

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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__="attention"      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






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

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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					*, UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							traverse_util.flatten_dict(variables['target']      )
					__lowerCAmelCase					=							{'/'.join(UpperCAmelCase__      ): v for k, v in old.items()}

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

					__lowerCAmelCase					=							collections.OrderedDict()

					# Shared embeddings.
					__lowerCAmelCase					=							old['token_embedder/embedding']

					# Encoder.
					for i in range(UpperCAmelCase__      ):
										# Block i, layer 0 (Self Attention).
										__lowerCAmelCase					=							tax_layer_norm_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'encoder'		,					'pre_attention_layer_norm'      )
										__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							tax_attention_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'encoder'		,					'attention'      )
										__lowerCAmelCase					=							layer_norm
										__lowerCAmelCase					=							k.T
										__lowerCAmelCase					=							o.T
										__lowerCAmelCase					=							q.T
										__lowerCAmelCase					=							v.T

										# Block i, layer 1 (MLP).
										__lowerCAmelCase					=							tax_layer_norm_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'encoder'		,					'pre_mlp_layer_norm'      )
										__lowerCAmelCase,	__lowerCAmelCase					=							tax_mlp_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'encoder'		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							layer_norm
										if split_mlp_wi:
															__lowerCAmelCase					=							wi[0].T
															__lowerCAmelCase					=							wi[1].T
										else:
															__lowerCAmelCase					=							wi.T
										__lowerCAmelCase					=							wo.T

					__lowerCAmelCase					=							old[
					    'encoder/relpos_bias/rel_embedding'
					].T
					__lowerCAmelCase					=							old['encoder/encoder_norm/scale']

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

															# Block i, layer 1 (Cross Attention).
															__lowerCAmelCase					=							tax_layer_norm_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'decoder'		,					'pre_cross_attention_layer_norm'      )
															__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							tax_attention_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'decoder'		,					'encoder_decoder_attention'      )
															__lowerCAmelCase					=							layer_norm
															__lowerCAmelCase					=							k.T
															__lowerCAmelCase					=							o.T
															__lowerCAmelCase					=							q.T
															__lowerCAmelCase					=							v.T

															# Block i, layer 2 (MLP).
															__lowerCAmelCase					=							tax_layer_norm_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'decoder'		,					'pre_mlp_layer_norm'      )
															__lowerCAmelCase,	__lowerCAmelCase					=							tax_mlp_lookup(UpperCAmelCase__		,					UpperCAmelCase__		,					'decoder'		,					UpperCAmelCase__      )
															__lowerCAmelCase					=							layer_norm
															if split_mlp_wi:
																				__lowerCAmelCase					=							wi[0].T
																				__lowerCAmelCase					=							wi[1].T
															else:
																				__lowerCAmelCase					=							wi.T
															__lowerCAmelCase					=							wo.T

										__lowerCAmelCase					=							old['decoder/decoder_norm/scale']
										__lowerCAmelCase					=							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:
															__lowerCAmelCase					=							old['decoder/logits_dense/kernel'].T

					return new


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							state_dict['shared.weight']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															__lowerCAmelCase					=							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.'      )
															__lowerCAmelCase					=							state_dict['shared.weight']

					return state_dict


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							checkpoints.load_tax_checkpoint(UpperCAmelCase__      )
					__lowerCAmelCase					=							convert_tax_to_pytorch(UpperCAmelCase__		,					num_layers=config.num_layers		,					is_encoder_only=UpperCAmelCase__      )
					__lowerCAmelCase					=							make_state_dict(UpperCAmelCase__		,					UpperCAmelCase__      )
					model.load_state_dict(UpperCAmelCase__		,					strict=UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__ = False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							TaConfig.from_json_file(UpperCAmelCase__      )
					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:
										__lowerCAmelCase					=							TaEncoderModel(UpperCAmelCase__      )
					else:
										__lowerCAmelCase					=							TaForConditionalGeneration(UpperCAmelCase__      )

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

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

					# Verify that we can load the checkpoint.
					model.from_pretrained(UpperCAmelCase__      )
					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
		)









import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if "img_encoder.pos_embed" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.pos_embed'		,					'vision_model.embeddings.position_embeddings'      )
					if "img_encoder.patch_embed.proj" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.patch_embed.proj'		,					'vision_model.embeddings.patch_embeddings.projection'      )
					if "img_encoder.patch_embed.norm" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.patch_embed.norm'		,					'vision_model.embeddings.layernorm'      )
					if "img_encoder.layers" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.layers'		,					'vision_model.encoder.stages'      )
					if "blocks" in name and "res" not in name:
										__lowerCAmelCase					=							name.replace('blocks'		,					'layers'      )
					if "attn" in name and "pre_assign" not in name:
										__lowerCAmelCase					=							name.replace('attn'		,					'self_attn'      )
					if "proj" in name and "self_attn" in name and "text" not in name:
										__lowerCAmelCase					=							name.replace('proj'		,					'out_proj'      )
					if "pre_assign_attn.attn.proj" in name:
										__lowerCAmelCase					=							name.replace('pre_assign_attn.attn.proj'		,					'pre_assign_attn.attn.out_proj'      )
					if "norm1" in name:
										__lowerCAmelCase					=							name.replace('norm1'		,					'layer_norm1'      )
					if "norm2" in name and "pre_assign" not in name:
										__lowerCAmelCase					=							name.replace('norm2'		,					'layer_norm2'      )
					if "img_encoder.norm" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.norm'		,					'vision_model.layernorm'      )
					# text encoder
					if "text_encoder.token_embedding" in name:
										__lowerCAmelCase					=							name.replace('text_encoder.token_embedding'		,					'text_model.embeddings.token_embedding'      )
					if "text_encoder.positional_embedding" in name:
										__lowerCAmelCase					=							name.replace('text_encoder.positional_embedding'		,					'text_model.embeddings.position_embedding.weight'      )
					if "text_encoder.transformer.resblocks." in name:
										__lowerCAmelCase					=							name.replace('text_encoder.transformer.resblocks.'		,					'text_model.encoder.layers.'      )
					if "ln_1" in name:
										__lowerCAmelCase					=							name.replace('ln_1'		,					'layer_norm1'      )
					if "ln_2" in name:
										__lowerCAmelCase					=							name.replace('ln_2'		,					'layer_norm2'      )
					if "c_fc" in name:
										__lowerCAmelCase					=							name.replace('c_fc'		,					'fc1'      )
					if "c_proj" in name:
										__lowerCAmelCase					=							name.replace('c_proj'		,					'fc2'      )
					if "text_encoder" in name:
										__lowerCAmelCase					=							name.replace('text_encoder'		,					'text_model'      )
					if "ln_final" in name:
										__lowerCAmelCase					=							name.replace('ln_final'		,					'final_layer_norm'      )
					# projection layers
					if "img_projector.linear_hidden." in name:
										__lowerCAmelCase					=							name.replace('img_projector.linear_hidden.'		,					'visual_projection.'      )
					if "img_projector.linear_out." in name:
										__lowerCAmelCase					=							name.replace('img_projector.linear_out.'		,					'visual_projection.3.'      )
					if "text_projector.linear_hidden" in name:
										__lowerCAmelCase					=							name.replace('text_projector.linear_hidden'		,					'text_projection'      )
					if "text_projector.linear_out" in name:
										__lowerCAmelCase					=							name.replace('text_projector.linear_out'		,					'text_projection.3'      )

					return name


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase					=							orig_state_dict.pop(UpperCAmelCase__      )

										if "qkv" in key:
															# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__lowerCAmelCase					=							key.split('.'      )
															__lowerCAmelCase,	__lowerCAmelCase					=							int(key_split[2]      ), int(key_split[4]      )
															__lowerCAmelCase					=							config.vision_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[dim : dim * 2, :]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							val[-dim:]
										elif "in_proj" in key:
															# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__lowerCAmelCase					=							key.split('.'      )
															__lowerCAmelCase					=							int(key_split[3]      )
															__lowerCAmelCase					=							config.text_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[
																				    dim : dim * 2, :
																				]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							val[-dim:]
										else:
															__lowerCAmelCase					=							rename_key(UpperCAmelCase__      )
															# squeeze if necessary
															if (
															    "text_projection.0" in new_name
															    or "text_projection.3" in new_name
															    or "visual_projection.0" in new_name
															    or "visual_projection.3" in new_name
															):
																				__lowerCAmelCase					=							val.squeeze_()
															else:
																				__lowerCAmelCase					=							val

					return orig_state_dict


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							'http://images.cocodataset.org/val2017/000000039769.jpg'
					__lowerCAmelCase					=							Image.open(requests.get(UpperCAmelCase__		,					stream=UpperCAmelCase__      ).raw      )
					return im


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__="groupvit-gcc-yfcc"		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							GroupViTConfig()
					__lowerCAmelCase					=							GroupViTModel(UpperCAmelCase__      ).eval()

					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location='cpu'      )['model']
					__lowerCAmelCase					=							convert_state_dict(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase,	__lowerCAmelCase					=							model.load_state_dict(UpperCAmelCase__		,					strict=UpperCAmelCase__      )
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase__      ) == 0)

					# verify result
					__lowerCAmelCase					=							CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'      )
					__lowerCAmelCase					=							prepare_img()
					__lowerCAmelCase					=							processor(text=['a photo of a cat', 'a photo of a dog']		,					images=UpperCAmelCase__		,					padding=UpperCAmelCase__		,					return_tensors='pt'      )

					with torch.no_grad():
										__lowerCAmelCase					=							model(**UpperCAmelCase__      )

					if model_name == "groupvit-gcc-yfcc":
										__lowerCAmelCase					=							torch.tensor([[13.3523, 6.3629]]      )
					elif model_name == "groupvit-gcc-redcaps":
										__lowerCAmelCase					=							torch.tensor([[16.1873, 8.6230]]      )
					else:
										raise ValueError(F"""Model name {model_name} not supported."""      )
					assert torch.allclose(outputs.logits_per_image		,					UpperCAmelCase__		,					atol=1E-3      )

					processor.save_pretrained(UpperCAmelCase__      )
					model.save_pretrained(UpperCAmelCase__      )
					print('Successfully saved processor and model to'		,					UpperCAmelCase__      )

					if push_to_hub:
										print('Pushing to the hub...'      )
										processor.push_to_hub(UpperCAmelCase__		,					organization='nielsr'      )
										model.push_to_hub(UpperCAmelCase__		,					organization='nielsr'      )


if __name__ == "__main__":
		lowerCamelCase      				=     argparse.ArgumentParser()
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
		)
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
		parser.add_argument(
		    '''--model_name''',
		    default='''groupvit-gccy-fcc''',
		    type=str,
		    help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
		)
		lowerCamelCase      				=     parser.parse_args()

		convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)






