
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	AutoConfig.from_pretrained(__lowerCAmelCase				)
			lowerCAmelCase_						=	FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCAmelCase				)
			lowerCAmelCase_						=	checkpoints.load_tax_checkpoint(__lowerCAmelCase				)

			lowerCAmelCase_						=	"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

			if config.model_type == "t5":
						lowerCAmelCase_						=	"SelfAttention"
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						lowerCAmelCase_						=	"LocalSelfAttention"
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCAmelCase_						=	"TransientGlobalSelfAttention"
			else:
						raise ValueError(
						    "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
						    " attribute with a value from ['local', 'transient-global]."				)

			# Encoder
			for layer_index in range(config.num_layers				):
						lowerCAmelCase_						=	F"""layers_{str(__lowerCAmelCase				)}"""

						# Self-Attention
						lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
						lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
						lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
						lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

						# Layer Normalization
						lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

						if split_mlp_wi:
									lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

						lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						lowerCAmelCase_						=	tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						lowerCAmelCase_						=	flax_model.params["encoder"]["block"][str(__lowerCAmelCase				)]["layer"]
						lowerCAmelCase_						=	tax_attention_key
						lowerCAmelCase_						=	tax_attention_out
						lowerCAmelCase_						=	tax_attention_query
						lowerCAmelCase_						=	tax_attention_value

						lowerCAmelCase_						=	tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCAmelCase_						=	tax_global_layer_norm

						if split_mlp_wi:
									lowerCAmelCase_						=	tax_mlp_wi_a
									lowerCAmelCase_						=	tax_mlp_wi_a
						else:
									lowerCAmelCase_						=	tax_mlp_wi

						lowerCAmelCase_						=	tax_mlp_wo
						lowerCAmelCase_						=	tax_mlp_layer_norm

						lowerCAmelCase_						=	flax_model_encoder_layer_block

			# Only for layer 0:
			lowerCAmelCase_						=	tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
			lowerCAmelCase_						=	tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCAmelCase_						=	tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
						lowerCAmelCase_						=	tax_encoder_global_rel_embedding

			# Assigning
			lowerCAmelCase_						=	tax_model["target"]["encoder"]["encoder_norm"]["scale"]
			lowerCAmelCase_						=	tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers				):
						lowerCAmelCase_						=	F"""layers_{str(__lowerCAmelCase				)}"""

						# Self-Attention
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

						# Layer Normalization
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
						    "scale"
						]

						# Encoder-Decoder-Attention
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
						lowerCAmelCase_						=	tax_enc_dec_attention_module["key"]["kernel"]
						lowerCAmelCase_						=	tax_enc_dec_attention_module["out"]["kernel"]
						lowerCAmelCase_						=	tax_enc_dec_attention_module["query"]["kernel"]
						lowerCAmelCase_						=	tax_enc_dec_attention_module["value"]["kernel"]

						# Layer Normalization
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

						# MLP
						if split_mlp_wi:
									lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						lowerCAmelCase_						=	tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						lowerCAmelCase_						=	flax_model.params["decoder"]["block"][str(__lowerCAmelCase				)]["layer"]
						lowerCAmelCase_						=	tax_attention_key
						lowerCAmelCase_						=	tax_attention_out
						lowerCAmelCase_						=	tax_attention_query
						lowerCAmelCase_						=	tax_attention_value

						lowerCAmelCase_						=	tax_pre_attention_layer_norm

						lowerCAmelCase_						=	tax_enc_dec_attention_key
						lowerCAmelCase_						=	tax_enc_dec_attention_out
						lowerCAmelCase_						=	tax_enc_dec_attention_query
						lowerCAmelCase_						=	tax_enc_dec_attention_value

						lowerCAmelCase_						=	tax_cross_layer_norm

						if split_mlp_wi:
									lowerCAmelCase_						=	tax_mlp_wi_a
									lowerCAmelCase_						=	tax_mlp_wi_a
						else:
									lowerCAmelCase_						=	tax_mlp_wi

						lowerCAmelCase_						=	tax_mlp_wo

						lowerCAmelCase_						=	txa_mlp_layer_norm

						lowerCAmelCase_						=	flax_model_decoder_layer_block

			# Decoder Normalization
			lowerCAmelCase_						=	tax_model["target"]["decoder"]["decoder_norm"]["scale"]
			lowerCAmelCase_						=	txa_decoder_norm

			# Only for layer 0:
			lowerCAmelCase_						=	tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
			lowerCAmelCase_						=	tax_decoder_rel_embedding

			# Token Embeddings
			lowerCAmelCase_						=	tax_model["target"]["token_embedder"]["embedding"]
			lowerCAmelCase_						=	txa_token_embeddings

			# LM Head (only in v1.1 and LongT5 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						lowerCAmelCase_						=	tax_model["target"]["decoder"]["logits_dense"]["kernel"]

			flax_model.save_pretrained(__lowerCAmelCase				)
			print("T5X Model was sucessfully converted!"				)


if __name__ == "__main__":
			_A   =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
			)
			parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
			parser.add_argument(
			    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
			)
			_A   =     parser.parse_args()
			convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:





			class 	_lowerCAmelCase					:




							@staticmethod
							def      __a						(					*_UpperCamelCase				,				**_UpperCamelCase     )				->      str:
										pass





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					=MODEL_FOR_OBJECT_DETECTION_MAPPING




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				image_processor=_UpperCamelCase     )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"				,				threshold=0.0     )

							self.assertGreater(len(_UpperCamelCase     )				,				0     )
							for detected_object in outputs:
										self.assertEqual(
										    _UpperCamelCase				,				{
										        "score": ANY(_UpperCamelCase     ),
										        "label": ANY(_UpperCamelCase     ),
										        "box": {"xmin": ANY(_UpperCamelCase     ), "ymin": ANY(_UpperCamelCase     ), "xmax": ANY(_UpperCamelCase     ), "ymax": ANY(_UpperCamelCase     )},
										    }				,				)

							import datasets

							lowerCAmelCase_						=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"				,				"image"				,				split="test"     )

							lowerCAmelCase_						=	[
							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
							    "http://images.cocodataset.org/val2017/000000039769.jpg",
							    # RGBA
							    dataset[0]["file"],
							    # LA
							    dataset[1]["file"],
							    # L
							    dataset[2]["file"],
							]
							lowerCAmelCase_						=	object_detector(_UpperCamelCase				,				threshold=0.0     )

							self.assertEqual(len(_UpperCamelCase     )				,				len(_UpperCamelCase     )     )
							for outputs in batch_outputs:
										self.assertGreater(len(_UpperCamelCase     )				,				0     )
										for detected_object in outputs:
													self.assertEqual(
													    _UpperCamelCase				,				{
													        "score": ANY(_UpperCamelCase     ),
													        "label": ANY(_UpperCamelCase     ),
													        "box": {"xmin": ANY(_UpperCamelCase     ), "ymin": ANY(_UpperCamelCase     ), "xmax": ANY(_UpperCamelCase     ), "ymax": ANY(_UpperCamelCase     )},
													    }				,				)




				@require_tf
				@unittest.skip("Object detection not implemented in TF"     )
				def      __a						(					self     )				->      Optional[int]:
							pass




				@require_torch
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"hf-internal-testing/tiny-detr-mobilenetsv3"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=0.0     )

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]				,				threshold=0.0				,				)

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	0.9985
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=_UpperCamelCase     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)




				@require_torch
				@require_pytesseract
				@slow
				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	"Narsil/layoutlmv3-finetuned-funsd"
							lowerCAmelCase_						=	0.9993

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase				,				threshold=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector(
							    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							    ]				,				)



