





"""simple docstring"""





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE="attention"    ):




		"""simple docstring"""


		UpperCamelCase   =	params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
		UpperCamelCase   =	params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
		UpperCamelCase   =	params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
		UpperCamelCase   =	params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
		return k, o, q, v


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False    ):




		"""simple docstring"""


		if split_mlp_wi:
				UpperCamelCase   =	params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
				UpperCamelCase   =	params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
				UpperCamelCase   =	(wi_a, wi_a)
		else:
				UpperCamelCase   =	params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

		UpperCamelCase   =	params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
		return wi, wo


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return params[F"{prefix}/layers_{i}/{layer_name}/scale"]


def       a__					(					_SCREAMING_SNAKE_CASE  ,	*, _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	traverse_util.flatten_dict(variables["target"]    )
		UpperCamelCase   =	{"/".join(_SCREAMING_SNAKE_CASE    ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		UpperCamelCase   =	"encoder/layers_0/mlp/wi_0/kernel" in old
		print("Split MLP:"  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	collections.OrderedDict()

		# Shared embeddings.
		UpperCamelCase   =	old["token_embedder/embedding"]

		# Encoder.
		for i in range(_SCREAMING_SNAKE_CASE    ):
				# Block i, layer 0 (Self Attention).
				UpperCamelCase   =	tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"encoder"  ,	"pre_attention_layer_norm"    )
				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	tax_attention_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"encoder"  ,	"attention"    )
				UpperCamelCase   =	layer_norm
				UpperCamelCase   =	k.T
				UpperCamelCase   =	o.T
				UpperCamelCase   =	q.T
				UpperCamelCase   =	v.T

				# Block i, layer 1 (MLP).
				UpperCamelCase   =	tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"encoder"  ,	"pre_mlp_layer_norm"    )
				UpperCamelCase   ,				UpperCamelCase   =	tax_mlp_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"encoder"  ,	_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	layer_norm
				if split_mlp_wi:
						UpperCamelCase   =	wi[0].T
						UpperCamelCase   =	wi[1].T
				else:
						UpperCamelCase   =	wi.T
				UpperCamelCase   =	wo.T

		UpperCamelCase   =	old[
		    "encoder/relpos_bias/rel_embedding"
		].T
		UpperCamelCase   =	old["encoder/encoder_norm/scale"]

		if not is_encoder_only:
				# Decoder.
				for i in range(_SCREAMING_SNAKE_CASE    ):
						# Block i, layer 0 (Self Attention).
						UpperCamelCase   =	tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"decoder"  ,	"pre_self_attention_layer_norm"    )
						UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	tax_attention_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"decoder"  ,	"self_attention"    )
						UpperCamelCase   =	layer_norm
						UpperCamelCase   =	k.T
						UpperCamelCase   =	o.T
						UpperCamelCase   =	q.T
						UpperCamelCase   =	v.T

						# Block i, layer 1 (Cross Attention).
						UpperCamelCase   =	tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"decoder"  ,	"pre_cross_attention_layer_norm"    )
						UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	tax_attention_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"decoder"  ,	"encoder_decoder_attention"    )
						UpperCamelCase   =	layer_norm
						UpperCamelCase   =	k.T
						UpperCamelCase   =	o.T
						UpperCamelCase   =	q.T
						UpperCamelCase   =	v.T

						# Block i, layer 2 (MLP).
						UpperCamelCase   =	tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"decoder"  ,	"pre_mlp_layer_norm"    )
						UpperCamelCase   ,				UpperCamelCase   =	tax_mlp_lookup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	"decoder"  ,	_SCREAMING_SNAKE_CASE    )
						UpperCamelCase   =	layer_norm
						if split_mlp_wi:
								UpperCamelCase   =	wi[0].T
								UpperCamelCase   =	wi[1].T
						else:
								UpperCamelCase   =	wi.T
						UpperCamelCase   =	wo.T

				UpperCamelCase   =	old["decoder/decoder_norm/scale"]
				UpperCamelCase   =	old[
				    "decoder/relpos_bias/rel_embedding"
				].T

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						UpperCamelCase   =	old["decoder/logits_dense/kernel"].T

		return new


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	collections.OrderedDict([(k, torch.from_numpy(v.copy()    )) for (k, v) in converted_params.items()]    )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				UpperCamelCase   =	state_dict["shared.weight"]

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						UpperCamelCase   =	state_dict["shared.weight"]

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print("Using shared word embeddings as lm_head."    )
						UpperCamelCase   =	state_dict["shared.weight"]

		return state_dict


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	convert_tax_to_pytorch(_SCREAMING_SNAKE_CASE  ,	num_layers=config.num_layers  ,	is_encoder_only=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_state_dict(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = False    ):




		"""simple docstring"""


		UpperCamelCase   =	TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
		print(F"Building PyTorch model from configuration: {config}"    )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				UpperCamelCase   =	TaEncoderModel(_SCREAMING_SNAKE_CASE    )
		else:
				UpperCamelCase   =	TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		# Save pytorch-model
		print(F"Save PyTorch model to {pytorch_dump_path}"    )
		model.save_pretrained(_SCREAMING_SNAKE_CASE    )

		# Verify that we can load the checkpoint.
		model.from_pretrained(_SCREAMING_SNAKE_CASE    )
		print("Done"    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
						# Required parameters
						parser.add_argument(
						    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
						)
						lowerCAmelCase__     			=			parser.parse_args()
						convert_tax_checkpoint_to_pytorch(
						    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
						)













"""simple docstring"""





import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        _lowerCamelCase      (       unittest.TestCase ):
						UpperCAmelCase_ =			MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   str:
								UpperCamelCase   =	hf_hub_download(
								    repo_id="nateraw/video-demo"   ,							filename="archery.mp4"   ,							repo_type="dataset"		)
								UpperCamelCase   =	VideoClassificationPipeline(model=__a   ,							image_processor=__a   ,							top_k=2		)
								UpperCamelCase   =	[
								    example_video_filepath,
								    "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
								]
								return video_classifier, examples


						def     snake_case_   (self   ,							__a   ,							__a		) ->   str:
								for example in examples:
										UpperCamelCase   =	video_classifier(__a		)

										self.assertEqual(
										    __a   ,							[
										        {"score": ANY(__a		), "label": ANY(__a		)},
										        {"score": ANY(__a		), "label": ANY(__a		)},
										    ]   ,							)


						@require_torch
						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
								UpperCamelCase   =	VideoMAEFeatureExtractor(
								    size={"shortest_edge": 10}   ,							crop_size={"height": 10, "width": 10}		)
								UpperCamelCase   =	pipeline(
								    "video-classification"   ,							model=__a   ,							feature_extractor=__a   ,							frame_sampling_rate=4		)

								UpperCamelCase   =	hf_hub_download(repo_id="nateraw/video-demo"   ,							filename="archery.mp4"   ,							repo_type="dataset"		)
								UpperCamelCase   =	video_classifier(__a   ,							top_k=2		)
								self.assertEqual(
								    nested_simplify(__a   ,							decimals=4		)   ,							[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]   ,							)

								UpperCamelCase   =	video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]   ,							top_k=2   ,							)
								self.assertEqual(
								    nested_simplify(__a   ,							decimals=4		)   ,							[
								        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
								        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
								    ]   ,							)




						@require_tf
						def     snake_case_   (self		) ->   Optional[Any]:
								pass






