import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput
class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     int ,			_A      :     Optional[int] ,			_A      :     Dict ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     dataset
												_UpperCamelCase			     =     process
												_UpperCamelCase			     =     params







							def __len__(   self      :     Optional[Any]		):
												return len(self.dataset		)







							def __getitem__(   self      :     Any ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     self.dataset[i]
												_UpperCamelCase			     =     self.process(_A ,			**self.params		)
												return processed




class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     List[str] ,			_A      :     Union[str, Any] ,			_A      :     List[Any] ,			_A      :     Optional[int] ,			_A      :     Any=None		):
												_UpperCamelCase			     =     loader
												_UpperCamelCase			     =     infer
												_UpperCamelCase			     =     params
												if loader_batch_size == 1:
																	# Let's spare some time by deactivating altogether
																	_UpperCamelCase			     =     None
												_UpperCamelCase			     =     loader_batch_size

												# Internal bookkeeping
												_UpperCamelCase			     =     None
												_UpperCamelCase			     =     None







							def __len__(   self      :     str		):
												return len(self.loader		)







							def __iter__(   self      :     str		):
												_UpperCamelCase			     =     iter(self.loader		)
												return self







							def  UpperCamelCase_						(   self      :     List[str]		):
												if isinstance(self._loader_batch_data ,			torch.Tensor		):
																	# Batch data is simple tensor, just fetch the slice
																	_UpperCamelCase			     =     self._loader_batch_data[self._loader_batch_index]
												else:
																	# Batch data is assumed to be BaseModelOutput (or dict)
																	_UpperCamelCase			     =     {}
																	for k, element in self._loader_batch_data.items():
																						if isinstance(_A ,			_A		):
																											# Convert ModelOutput to tuple first
																											_UpperCamelCase			     =     element.to_tuple()
																											if isinstance(element[0] ,			torch.Tensor		):
																																_UpperCamelCase			     =     tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																											elif isinstance(element[0] ,			np.ndarray		):
																																_UpperCamelCase			     =     tuple(np.expand_dims(el[self._loader_batch_index] ,			0		) for el in element		)
																											continue
																						if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_A ,			_A		):
																											# Those are stored as lists of tensors so need specific unbatching.
																											if isinstance(element[0] ,			torch.Tensor		):
																																_UpperCamelCase			     =     tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																											elif isinstance(element[0] ,			np.ndarray		):
																																_UpperCamelCase			     =     tuple(np.expand_dims(el[self._loader_batch_index] ,			0		) for el in element		)
																											continue
																						if element is None:
																											# This can happen for optional data that get passed around
																											_UpperCamelCase			     =     None
																						elif isinstance(element[self._loader_batch_index] ,			torch.Tensor		):
																											# Take correct batch data, but make it looked like batch_size=1
																											# For compatibility with other methods within transformers

																											_UpperCamelCase			     =     element[self._loader_batch_index].unsqueeze(0		)
																						elif isinstance(element[self._loader_batch_index] ,			np.ndarray		):
																											# Take correct batch data, but make it looked like batch_size=1
																											# For compatibility with other methods within transformers
																											_UpperCamelCase			     =     np.expand_dims(element[self._loader_batch_index] ,			0		)
																						else:
																											# This is typically a list, so no need to `unsqueeze`.
																											_UpperCamelCase			     =     element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																	_UpperCamelCase			     =     self._loader_batch_data.__class__(_A		)
												self._loader_batch_index += 1
												return result







							def  UpperCamelCase_						(   self      :     Dict		):
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																	# We are currently unrolling a batch so we just need to return
																	# the current item within a batch
																	return self.loader_batch_item()

												# We're out of items within a batch
												_UpperCamelCase			     =     next(self.iterator		)
												_UpperCamelCase			     =     self.infer(_A ,			**self.params		)
												# We now have a batch of "inferred things".
												if self.loader_batch_size is not None:
																	# Try to infer the size of the batch
																	if isinstance(_A ,			torch.Tensor		):
																						_UpperCamelCase			     =     processed
																	else:
																						_UpperCamelCase			     =     list(processed.keys()		)[0]
																						_UpperCamelCase			     =     processed[key]
																	if isinstance(_A ,			_A		):
																						_UpperCamelCase			     =     len(_A		)
																	else:
																						_UpperCamelCase			     =     first_tensor.shape[0]
																	if 0 < observed_batch_size < self.loader_batch_size:
																						# could be last batch so we can't unroll as many
																						# elements.
																						_UpperCamelCase			     =     observed_batch_size
																	# Setting internal index to unwrap the batch
																	_UpperCamelCase			     =     processed
																	_UpperCamelCase			     =     0
																	return self.loader_batch_item()
												else:
																	# We're not unrolling batches
																	return processed




class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     str ,			_A      :     Dict ,			_A      :     List[str] ,			_A      :     str ,			_A      :     int=None		):
												super().__init__(_A ,			_A ,			_A		)







							def __iter__(   self      :     Optional[int]		):
												_UpperCamelCase			     =     iter(self.loader		)
												_UpperCamelCase			     =     None
												return self







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												if self.subiterator is None:
																	_UpperCamelCase			     =     self.infer(next(self.iterator		) ,			**self.params		)
												try:
																	# Try to return next item
																	_UpperCamelCase			     =     next(self.subiterator		)
												except StopIteration:
																	# When a preprocess iterator ends, we can start lookig at the next item
																	# ChunkIterator will keep feeding until ALL elements of iterator
																	# all have created their subiterator and have been iterating against.
																	#
																	# Another way to look at it, is we're basically flattening lists of lists
																	# into a single list, but with generators
																	_UpperCamelCase			     =     self.infer(next(self.iterator		) ,			**self.params		)
																	_UpperCamelCase			     =     next(self.subiterator		)
												return processed




class     lowerCAmelCase_     (       __lowercase ):







							def __iter__(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     iter(self.loader		)
												return self







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												# Extremely similar to PipelineIterator in its unpacking mechanism
												# BUT, we have an extra required item which is the presence of `is_last`
												# That is because everything is flattened by `PipelineChunkIterator` we
												# need to keep track of how to regroup here in the original `process`
												# boundaries so that `process` and `postprocess` see the same data.

												# This iterator accumulates items (possibly while unbatching) until it
												# its a `is_last` and then just passes it on to the caller.
												_UpperCamelCase			     =     False
												_UpperCamelCase			     =     []
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																	while self._loader_batch_index < self.loader_batch_size:
																						_UpperCamelCase			     =     self.loader_batch_item()
																						_UpperCamelCase			     =     item.pop('''is_last'''		)
																						accumulator.append(_A		)
																						if is_last:
																											return accumulator

												while not is_last:
																	_UpperCamelCase			     =     self.infer(next(self.iterator		) ,			**self.params		)
																	if self.loader_batch_size is not None:
																						if isinstance(_A ,			torch.Tensor		):
																											_UpperCamelCase			     =     processed
																						else:
																											_UpperCamelCase			     =     list(processed.keys()		)[0]
																											_UpperCamelCase			     =     processed[key]
																						if isinstance(_A ,			_A		):
																											_UpperCamelCase			     =     len(_A		)
																						else:
																											_UpperCamelCase			     =     first_tensor.shape[0]
																						if 0 < observed_batch_size < self.loader_batch_size:
																											# could be last batch so we can't unroll as many
																											# elements.
																											_UpperCamelCase			     =     observed_batch_size
																						_UpperCamelCase			     =     processed
																						_UpperCamelCase			     =     0
																						while self._loader_batch_index < self.loader_batch_size:
																											_UpperCamelCase			     =     self.loader_batch_item()
																											_UpperCamelCase			     =     item.pop('''is_last'''		)
																											accumulator.append(_A		)
																											if is_last:
																																return accumulator
																	else:
																						_UpperCamelCase			     =     processed
																						_UpperCamelCase			     =     item.pop('''is_last'''		)
																						accumulator.append(_A		)
												return accumulator




class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     List[str] ,			_A      :     Dataset ,			_A      :     str		):
												_UpperCamelCase			     =     dataset
												_UpperCamelCase			     =     key







							def __len__(   self      :     List[str]		):
												return len(self.dataset		)







							def __getitem__(   self      :     Tuple ,			_A      :     Union[str, Any]		):
												return self.dataset[i][self.key]




class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     List[Any] ,			_A      :     Dataset ,			_A      :     str ,			_A      :     str		):
												_UpperCamelCase			     =     dataset
												_UpperCamelCase			     =     keya
												_UpperCamelCase			     =     keya







							def __len__(   self      :     List[str]		):
												return len(self.dataset		)







							def __getitem__(   self      :     Optional[int] ,			_A      :     Any		):
												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

    return name





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    for key in orig_state_dict.copy().keys():
        snake_case__    =					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
            snake_case__    =					key.split("."				)
            snake_case__    ,       snake_case__    =					int(key_split[2]				), int(key_split[4]				)
            snake_case__    =					config.vision_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[dim : dim * 2, :]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					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
            snake_case__    =					key.split("."				)
            snake_case__    =					int(key_split[3]				)
            snake_case__    =					config.text_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[
                    dim : dim * 2, :
                ]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					val[-dim:]
        else:
            snake_case__    =					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
            ):
                snake_case__    =					val.squeeze_()
            else:
                snake_case__    =					val

    return orig_state_dict





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case__    =					Image.open(requests.get(__lowerCAmelCase     ,		stream=__lowerCAmelCase				).raw				)
    return im





@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase="groupvit-gcc-yfcc"     ,		__lowerCAmelCase=False				):
    snake_case__    =					GroupViTConfig()
    snake_case__    =					GroupViTModel(__lowerCAmelCase				).eval()

    snake_case__    =					torch.load(__lowerCAmelCase     ,		map_location="cpu"				)["model"]
    snake_case__    =					convert_state_dict(__lowerCAmelCase     ,		__lowerCAmelCase				)
    snake_case__    ,       snake_case__    =					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
    snake_case__    =					CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"				)
    snake_case__    =					prepare_img()
    snake_case__    =					processor(text=["a photo of a cat", "a photo of a dog"]     ,		images=__lowerCAmelCase     ,		padding=__lowerCAmelCase     ,		return_tensors="pt"				)

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

    if model_name == "groupvit-gcc-yfcc":
        snake_case__    =					torch.tensor([[13.3_523, 6.3_629]]				)
    elif model_name == "groupvit-gcc-redcaps":
        snake_case__    =					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__":
     __magic_name__  							=	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`.''',
     )
     __magic_name__  							=	parser.parse_args()

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



