
"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def  lowercase						(	lowerCAmelCase__						):
			# vision encoder
			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						(	lowerCAmelCase__	,lowerCAmelCase__						):
			for key in orig_state_dict.copy().keys():
						lowerCamelCase_					=  orig_state_dict.pop(lowerCAmelCase__						)

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






@torch.no_grad()
def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__="groupvit-gcc-yfcc"	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  GroupViTConfig()
			lowerCamelCase_					=  GroupViTModel(lowerCAmelCase__						).eval()

			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			lowerCamelCase_					=  convert_state_dict(lowerCAmelCase__	,lowerCAmelCase__						)
			lowerCamelCase_		, lowerCamelCase_					=  model.load_state_dict(lowerCAmelCase__	,strict=lowerCAmelCase__						)
			assert missing_keys == ["text_model.embeddings.position_ids"]
			assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase__						) == 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=lowerCAmelCase__	,padding=lowerCAmelCase__	,return_tensors='''pt'''						)

			with torch.no_grad():
						lowerCamelCase_					=  model(**lowerCAmelCase__						)

			if model_name == "groupvit-gcc-yfcc":
						lowerCamelCase_					=  torch.tensor([[13.3_523, 6.3_629]]						)
			elif model_name == "groupvit-gcc-redcaps":
						lowerCamelCase_					=  torch.tensor([[16.1_873, 8.6_230]]						)
			else:
						raise ValueError(f"Model name {model_name} not supported."						)
			assert torch.allclose(outputs.logits_per_image	,lowerCAmelCase__	,atol=1E-3						)

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

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


if __name__ == "__main__":
						A_    							=					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`.""",
						)
						A_    							=					parser.parse_args()

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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



