

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														snake_case_							=    orig_state_dict.pop(UpperCamelCase__      )

														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(UpperCamelCase__      )
																					# 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 					__lowerCamelCase	( ):







							'''simple docstring'''






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





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__="groupvit-gcc-yfcc"   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






							snake_case_							=    GroupViTConfig()
							snake_case_							=    GroupViTModel(UpperCamelCase__      ).eval()

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

							with torch.no_grad():
														snake_case_							=    model(**UpperCamelCase__      )

							if model_name == "groupvit-gcc-yfcc":
														snake_case_							=    torch.tensor([[13.35_23, 6.36_29]]      )
							elif model_name == "groupvit-gcc-redcaps":
														snake_case_							=    torch.tensor([[16.18_73, 8.62_30]]      )
							else:
														raise ValueError(F'''Model name {model_name} not supported.'''      )
							assert torch.allclose(outputs.logits_per_image   ,							UpperCamelCase__   ,							atol=1E-3      )

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

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


if __name__ == "__main__":
					_UpperCAmelCase		:   str										=       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		:   str										=       parser.parse_args()

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







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			None
				__SCREAMING_SNAKE_CASE					: Any       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: int       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''tokenizer_file'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
											tokenizer.save_pretrained(self.tmpdirname       )




				def     a							(					self      ,   **snake_case       ):
											kwargs.update(self.special_tokens_map       )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()

											snake_case_							=    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											snake_case_							=    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

											snake_case_							=    tokenizer.batch_encode_plus(snake_case       )['input_ids']
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.batch_decode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case=6       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									snake_case_							=    self.rust_tokenizer_class.from_pretrained(snake_case      ,   **snake_case       )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									snake_case_							=    'This is a simple input'
																									snake_case_							=    ['This is a simple input 1', 'This is a simple input 2']
																									snake_case_							=    ('This is a simple input', 'This is a pair')
																									snake_case_							=    [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode_plus(snake_case      ,   max_length=snake_case       )

																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'       )

																									snake_case_							=    None  # Hotfixing padding = None
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    load_dataset('xnli'      ,   'all_languages'      ,   split='test'      ,   streaming=snake_case       )

											snake_case_							=    next(iter(snake_case       )       )['premise']  # pick up one data
											snake_case_							=    list(sample_data.values()       )

											snake_case_							=    list(map(tokenizer.encode      ,   snake_case       )       )
											snake_case_							=    [tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
											# any sequence length constraints. This test of the parent class will fail since it relies on the
											# maximum sequence length of the positoonal embeddings.
											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )      ,   1       )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )      ,   1       )




