inputs['input_ids']							,      attention_mask=inputs['attention_mask']	).sequences

        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)

        self.assertIsNotNone(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Dict	)			->				Dict:
        _UpperCamelCase  :							Optional[Any]	    =	'facebook/opt-350m'

        _UpperCamelCase  :							Optional[Any]	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)

        _UpperCamelCase  :							List[Any]	    =	'left'

        # use different length sentences to test batching
        _UpperCamelCase  :							Dict	    =	[
            'Hello, my dog is a little',
            'Today, I',
        ]

        _UpperCamelCase  :							Union[str, Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'							,      padding=lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	inputs['input_ids']

        _UpperCamelCase  :							Union[str, Any]	    =	model.generate(input_ids=lowerCamelCase							,      attention_mask=inputs['attention_mask']							,      trace=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	tokenizer(sentences[0]							,      return_tensors='jax'	).input_ids
        _UpperCamelCase  :							List[str]	    =	model.generate(input_ids=lowerCamelCase	)

        _UpperCamelCase  :							Tuple	    =	inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
        _UpperCamelCase  :							List[str]	    =	tokenizer(sentences[1]							,      return_tensors='jax'	).input_ids
        _UpperCamelCase  :							List[Any]	    =	model.generate(input_ids=lowerCamelCase							,      max_length=model.config.max_length - num_paddings	)

        _UpperCamelCase  :							int	    =	tokenizer.batch_decode(outputs[0]							,      skip_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.decode(output_non_padded[0][0]							,      skip_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.decode(output_padded[0][0]							,      skip_special_tokens=lowerCamelCase	)

        _UpperCamelCase  :							Dict	    =	[
            'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
            'Today, I was in the middle of a conversation with a friend about the',
        ]
        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)
        self.assertListEqual(lowerCamelCase							,      [non_padded_sentence, padded_sentence]	)





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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Any				      = TimesformerConfig()

    if "large" in model_name:
        SCREAMING_SNAKE_CASE				:				Dict				      = 96

    if "hr" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 16
        SCREAMING_SNAKE_CASE				:				str				      = 448

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''huggingface/label-files'''
    if "k400" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 400
        SCREAMING_SNAKE_CASE				:				Tuple				      = '''kinetics400-id2label.json'''
    elif "k600" in model_name:
        SCREAMING_SNAKE_CASE				:				Any				      = 600
        SCREAMING_SNAKE_CASE				:				int				      = '''kinetics600-id2label.json'''
    elif "ssv2" in model_name:
        SCREAMING_SNAKE_CASE				:				Tuple				      = 174
        SCREAMING_SNAKE_CASE				:				int				      = '''something-something-v2-id2label.json'''
    else:
        raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = json.load(open(hf_hub_download(a__ ,						a__ ,						repo_type='''dataset'''   ) ,						'''r'''   )   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = {int(a__   ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE				:				str				      = idalabel
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {v: k for k, v in idalabel.items()}

    return config







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    return name







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = orig_state_dict.pop(a__   )

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

        if "qkv" in key:
            SCREAMING_SNAKE_CASE				:				int				      = key.split('''.'''   )
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = int(key_split[1]   )
            SCREAMING_SNAKE_CASE				:				int				      = '''timesformer.encoder.layer.'''
            if "temporal" in key:
                SCREAMING_SNAKE_CASE				:				Tuple				      = '''.temporal_attention.attention.qkv.'''
            else:
                SCREAMING_SNAKE_CASE				:				List[Any]				      = '''.attention.attention.qkv.'''
            if "weight" in key:
                SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = val
            else:
                SCREAMING_SNAKE_CASE				:				Optional[int]				      = val
        else:
            SCREAMING_SNAKE_CASE				:				Tuple				      = val

    return orig_state_dict







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,						filename='''eating_spaghetti.npy''' ,						repo_type='''dataset'''   )
    SCREAMING_SNAKE_CASE				:				int				      = np.load(a__   )
    return list(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = get_timesformer_config(a__   )

    SCREAMING_SNAKE_CASE				:				Tuple				      = TimesformerForVideoClassification(a__   )

    # download original checkpoint, hosted on Google Drive
    SCREAMING_SNAKE_CASE				:				Tuple				      = '''pytorch_model.bin'''
    gdown.cached_download(a__ ,						a__ ,						quiet=a__   )
    SCREAMING_SNAKE_CASE				:				int				      = torch.load(a__ ,						map_location='''cpu'''   )
    if "model" in files:
        SCREAMING_SNAKE_CASE				:				Tuple				      = files['''model''']
    elif "module" in files:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = files['''module''']
    else:
        SCREAMING_SNAKE_CASE				:				Any				      = files['''model_state''']
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = convert_state_dict(a__ ,						a__   )

    model.load_state_dict(a__   )
    model.eval()

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

    SCREAMING_SNAKE_CASE				:				Any				      = model(**a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = outputs.logits

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
        # 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":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.3_016, -0.7_713, -0.4_205]   )
    elif model_name == "timesformer-base-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-0.7_267, -0.7_466, 3.2_404]   )
    elif model_name == "timesformer-base-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_059, 0.6_433, -3.1_457]   )
    elif model_name == "timesformer-large-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				Any				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				str				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-hr-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				List[str]				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_617, -3.7_311, -3.7_708]   )
    elif model_name == "timesformer-hr-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor([2.5_273, 0.7_127, 1.8_848]   )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = 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] ,						a__ ,						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(a__   )
        model.save_pretrained(a__   )

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


if __name__ == "__main__":
      a__				:      Optional[int]    		=     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.'''
      )

      a__				:      str    		=     parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )
