
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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    str="attention")      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
							__UpperCamelCase			:     Any         =      params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
							__UpperCamelCase			:     Union[str, Any]         =      params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
							__UpperCamelCase			:     int         =      params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
							return k, o, q, v




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[Any]=False)      ->					Union[str, Any]:






							'''simple docstring'''





							if split_mlp_wi:
														__UpperCamelCase			:     List[str]         =      params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
														__UpperCamelCase			:     Optional[int]         =      params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
														__UpperCamelCase			:     Any         =      (wi_a, wi_a)
							else:
														__UpperCamelCase			:     Optional[Any]         =      params[F'{prefix}/layers_{i}/mlp/wi/kernel']

							__UpperCamelCase			:     Optional[Any]         =      params[F'{prefix}/layers_{i}/mlp/wo/kernel']
							return wi, wo




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    Tuple)      ->					List[str]:






							'''simple docstring'''





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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    dict		,						*, _lowerCamelCase				:    int		,						_lowerCamelCase				:    bool)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      traverse_util.flatten_dict(variables["target"])
							__UpperCamelCase			:     Union[str, Any]         =      {"/".join(_lowerCamelCase): v for k, v in old.items()}

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

							__UpperCamelCase			:     Optional[int]         =      collections.OrderedDict()

							# Shared embeddings.
							__UpperCamelCase			:     Union[str, Any]         =      old["token_embedder/embedding"]

							# Encoder.
							for i in range(_lowerCamelCase):
														# Block i, layer 0 (Self Attention).
														__UpperCamelCase			:     Any         =      tax_layer_norm_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"encoder"		,						"pre_attention_layer_norm")
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      tax_attention_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"encoder"		,						"attention")
														__UpperCamelCase			:     Tuple         =      layer_norm
														__UpperCamelCase			:     List[Any]         =      k.T
														__UpperCamelCase			:     Union[str, Any]         =      o.T
														__UpperCamelCase			:     Optional[int]         =      q.T
														__UpperCamelCase			:     Any         =      v.T

														# Block i, layer 1 (MLP).
														__UpperCamelCase			:     int         =      tax_layer_norm_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"encoder"		,						"pre_mlp_layer_norm")
														__UpperCamelCase     ,		__UpperCamelCase			:     str         =      tax_mlp_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"encoder"		,						_lowerCamelCase)
														__UpperCamelCase			:     Union[str, Any]         =      layer_norm
														if split_mlp_wi:
																					__UpperCamelCase			:     Union[str, Any]         =      wi[0].T
																					__UpperCamelCase			:     Tuple         =      wi[1].T
														else:
																					__UpperCamelCase			:     Dict         =      wi.T
														__UpperCamelCase			:     List[Any]         =      wo.T

							__UpperCamelCase			:     List[Any]         =      old[
							    "encoder/relpos_bias/rel_embedding"
							].T
							__UpperCamelCase			:     Dict         =      old["encoder/encoder_norm/scale"]

							if not is_encoder_only:
														# Decoder.
														for i in range(_lowerCamelCase):
																					# Block i, layer 0 (Self Attention).
																					__UpperCamelCase			:     Any         =      tax_layer_norm_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"decoder"		,						"pre_self_attention_layer_norm")
																					__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      tax_attention_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"decoder"		,						"self_attention")
																					__UpperCamelCase			:     Union[str, Any]         =      layer_norm
																					__UpperCamelCase			:     Optional[Any]         =      k.T
																					__UpperCamelCase			:     int         =      o.T
																					__UpperCamelCase			:     List[str]         =      q.T
																					__UpperCamelCase			:     Tuple         =      v.T

																					# Block i, layer 1 (Cross Attention).
																					__UpperCamelCase			:     Optional[int]         =      tax_layer_norm_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"decoder"		,						"pre_cross_attention_layer_norm")
																					__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      tax_attention_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"decoder"		,						"encoder_decoder_attention")
																					__UpperCamelCase			:     Optional[int]         =      layer_norm
																					__UpperCamelCase			:     str         =      k.T
																					__UpperCamelCase			:     Any         =      o.T
																					__UpperCamelCase			:     Optional[int]         =      q.T
																					__UpperCamelCase			:     Union[str, Any]         =      v.T

																					# Block i, layer 2 (MLP).
																					__UpperCamelCase			:     List[Any]         =      tax_layer_norm_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"decoder"		,						"pre_mlp_layer_norm")
																					__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      tax_mlp_lookup(_lowerCamelCase		,						_lowerCamelCase		,						"decoder"		,						_lowerCamelCase)
																					__UpperCamelCase			:     Union[str, Any]         =      layer_norm
																					if split_mlp_wi:
																												__UpperCamelCase			:     Optional[Any]         =      wi[0].T
																												__UpperCamelCase			:     str         =      wi[1].T
																					else:
																												__UpperCamelCase			:     Any         =      wi.T
																					__UpperCamelCase			:     Dict         =      wo.T

														__UpperCamelCase			:     Optional[int]         =      old["decoder/decoder_norm/scale"]
														__UpperCamelCase			:     Optional[int]         =      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			:     int         =      old["decoder/logits_dense/kernel"].T

							return new




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    bool)      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      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			:     Tuple         =      state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__UpperCamelCase			:     List[Any]         =      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			:     Optional[Any]         =      state_dict["shared.weight"]

							return state_dict




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[str])      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      checkpoints.load_tax_checkpoint(_lowerCamelCase)
							__UpperCamelCase			:     int         =      convert_tax_to_pytorch(_lowerCamelCase		,						num_layers=config.num_layers		,						is_encoder_only=_lowerCamelCase)
							__UpperCamelCase			:     Any         =      make_state_dict(_lowerCamelCase		,						_lowerCamelCase)
							model.load_state_dict(_lowerCamelCase		,						strict=_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    bool = False)      ->					Any:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      TaConfig.from_json_file(_lowerCamelCase)
							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			:     Union[str, Any]         =      TaEncoderModel(_lowerCamelCase)
							else:
														__UpperCamelCase			:     Optional[Any]         =      TaForConditionalGeneration(_lowerCamelCase)

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

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

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


if __name__ == "__main__":
		lowercase						:      List[Any]									= 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
		)
		lowercase						:      Dict									= parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:




		class 				lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:Optional[Any]  ,     a	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      DepthEstimationPipeline(model=a  ,     image_processor=a		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      depth_estimator(
								    [
								        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"],
								    ]		)
								self.assertEqual(
								    [
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								    ]  ,     a  ,     )







	@require_tf
	@unittest.skip("Depth estimation is not implemented in TF"		)
	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								pass







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      hashimage(outputs["depth"]		)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)  ,     29.304		)
								self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)  ,     2.662		)







	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)