
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,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowercase  : Optional[int]						   =							TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowercase  : List[str]						   =							TaTokenizerFast

lowercase  : Any						   =							{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  : Dict						   =							[
		    """MT5EncoderModel""",
		    """MT5ForConditionalGeneration""",
		    """MT5ForQuestionAnswering""",
		    """MT5Model""",
		    """MT5PreTrainedModel""",
		    """MT5Stack""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  : List[Any]						   =							["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  : Tuple						   =							["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		lowercase  : Union[str, Any]						   =							_LazyModule(
		    __name__,
		    globals()["""__file__"""],
		    _import_structure,
		    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
		    module_spec=__spec__,
		)


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







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

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       for key in orig_state_dict.copy().keys():
              lowercase							:					List[Any]	     =				orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

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

       return orig_state_dict







def 						_snake_case(  )			->   List[Any]:
       lowercase							:					Tuple	     =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:					Any	     =				Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,     stream=SCREAMING_SNAKE_CASE__    ).raw    )
       return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__="groupvit-gcc-yfcc" ,     SCREAMING_SNAKE_CASE__=False    )			->   str:
       lowercase							:					Dict	     =				GroupViTConfig()
       lowercase							:					Tuple	     =				GroupViTModel(SCREAMING_SNAKE_CASE__    ).eval()

       lowercase							:					List[str]	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )["""model"""]
       lowercase							:					Optional[Any]	     =				convert_state_dict(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					int	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE__    ) == 0)

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

       with torch.no_grad():
              lowercase							:					Optional[Any]	     =				model(**SCREAMING_SNAKE_CASE__    )

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

       processor.save_pretrained(SCREAMING_SNAKE_CASE__    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )
       print("""Successfully saved processor and model to""" ,     SCREAMING_SNAKE_CASE__    )

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


if __name__ == "__main__":
  lowercase  : List[Any]						   =							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`.""",
  )
  lowercase  : Union[str, Any]						   =							parser.parse_args()

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