
"""simple docstring"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 	_lowercase       (    UpperCAmelCase_		):
	lowercase_				=     """marian"""
	lowercase_				=     ["""past_key_values"""]
	lowercase_				=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__(			self		,			UpperCAmelCase_=58101		,			UpperCAmelCase_=None		,			UpperCAmelCase_=1024		,			UpperCAmelCase_=12		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=16		,			UpperCAmelCase_=12		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=16		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=1024		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=58100		,			UpperCAmelCase_=False		,			UpperCAmelCase_=58100		,			UpperCAmelCase_=0		,			UpperCAmelCase_=0		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			lowerCamelCase					:      str          =   vocab_size
			lowerCamelCase					:      Dict          =   decoder_vocab_size or vocab_size
			lowerCamelCase					:      int          =   max_position_embeddings
			lowerCamelCase					:      Dict          =   d_model
			lowerCamelCase					:      Any          =   encoder_ffn_dim
			lowerCamelCase					:      List[str]          =   encoder_layers
			lowerCamelCase					:      List[str]          =   encoder_attention_heads
			lowerCamelCase					:      str          =   decoder_ffn_dim
			lowerCamelCase					:      Any          =   decoder_layers
			lowerCamelCase					:      List[str]          =   decoder_attention_heads
			lowerCamelCase					:      Optional[int]          =   dropout
			lowerCamelCase					:      Dict          =   attention_dropout
			lowerCamelCase					:      List[str]          =   activation_dropout
			lowerCamelCase					:      Optional[Any]          =   activation_function
			lowerCamelCase					:      Union[str, Any]          =   init_std
			lowerCamelCase					:      Tuple          =   encoder_layerdrop
			lowerCamelCase					:      Dict          =   decoder_layerdrop
			lowerCamelCase					:      Optional[Any]          =   use_cache
			lowerCamelCase					:      int          =   encoder_layers
			lowerCamelCase					:      Dict          =   scale_embedding  # scale factor will be sqrt(d_model) if True
			lowerCamelCase					:      Optional[Any]          =   share_encoder_decoder_embeddings
			super().__init__(
			    pad_token_id=__lowercase		,			eos_token_id=__lowercase		,			is_encoder_decoder=__lowercase		,			decoder_start_token_id=__lowercase		,			forced_eos_token_id=__lowercase		,			**__lowercase		,			)






class 	_lowercase       (    UpperCAmelCase_		):




	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      Dict          =   OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					    ] )

					if self.use_past:
							lowerCamelCase					:      Optional[int]          =   {0: '''batch'''}
							lowerCamelCase					:      Optional[int]          =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
							lowerCamelCase					:      Tuple          =   {0: '''batch''', 1: '''decoder_sequence'''}
							lowerCamelCase					:      List[Any]          =   {0: '''batch''', 1: '''decoder_sequence'''}

					if self.use_past:
							self.fill_with_past_key_values_(__lowercase		,			direction='inputs' )
			elif self.task == "causal-lm":
					# TODO: figure this case out.
					lowerCamelCase					:      int          =   OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					    ] )
					if self.use_past:
							lowerCamelCase					:      Tuple          =   self.num_layers
							for i in range(__lowercase ):
									lowerCamelCase					:      List[Any]          =   {0: '''batch''', 2: '''past_sequence + sequence'''}
									lowerCamelCase					:      List[Any]          =   {0: '''batch''', 2: '''past_sequence + sequence'''}
			else:
					lowerCamelCase					:      Tuple          =   OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
					        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
					    ] )

			return common_inputs




	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      Any          =   super().outputs
			else:
					lowerCamelCase					:      Tuple          =   super(__lowercase		,			self ).outputs
					if self.use_past:
							lowerCamelCase					:      str          =   self.num_layers
							for i in range(__lowercase ):
									lowerCamelCase					:      Union[str, Any]          =   {0: '''batch''', 2: '''past_sequence + sequence'''}
									lowerCamelCase					:      Tuple          =   {0: '''batch''', 2: '''past_sequence + sequence'''}
			return common_outputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			lowerCamelCase					:      Optional[int]          =   self._generate_dummy_inputs_for_encoder_and_decoder(
			    __lowercase		,			__lowercase		,			__lowercase		,			__lowercase		,			__lowercase )

			# Generate decoder inputs
			lowerCamelCase					:      Dict          =   seq_length if not self.use_past else 1
			lowerCamelCase					:      str          =   self._generate_dummy_inputs_for_encoder_and_decoder(
			    __lowercase		,			__lowercase		,			__lowercase		,			__lowercase		,			__lowercase )
			lowerCamelCase					:      Any          =   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
			lowerCamelCase					:      List[str]          =   dict(**__lowercase		,			**__lowercase )

			if self.use_past:
					if not is_torch_available():
							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
					else:
							import torch
					lowerCamelCase					:      str          =   common_inputs['''input_ids'''].shape
					lowerCamelCase					:      str          =   common_inputs['''decoder_input_ids'''].shape[1]
					lowerCamelCase					:      List[str]          =   self.num_attention_heads
					lowerCamelCase					:      Optional[Any]          =   (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					lowerCamelCase					:      List[Any]          =   decoder_seq_length + 3
					lowerCamelCase					:      Any          =   (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					lowerCamelCase					:      List[Any]          =   torch.cat(
					    [common_inputs['decoder_attention_mask'], torch.ones(__lowercase		,			__lowercase )]		,			dim=1 )

					lowerCamelCase					:      Optional[Any]          =   []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					lowerCamelCase					:      int          =   self.num_layers
					lowerCamelCase					:      Optional[Any]          =   min(__lowercase		,			__lowercase )
					lowerCamelCase					:      str          =   max(__lowercase		,			__lowercase ) - min_num_layers
					lowerCamelCase					:      Dict          =   '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

					for _ in range(__lowercase ):
							common_inputs["past_key_values"].append(
							    (
							        torch.zeros(__lowercase ),
							        torch.zeros(__lowercase ),
							        torch.zeros(__lowercase ),
							        torch.zeros(__lowercase ),
							    ) )
					# TODO: test this.
					lowerCamelCase					:      Optional[Any]          =   encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
					for _ in range(__lowercase		,			__lowercase ):
							common_inputs["past_key_values"].append((torch.zeros(__lowercase ), torch.zeros(__lowercase )) )
			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			lowerCamelCase					:      str          =   self._generate_dummy_inputs_for_encoder_and_decoder(
			    __lowercase		,			__lowercase		,			__lowercase		,			__lowercase		,			__lowercase )

			if self.use_past:
					if not is_torch_available():
							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
					else:
							import torch
					lowerCamelCase					:      List[str]          =   common_inputs['''input_ids'''].shape
					# Not using the same length for past_key_values
					lowerCamelCase					:      Optional[int]          =   seqlen + 2
					lowerCamelCase					:      List[Any]          =   self.num_layers
					lowerCamelCase					:      Union[str, Any]          =   self.num_attention_heads
					lowerCamelCase					:      Optional[Any]          =   (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					lowerCamelCase					:      Dict          =   common_inputs['''attention_mask'''].dtype
					lowerCamelCase					:      List[str]          =   torch.cat(
					    [common_inputs['attention_mask'], torch.ones(__lowercase		,			__lowercase		,			dtype=__lowercase )]		,			dim=1 )
					lowerCamelCase					:      Any          =   [
					    (torch.zeros(__lowercase ), torch.zeros(__lowercase )) for _ in range(__lowercase )
					]
			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			# Copied from OnnxConfig.generate_dummy_inputs
			# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			lowerCamelCase					:      Tuple          =   compute_effective_axis_dimension(
			    __lowercase		,			fixed_dimension=OnnxConfig.default_fixed_batch		,			num_token_to_add=0 )

			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			lowerCamelCase					:      List[str]          =   tokenizer.num_special_tokens_to_add(__lowercase )
			lowerCamelCase					:      Any          =   compute_effective_axis_dimension(
			    __lowercase		,			fixed_dimension=OnnxConfig.default_fixed_sequence		,			num_token_to_add=__lowercase )

			# Generate dummy inputs according to compute batch and sequence
			lowerCamelCase					:      Optional[Any]          =   [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
			lowerCamelCase					:      str          =   dict(tokenizer(__lowercase		,			return_tensors=__lowercase ) )
			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      Optional[Any]          =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    __lowercase		,			batch_size=__lowercase		,			seq_length=__lowercase		,			is_pair=__lowercase		,			framework=__lowercase )

			else:
					lowerCamelCase					:      Any          =   self._generate_dummy_inputs_for_causal_lm(
					    __lowercase		,			batch_size=__lowercase		,			seq_length=__lowercase		,			is_pair=__lowercase		,			framework=__lowercase )

			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      Tuple          =   super()._flatten_past_key_values_(__lowercase		,			__lowercase		,			__lowercase		,			__lowercase )
			else:
					lowerCamelCase					:      Any          =   super(__lowercase		,			self )._flatten_past_key_values_(
					    __lowercase		,			__lowercase		,			__lowercase		,			__lowercase )




	@property
	def       _UpperCamelCase   (			self )		->							float:
			return 1E-4








"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   OrderedDict()
		for key, value in state_dict.items():
				if key.startswith('module.encoder'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.encoder',						'glpn.encoder'      )
				if key.startswith('module.decoder'      ):
						lowerCamelCase					:      str          =   key.replace('module.decoder',						'decoder.stages'      )
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						lowerCamelCase					:      Any          =   key[key.find('patch_embed'      ) + len('patch_embed'      )]
						lowerCamelCase					:      Dict          =   key.replace(F"""patch_embed{idx}""",						F"""patch_embeddings.{int(a_      )-1}"""      )
				if "norm" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('norm',						'layer_norm'      )
				if "glpn.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						lowerCamelCase					:      List[str]          =   key[key.find('glpn.encoder.layer_norm'      ) + len('glpn.encoder.layer_norm'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""layer_norm{idx}""",						F"""layer_norm.{int(a_      )-1}"""      )
				if "layer_norm1" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('layer_norm1',						'layer_norm_1'      )
				if "layer_norm2" in key:
						lowerCamelCase					:      str          =   key.replace('layer_norm2',						'layer_norm_2'      )
				if "block" in key:
						# replace for example block1 by block.0
						lowerCamelCase					:      Union[str, Any]          =   key[key.find('block'      ) + len('block'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""block{idx}""",						F"""block.{int(a_      )-1}"""      )
				if "attn.q" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('attn.q',						'attention.self.query'      )
				if "attn.proj" in key:
						lowerCamelCase					:      Dict          =   key.replace('attn.proj',						'attention.output.dense'      )
				if "attn" in key:
						lowerCamelCase					:      int          =   key.replace('attn',						'attention.self'      )
				if "fc1" in key:
						lowerCamelCase					:      Any          =   key.replace('fc1',						'dense1'      )
				if "fc2" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('fc2',						'dense2'      )
				if "linear_pred" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('linear_pred',						'classifier'      )
				if "linear_fuse" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('linear_fuse.conv',						'linear_fuse'      )
						lowerCamelCase					:      Optional[int]          =   key.replace('linear_fuse.bn',						'batch_norm'      )
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						lowerCamelCase					:      str          =   key[key.find('linear_c'      ) + len('linear_c'      )]
						lowerCamelCase					:      List[Any]          =   key.replace(F"""linear_c{idx}""",						F"""linear_c.{int(a_      )-1}"""      )
				if "bot_conv" in key:
						lowerCamelCase					:      int          =   key.replace('bot_conv',						'0.convolution'      )
				if "skip_conv1" in key:
						lowerCamelCase					:      Any          =   key.replace('skip_conv1',						'1.convolution'      )
				if "skip_conv2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('skip_conv2',						'2.convolution'      )
				if "fusion1" in key:
						lowerCamelCase					:      str          =   key.replace('fusion1',						'1.fusion'      )
				if "fusion2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('fusion2',						'2.fusion'      )
				if "fusion3" in key:
						lowerCamelCase					:      List[str]          =   key.replace('fusion3',						'3.fusion'      )
				if "fusion" in key and "conv" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('conv',						'convolutional_layer'      )
				if key.startswith('module.last_layer_depth'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.last_layer_depth',						'head.head'      )
				lowerCamelCase					:      List[Any]          =   value

		return new_state_dict
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		for i in range(config.num_encoder_blocks      ):
				for j in range(config.depths[i]      ):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						lowerCamelCase					:      Any          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
						lowerCamelCase					:      Optional[Any]          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""      )
						# next, add keys and values (in that order) to the state dict
						lowerCamelCase					:      Any          =   kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase					:      Dict          =   kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[config.hidden_sizes[i] :]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      List[Any]          =   Image.open(requests.get(a_,						stream=a_      ).raw      )

		return image
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_=False,						a_=None      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   GLPNConfig(hidden_sizes=[64, 128, 320, 512],						decoder_hidden_size=64,						depths=[3, 8, 27, 3]      )

		# load image processor (only resize + rescale)
		lowerCamelCase					:      Any          =   GLPNImageProcessor()

		# prepare image
		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      Tuple          =   image_processor(images=a_,						return_tensors='pt'      ).pixel_values

		logger.info('Converting model...'      )

		# load original state dict
		lowerCamelCase					:      Optional[Any]          =   torch.load(a_,						map_location=torch.device('cpu'      )      )

		# rename keys
		lowerCamelCase					:      Any          =   rename_keys(a_      )

		# key and value matrices need special treatment
		read_in_k_v(a_,						a_      )

		# create HuggingFace model and load state dict
		lowerCamelCase					:      Optional[int]          =   GLPNForDepthEstimation(a_      )
		model.load_state_dict(a_      )
		model.eval()

		# forward pass
		lowerCamelCase					:      str          =   model(a_      )
		lowerCamelCase					:      Any          =   outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCamelCase					:      Any          =   torch.tensor(
						    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]      )
				elif "kitti" in model_name:
						lowerCamelCase					:      str          =   torch.tensor(
						    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      )
				else:
						raise ValueError(F"""Unknown model name: {model_name}"""      )

				lowerCamelCase					:      int          =   torch.Size([1, 480, 640]      )

				assert predicted_depth.shape == expected_shape
				assert torch.allclose(predicted_depth[0, :3, :3],						a_,						atol=1E-4      )
				print('Looks ok!'      )

		# finally, push to hub if required
		if push_to_hub:
				logger.info('Pushing model and image processor to the hub...'      )
				model.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add model',						use_temp_dir=a_,						)
				image_processor.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add image processor',						use_temp_dir=a_,						)


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path',
					    default=None,
					    type=str,
					    help='Path to the original PyTorch checkpoint (.pth file).',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
					)
					parser.add_argument(
					    '--model_name',
					    default='glpn-kitti',
					    type=str,
					    help='Name of the model in case you\'re pushing to the hub.',
					)
					_A						=			parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






