

'''simple docstring'''





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
							    TimesformerForVideoClassification,
							    TimesformerModel,
							)
							from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from transformers import VideoMAEImageProcessor





class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		List[str]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Union[str, Any]=13		,				UpperCAmelCase__    :		Union[str, Any]=10		,				UpperCAmelCase__    :		List[str]=3		,				UpperCAmelCase__    :		List[Any]=2		,				UpperCAmelCase__    :		str=2		,				UpperCAmelCase__    :		Any=True		,				UpperCAmelCase__    :		Union[str, Any]=True		,				UpperCAmelCase__    :		Union[str, Any]=32		,				UpperCAmelCase__    :		str=5		,				UpperCAmelCase__    :		int=4		,				UpperCAmelCase__    :		Union[str, Any]=37		,				UpperCAmelCase__    :		Optional[int]="gelu"		,				UpperCAmelCase__    :		List[Any]=0.1		,				UpperCAmelCase__    :		List[Any]=0.1		,				UpperCAmelCase__    :		Tuple=10		,				UpperCAmelCase__    :		List[Any]=0.02		,				UpperCAmelCase__    :		Optional[int]="divided_space_time"		,				UpperCAmelCase__    :		Optional[int]=None		,				):
										'''simple docstring'''
										lowercase   :       int								=parent
										lowercase   :       Tuple								=batch_size
										lowercase   :       Optional[Any]								=image_size
										lowercase   :       Tuple								=num_channels
										lowercase   :       List[str]								=patch_size
										lowercase   :       str								=num_frames
										lowercase   :       Tuple								=is_training
										lowercase   :       List[str]								=use_labels
										lowercase   :       int								=hidden_size
										lowercase   :       Union[str, Any]								=num_hidden_layers
										lowercase   :       Optional[int]								=num_attention_heads
										lowercase   :       List[str]								=intermediate_size
										lowercase   :       List[Any]								=hidden_act
										lowercase   :       Any								=hidden_dropout_prob
										lowercase   :       Tuple								=attention_probs_dropout_prob
										lowercase   :       List[Any]								=attention_type
										lowercase   :       Optional[Any]								=initializer_range
										lowercase   :       Dict								=scope
										lowercase   :       Dict								=num_labels

										# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
										lowercase   :       List[str]								=(image_size // patch_size) ** 2
										lowercase   :       Union[str, Any]								=(num_frames) * self.num_patches_per_frame + 1







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=floats_tensor(
										    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]      )

										lowercase   :       Union[str, Any]								=None
										if self.use_labels:
																lowercase   :       Union[str, Any]								=ids_tensor([self.batch_size]		,				self.num_labels      )

										lowercase   :       int								=self.get_config()

										return config, pixel_values, labels







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       int								=TimesformerConfig(
										    image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				num_frames=self.num_frames		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				initializer_range=self.initializer_range		,				attention_type=self.attention_type		,				)
										lowercase   :       int								=self.num_labels
										return config







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=TimesformerModel(config=UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.eval()
										lowercase   :       int								=model(UpperCAmelCase__      )
										self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=TimesformerForVideoClassification(UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.eval()

										lowercase   :       List[str]								=model(UpperCAmelCase__      )

										# verify the logits shape
										lowercase   :       Union[str, Any]								=torch.Size((self.batch_size, self.num_labels)      )
										self.parent.assertEqual(result.logits.shape		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.prepare_config_and_inputs()
										lowercase		,  lowercase		,  lowercase   :       Any								=config_and_inputs
										lowercase   :       List[Any]								={'''pixel_values''': pixel_values}
										return config, inputs_dict





@require_torch
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
				lowerCamelCase_							=						(
				    {'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
				    if is_torch_available()
				    else {}
				)

				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=TimesformerModelTester(self      )
										lowercase   :       str								=ConfigTester(
										    self		,				config_class=UpperCAmelCase__		,				has_text_modality=UpperCAmelCase__		,				hidden_size=37      )







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Dict=False      ):
										'''simple docstring'''
										lowercase   :       int								=copy.deepcopy(UpperCAmelCase__      )

										if return_labels:
																if model_class in get_values(UpperCAmelCase__      ):
																						lowercase   :       Optional[Any]								=torch.zeros(
																						    self.model_tester.batch_size		,				dtype=torch.long		,				device=UpperCAmelCase__      )

										return inputs_dict







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				@unittest.skip(reason='''TimeSformer does not use inputs_embeds'''      )
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       List[str]								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase   :       Any								=model_class(UpperCAmelCase__      )
																self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module)      )
																lowercase   :       Optional[int]								=model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(UpperCAmelCase__		,				nn.Linear      )      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       int								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase   :       int								=model_class(UpperCAmelCase__      )
																lowercase   :       str								=inspect.signature(model.forward      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowercase   :       List[str]								=[*signature.parameters.keys()]

																lowercase   :       Any								=['''pixel_values''']
																self.assertListEqual(arg_names[:1]		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_video_classification(*UpperCAmelCase__      )







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase   :       Optional[Any]								=TimesformerModel.from_pretrained(UpperCAmelCase__      )
																self.assertIsNotNone(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										if not self.has_attentions:
																pass

										else:
																lowercase		,  lowercase   :       Any								=self.model_tester.prepare_config_and_inputs_for_common()
																lowercase   :       Optional[Any]								=True

																for model_class in self.all_model_classes:
																						lowercase   :       List[Any]								=self.model_tester.seq_length
																						lowercase   :       List[Any]								=self.model_tester.num_frames

																						lowercase   :       Dict								=True
																						lowercase   :       int								=False
																						lowercase   :       Optional[Any]								=True
																						lowercase   :       Optional[int]								=model_class(UpperCAmelCase__      )
																						model.to(UpperCAmelCase__      )
																						model.eval()
																						with torch.no_grad():
																												lowercase   :       Any								=model(**self._prepare_for_class(UpperCAmelCase__		,				UpperCAmelCase__      )      )
																						lowercase   :       Any								=outputs.attentions
																						self.assertEqual(len(UpperCAmelCase__      )		,				self.model_tester.num_hidden_layers      )

																						# check that output_attentions also work using config
																						del inputs_dict["output_attentions"]
																						lowercase   :       Optional[int]								=True
																						lowercase   :       Optional[Any]								=model_class(UpperCAmelCase__      )
																						model.to(UpperCAmelCase__      )
																						model.eval()
																						with torch.no_grad():
																												lowercase   :       Dict								=model(**self._prepare_for_class(UpperCAmelCase__		,				UpperCAmelCase__      )      )
																						lowercase   :       Dict								=outputs.attentions
																						self.assertEqual(len(UpperCAmelCase__      )		,				self.model_tester.num_hidden_layers      )

																						# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
																						self.assertListEqual(
																						    list(attentions[0].shape[-3:]      )		,				[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]		,				)
																						lowercase   :       Optional[int]								=len(UpperCAmelCase__      )

																						# Check attention is always last and order is fine
																						lowercase   :       str								=True
																						lowercase   :       Union[str, Any]								=True
																						lowercase   :       Optional[int]								=model_class(UpperCAmelCase__      )
																						model.to(UpperCAmelCase__      )
																						model.eval()
																						with torch.no_grad():
																												lowercase   :       Any								=model(**self._prepare_for_class(UpperCAmelCase__		,				UpperCAmelCase__      )      )

																						self.assertEqual(out_len + 1		,				len(UpperCAmelCase__      )      )

																						lowercase   :       Union[str, Any]								=outputs.attentions

																						self.assertEqual(len(UpperCAmelCase__      )		,				self.model_tester.num_hidden_layers      )

																						# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
																						self.assertListEqual(
																						    list(self_attentions[0].shape[-3:]      )		,				[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]		,				)







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										def check_hidden_states_output(UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
																lowercase   :       str								=model_class(UpperCAmelCase__      )
																model.to(UpperCAmelCase__      )
																model.eval()

																with torch.no_grad():
																						lowercase   :       Dict								=model(**self._prepare_for_class(UpperCAmelCase__		,				UpperCAmelCase__      )      )

																lowercase   :       Optional[int]								=outputs.hidden_states
																lowercase   :       List[str]								=self.model_tester.num_hidden_layers + 1
																self.assertEqual(len(UpperCAmelCase__      )		,				UpperCAmelCase__      )

																lowercase   :       Optional[int]								=self.model_tester.seq_length

																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]      )		,				[seq_length, self.model_tester.hidden_size]		,				)

										lowercase		,  lowercase   :       Any								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase   :       Any								=True
																check_hidden_states_output(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																lowercase   :       List[Any]								=True

																check_hidden_states_output(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      )






def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Union[str, Any]								=hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''      ,   filename='''eating_spaghetti.npy'''      ,   repo_type='''dataset'''	)
						lowercase   :       str								=np.load(__magic_name__	)
						return list(__magic_name__	)







@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				@cached_property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										# logits were tested with a different mean and std, so we use the same here
										return (
										    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,				image_std=[0.5, 0.5, 0.5]      )
										    if is_vision_available()
										    else None
										)







				@slow
				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''      ).to(
										    UpperCAmelCase__      )

										lowercase   :       int								=self.default_image_processor
										lowercase   :       Tuple								=prepare_video()
										lowercase   :       Any								=image_processor(video[:8]		,				return_tensors='''pt'''      ).to(UpperCAmelCase__      )

										# forward pass
										with torch.no_grad():
																lowercase   :       Optional[int]								=model(**UpperCAmelCase__      )

										# verify the logits
										lowercase   :       Any								=torch.Size((1, 400)      )
										self.assertEqual(outputs.logits.shape		,				UpperCAmelCase__      )

										lowercase   :       List[str]								=torch.tensor([-0.30_16, -0.77_13, -0.42_05]      ).to(UpperCAmelCase__      )

										self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				UpperCAmelCase__		,				atol=1E-4      )      )








'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.{param_name}''']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.{w}.weight'''
																					UpperCamelCase_							=					state_dict[param_name]
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.LayerNorm.{w}'''
																					UpperCamelCase_							=					state_dict[param_name]

    # Transformer Blocks #
							UpperCamelCase_							=					0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					state_dict["""lm_head.weight"""]

							print(f'''N layers selected for distillation: {std_idx}''')
							print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

							print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
							torch.save(compressed_sd, args.dump_checkpoint)





