



import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)


def 							__A(lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	checkpoints.load_tax_checkpoint(__snake_case )
		_UpperCamelCase						    =	flatten_dict(__snake_case )
		return flax_params


def 							__A(lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	{}

		_UpperCamelCase						    =	{
		    """token_embedder""": """embeddings""",
		    """encoder_norm""": """layernorm""",
		    """kernel""": """weight""",
		    """.out""": """.output""",
		    """scale""": """weight""",
		    """embedders_0.pos_embedding""": """row_embedder.weight""",
		    """embedders_1.pos_embedding""": """column_embedder.weight""",
		}

		_UpperCamelCase						    =	{
		    """query""": """attention.query""",
		    """key""": """attention.key""",
		    """value""": """attention.value""",
		    """output.dense""": """output""",
		    """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
		    """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
		    """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
		    """mlp.""": """mlp.DenseReluDense.""",
		    """pre_mlp_layer_norm""": """mlp.layer_norm""",
		    """self_attention.o""": """self_attention.attention.o""",
		    """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
		    """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
		    """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
		    """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
		}

		for key in flax_dict.keys():
				if "target" in key:
						# remove the first prefix from the key
						_UpperCamelCase						    =	""".""".join(key[1:] )

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								_UpperCamelCase						    =	new_key.replace(__snake_case						,  __snake_case )

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										_UpperCamelCase						    =	new_key.replace(__snake_case						,  __snake_case )

						if "layers" in new_key and "decoder" not in new_key:
								# use regex to replace the layer number
								_UpperCamelCase						    =	re.sub(R"""layers_(\d+)"""						,  R"""layer.\1"""						,  __snake_case )
								_UpperCamelCase						    =	new_key.replace("""encoder"""						,  """encoder.encoder""" )

						elif "layers" in new_key and "decoder" in new_key:
								# use regex to replace the layer number
								_UpperCamelCase						    =	re.sub(R"""layers_(\d+)"""						,  R"""layer.\1"""						,  __snake_case )

						_UpperCamelCase						    =	flax_dict[key]

		_UpperCamelCase						    =	{}
		# convert converted_dict into torch format
		for key in converted_dict.keys():
				if ("embed_tokens" not in key) and ("embedder" not in key):
						_UpperCamelCase						    =	torch.from_numpy(converted_dict[key].T )
				else:
						_UpperCamelCase						    =	torch.from_numpy(converted_dict[key] )

		return converted_torch_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=False						,  lowerCAmelCase=False )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	get_flax_param(__snake_case )

		if not use_large:
				_UpperCamelCase						    =	PixaStructVisionConfig()
				_UpperCamelCase						    =	PixaStructTextConfig()
		else:
				_UpperCamelCase						    =	PixaStructVisionConfig(
				    hidden_size=1_5_3_6						,  d_ff=3_9_6_8						,  num_attention_heads=2_4						,  num_hidden_layers=1_8 )
				_UpperCamelCase						    =	PixaStructTextConfig(hidden_size=1_5_3_6						,  d_ff=3_9_6_8						,  num_heads=2_4						,  num_layers=1_8 )
		_UpperCamelCase						    =	PixaStructConfig(
		    vision_config=encoder_config.to_dict()						,  text_config=decoder_config.to_dict()						,  is_vqa=__snake_case )

		_UpperCamelCase						    =	PixaStructForConditionalGeneration(__snake_case )

		_UpperCamelCase						    =	rename_and_convert_flax_params(__snake_case )
		model.load_state_dict(__snake_case )

		_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
		_UpperCamelCase						    =	PixaStructImageProcessor()
		_UpperCamelCase						    =	PixaStructProcessor(image_processor=__snake_case						,  tokenizer=__snake_case )

		if use_large:
				_UpperCamelCase						    =	4_0_9_6

		_UpperCamelCase						    =	True

		# mkdir if needed
		os.makedirs(__snake_case						,  exist_ok=__snake_case )

		model.save_pretrained(__snake_case )
		processor.save_pretrained(__snake_case )

		print("""Model saved in {}""".format(__snake_case ) )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--use_large", action="store_true", help="Use large model.")
		parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
		lowerCamelCase__	=							parser.parse_args()

		convert_pixastruct_original_pytorch_checkpoint_to_hf(
		    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
		)





import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		return torch.mm(lowerCAmelCase						,  normalized_text_embeds.t() )







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		CLIPConfig

						UpperCamelCase_							: str	=		["CLIPEncoderLayer"]

						def __init__(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__(a				)

								_UpperCamelCase						    =	CLIPVisionModel(config.vision_config				)
								_UpperCamelCase						    =	nn.Linear(config.vision_config.hidden_size       ,							config.projection_dim       ,							bias=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17       ,							config.projection_dim				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3       ,							config.projection_dim				)       ,							requires_grad=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3				)       ,							requires_grad=a				)

						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				).cpu().float().numpy()
								_UpperCamelCase						    =	cosine_distance(a       ,							self.concept_embeds				).cpu().float().numpy()

								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	image_embeds.shape[0]
								for i in range(a				):
										_UpperCamelCase						    =	{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

										# increase this value to create a stronger `nfsw` filter
										# at the cost of increasing the possibility of filtering benign images
										_UpperCamelCase						    =	0.0

										for concept_idx in range(len(special_cos_dist[0]				)				):
												_UpperCamelCase						    =	special_cos_dist[i][concept_idx]
												_UpperCamelCase						    =	self.special_care_embeds_weights[concept_idx].item()
												_UpperCamelCase						    =	round(concept_cos - concept_threshold + adjustment       ,							3				)
												if result_img["special_scores"][concept_idx] > 0:
														result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}				)
														_UpperCamelCase						    =	0.01

										for concept_idx in range(len(cos_dist[0]				)				):
												_UpperCamelCase						    =	cos_dist[i][concept_idx]
												_UpperCamelCase						    =	self.concept_embeds_weights[concept_idx].item()
												_UpperCamelCase						    =	round(concept_cos - concept_threshold + adjustment       ,							3				)
												if result_img["concept_scores"][concept_idx] > 0:
														result_img["bad_concepts"].append(a				)

										result.append(a				)

								_UpperCamelCase						    =	[len(res["""bad_concepts"""]				) > 0 for res in result]

								return images, has_nsfw_concepts







						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				)
								_UpperCamelCase						    =	cosine_distance(a       ,							self.concept_embeds				)

								# increase this value to create a stronger `nsfw` filter
								# at the cost of increasing the possibility of filtering benign images
								_UpperCamelCase						    =	0.0

								_UpperCamelCase						    =	special_cos_dist - self.special_care_embeds_weights + adjustment
								# special_scores = special_scores.round(decimals=3)
								_UpperCamelCase						    =	torch.any(special_scores > 0       ,							dim=1				)
								_UpperCamelCase						    =	special_care * 0.01
								_UpperCamelCase						    =	special_adjustment.unsqueeze(1				).expand(-1       ,							cos_dist.shape[1]				)

								_UpperCamelCase						    =	(cos_dist - self.concept_embeds_weights) + special_adjustment
								# concept_scores = concept_scores.round(decimals=3)
								_UpperCamelCase						    =	torch.any(concept_scores > 0       ,							dim=1				)

								return images, has_nsfw_concepts



