import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def       A	(    _lowercase						):
						# vision encoder
						if "img_encoder.pos_embed" in name:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							name.replace('''img_encoder.pos_embed'''		,  '''vision_model.embeddings.position_embeddings'''						)
						if "img_encoder.patch_embed.proj" in name:
												SCREAMING_SNAKE_CASE							:    Dict							=							name.replace('''img_encoder.patch_embed.proj'''		,  '''vision_model.embeddings.patch_embeddings.projection'''						)
						if "img_encoder.patch_embed.norm" in name:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name.replace('''img_encoder.patch_embed.norm'''		,  '''vision_model.embeddings.layernorm'''						)
						if "img_encoder.layers" in name:
												SCREAMING_SNAKE_CASE							:    int							=							name.replace('''img_encoder.layers'''		,  '''vision_model.encoder.stages'''						)
						if "blocks" in name and "res" not in name:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							name.replace('''blocks'''		,  '''layers'''						)
						if "attn" in name and "pre_assign" not in name:
												SCREAMING_SNAKE_CASE							:    Dict							=							name.replace('''attn'''		,  '''self_attn'''						)
						if "proj" in name and "self_attn" in name and "text" not in name:
												SCREAMING_SNAKE_CASE							:    List[str]							=							name.replace('''proj'''		,  '''out_proj'''						)
						if "pre_assign_attn.attn.proj" in name:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							name.replace('''pre_assign_attn.attn.proj'''		,  '''pre_assign_attn.attn.out_proj'''						)
						if "norm1" in name:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name.replace('''norm1'''		,  '''layer_norm1'''						)
						if "norm2" in name and "pre_assign" not in name:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							name.replace('''norm2'''		,  '''layer_norm2'''						)
						if "img_encoder.norm" in name:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							name.replace('''img_encoder.norm'''		,  '''vision_model.layernorm'''						)
						# text encoder
						if "text_encoder.token_embedding" in name:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name.replace('''text_encoder.token_embedding'''		,  '''text_model.embeddings.token_embedding'''						)
						if "text_encoder.positional_embedding" in name:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							name.replace('''text_encoder.positional_embedding'''		,  '''text_model.embeddings.position_embedding.weight'''						)
						if "text_encoder.transformer.resblocks." in name:
												SCREAMING_SNAKE_CASE							:    Tuple							=							name.replace('''text_encoder.transformer.resblocks.'''		,  '''text_model.encoder.layers.'''						)
						if "ln_1" in name:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name.replace('''ln_1'''		,  '''layer_norm1'''						)
						if "ln_2" in name:
												SCREAMING_SNAKE_CASE							:    List[str]							=							name.replace('''ln_2'''		,  '''layer_norm2'''						)
						if "c_fc" in name:
												SCREAMING_SNAKE_CASE							:    int							=							name.replace('''c_fc'''		,  '''fc1'''						)
						if "c_proj" in name:
												SCREAMING_SNAKE_CASE							:    str							=							name.replace('''c_proj'''		,  '''fc2'''						)
						if "text_encoder" in name:
												SCREAMING_SNAKE_CASE							:    int							=							name.replace('''text_encoder'''		,  '''text_model'''						)
						if "ln_final" in name:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							name.replace('''ln_final'''		,  '''final_layer_norm'''						)
						# projection layers
						if "img_projector.linear_hidden." in name:
												SCREAMING_SNAKE_CASE							:    Any							=							name.replace('''img_projector.linear_hidden.'''		,  '''visual_projection.'''						)
						if "img_projector.linear_out." in name:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							name.replace('''img_projector.linear_out.'''		,  '''visual_projection.3.'''						)
						if "text_projector.linear_hidden" in name:
												SCREAMING_SNAKE_CASE							:    List[str]							=							name.replace('''text_projector.linear_hidden'''		,  '''text_projection'''						)
						if "text_projector.linear_out" in name:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							name.replace('''text_projector.linear_out'''		,  '''text_projection.3'''						)

						return name
def       A	(    _lowercase		,  _lowercase						):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:    Any							=							orig_state_dict.pop(_lowercase						)

												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
																		SCREAMING_SNAKE_CASE							:    Tuple							=							key.split('''.'''						)
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							int(key_split[2]						), int(key_split[4]						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							config.vision_config.hidden_size
																		if "weight" in key:
																								SCREAMING_SNAKE_CASE							:    Any							=							val[:dim, :]
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							val[dim : dim * 2, :]
																								SCREAMING_SNAKE_CASE							:    Any							=							val[-dim:, :]
																		else:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							val[:dim]
																								SCREAMING_SNAKE_CASE							:    str							=							val[dim : dim * 2]
																								SCREAMING_SNAKE_CASE							:    str							=							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
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							key.split('''.'''						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							int(key_split[3]						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							config.text_config.hidden_size
																		if "weight" in key:
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							val[:dim, :]
																								SCREAMING_SNAKE_CASE							:    int							=							val[
																								    dim : dim * 2, :
																								]
																								SCREAMING_SNAKE_CASE							:    Any							=							val[-dim:, :]
																		else:
																								SCREAMING_SNAKE_CASE							:    int							=							val[:dim]
																								SCREAMING_SNAKE_CASE							:    Tuple							=							val[dim : dim * 2]
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							val[-dim:]
												else:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							rename_key(_lowercase						)
																		# 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
																		):
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							val.squeeze_()
																		else:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							val

						return orig_state_dict
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							Image.open(requests.get(_lowercase		,  stream=_lowercase						).raw						)
						return im
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase		,  _lowercase="groupvit-gcc-yfcc"		,  _lowercase=False						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							GroupViTConfig()
						SCREAMING_SNAKE_CASE							:    str							=							GroupViTModel(_lowercase						).eval()

						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.load(_lowercase		,  map_location='''cpu'''						)['''model''']
						SCREAMING_SNAKE_CASE							:    int							=							convert_state_dict(_lowercase		,  _lowercase						)
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							model.load_state_dict(_lowercase		,  strict=_lowercase						)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowercase						) == 0)

						# verify result
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							prepare_img()
						SCREAMING_SNAKE_CASE							:    Any							=							processor(text=['''a photo of a cat''', '''a photo of a dog''']		,  images=_lowercase		,  padding=_lowercase		,  return_tensors='''pt'''						)

						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(**_lowercase						)

						if model_name == "groupvit-gcc-yfcc":
												SCREAMING_SNAKE_CASE							:    Dict							=							torch.tensor([[13.3523, 6.3629]]						)
						elif model_name == "groupvit-gcc-redcaps":
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.tensor([[16.1873, 8.6230]]						)
						else:
												raise ValueError(f"""Model name {model_name} not supported."""						)
						assert torch.allclose(outputs.logits_per_image		,  _lowercase		,  atol=1e-3						)

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

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


if __name__ == "__main__":
					__UpperCamelCase		:       Dict									=     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`.',
					)
					__UpperCamelCase		:       List[str]									=     parser.parse_args()

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase		:       Any									=     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     ['MBartTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Dict									=     ['MBartTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MBartForCausalLM',
					    'MBartForConditionalGeneration',
					    'MBartForQuestionAnswering',
					    'MBartForSequenceClassification',
					    'MBartModel',
					    'MBartPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'TFMBartForConditionalGeneration',
					    'TFMBartModel',
					    'TFMBartPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'FlaxMBartForConditionalGeneration',
					    'FlaxMBartForQuestionAnswering',
					    'FlaxMBartForSequenceClassification',
					    'FlaxMBartModel',
					    'FlaxMBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart import MBartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart_fast import MBartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mbart import (
										    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MBartForCausalLM,
										    MBartForConditionalGeneration,
										    MBartForQuestionAnswering,
										    MBartForSequenceClassification,
										    MBartModel,
										    MBartPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mbart import (
										    FlaxMBartForConditionalGeneration,
										    FlaxMBartForQuestionAnswering,
										    FlaxMBartForSequenceClassification,
										    FlaxMBartModel,
										    FlaxMBartPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





