






import collections
import os
import re
from pathlib import Path


A__			:    Optional[Any]         =				'src/transformers'


# Matches is_xxx_available()
A__			:    List[str]         =				re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
A__			:    Any         =				re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A__			:    Tuple         =				re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
A__			:    Optional[int]         =				re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
A__			:    Dict         =				re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A__			:    Any         =				re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
A__			:    Dict         =				re.compile(r'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
A__			:    Tuple         =				re.compile(r'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
A__			:    List[str]         =				re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
A__			:    Any         =				re.compile(r'^\s*try:')
# Catches a line with else:
A__			:    Optional[Any]         =				re.compile(r'^\s*else:')






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if _re_test_backend.search(lowerCamelCase_       ) is None:
								return None
				lowercase__     				=    [b[0] for b in _re_backend.findall(lowerCamelCase_       )]
				backends.sort()
				return "_and_".join(lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     '''r'''	,     encoding='''utf-8'''	,     newline='''\n'''       ) as f:
								lowercase__     				=    f.readlines()

				lowercase__     				=    0
				while line_index < len(lowerCamelCase_       ) and not lines[line_index].startswith('''_import_structure = {'''       ):
								line_index += 1

				# If this is a traditional init, just return.
				if line_index >= len(lowerCamelCase_       ):
								return None

				# First grab the objects without a specific backend in _import_structure
				lowercase__     				=    []
				while not lines[line_index].startswith('''if TYPE_CHECKING'''       ) and find_backend(lines[line_index]       ) is None:
								lowercase__     				=    lines[line_index]
								# If we have everything on a single line, let's deal with it.
								if _re_one_line_import_struct.search(lowerCamelCase_       ):
												lowercase__     				=    _re_one_line_import_struct.search(lowerCamelCase_       ).groups()[0]
												lowercase__     				=    re.findall(r'''\[([^\]]+)\]'''	,     lowerCamelCase_       )
												for imp in imports:
																objects.extend([obj[1:-1] for obj in imp.split(''', '''       )]       )
												line_index += 1
												continue
								lowercase__     				=    _re_import_struct_key_value.search(lowerCamelCase_       )
								if single_line_import_search is not None:
												lowercase__     				=    [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''       ) if len(lowerCamelCase_       ) > 0]
												objects.extend(lowerCamelCase_       )
								elif line.startswith(''' ''' * 8 + '''"'''       ):
												objects.append(line[9:-3]       )
								line_index += 1

				lowercase__     				=    {'''none''': objects}
				# Let's continue with backend-specific objects in _import_structure
				while not lines[line_index].startswith('''if TYPE_CHECKING'''       ):
								# If the line is an if not is_backend_available, we grab all objects associated.
								lowercase__     				=    find_backend(lines[line_index]       )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]       ) is None:
												lowercase__     				=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]       ) is None:
																line_index += 1

												line_index += 1

												lowercase__     				=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(''' ''' * 4       ):
																lowercase__     				=    lines[line_index]
																if _re_import_struct_add_one.search(lowerCamelCase_       ) is not None:
																				objects.append(_re_import_struct_add_one.search(lowerCamelCase_       ).groups()[0]       )
																elif _re_import_struct_add_many.search(lowerCamelCase_       ) is not None:
																				lowercase__     				=    _re_import_struct_add_many.search(lowerCamelCase_       ).groups()[0].split(''', '''       )
																				lowercase__     				=    [obj[1:-1] for obj in imports if len(lowerCamelCase_       ) > 0]
																				objects.extend(lowerCamelCase_       )
																elif _re_between_brackets.search(lowerCamelCase_       ) is not None:
																				lowercase__     				=    _re_between_brackets.search(lowerCamelCase_       ).groups()[0].split(''', '''       )
																				lowercase__     				=    [obj[1:-1] for obj in imports if len(lowerCamelCase_       ) > 0]
																				objects.extend(lowerCamelCase_       )
																elif _re_quote_object.search(lowerCamelCase_       ) is not None:
																				objects.append(_re_quote_object.search(lowerCamelCase_       ).groups()[0]       )
																elif line.startswith(''' ''' * 8 + '''"'''       ):
																				objects.append(line[9:-3]       )
																elif line.startswith(''' ''' * 12 + '''"'''       ):
																				objects.append(line[13:-3]       )
																line_index += 1

												lowercase__     				=    objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				lowercase__     				=    []
				while (
				    line_index < len(lowerCamelCase_       )
				    and find_backend(lines[line_index]       ) is None
				    and not lines[line_index].startswith('''else'''       )
				):
								lowercase__     				=    lines[line_index]
								lowercase__     				=    _re_import.search(lowerCamelCase_       )
								if single_line_import_search is not None:
												objects.extend(single_line_import_search.groups()[0].split(''', '''       )       )
								elif line.startswith(''' ''' * 8       ):
												objects.append(line[8:-2]       )
								line_index += 1

				lowercase__     				=    {'''none''': objects}
				# Let's continue with backend-specific objects
				while line_index < len(lowerCamelCase_       ):
								# If the line is an if is_backend_available, we grab all objects associated.
								lowercase__     				=    find_backend(lines[line_index]       )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]       ) is None:
												lowercase__     				=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]       ) is None:
																line_index += 1

												line_index += 1

												lowercase__     				=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(''' ''' * 8       ):
																lowercase__     				=    lines[line_index]
																lowercase__     				=    _re_import.search(lowerCamelCase_       )
																if single_line_import_search is not None:
																				objects.extend(single_line_import_search.groups()[0].split(''', '''       )       )
																elif line.startswith(''' ''' * 12       ):
																				objects.append(line[12:-2]       )
																line_index += 1

												lowercase__     				=    objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''

				def find_duplicates(lowerCamelCase_       ):
								return [k for k, v in collections.Counter(lowerCamelCase_       ).items() if v > 1]

				if list(import_dict_objects.keys()       ) != list(type_hint_objects.keys()       ):
								return ["Both sides of the init do not have the same backends!"]

				lowercase__     				=    []
				for key in import_dict_objects.keys():
								lowercase__     				=    find_duplicates(import_dict_objects[key]       )
								if duplicate_imports:
												errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""       )
								lowercase__     				=    find_duplicates(type_hint_objects[key]       )
								if duplicate_type_hints:
												errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""       )

								if sorted(set(import_dict_objects[key]       )       ) != sorted(set(type_hint_objects[key]       )       ):
												lowercase__     				=    '''base imports''' if key == '''none''' else F"""{key} backend"""
												errors.append(F"""Differences for {name}:"""       )
												for a in type_hint_objects[key]:
																if a not in import_dict_objects[key]:
																				errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""       )
												for a in import_dict_objects[key]:
																if a not in type_hint_objects[key]:
																				errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""       )
				return errors






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    []
				for root, _, files in os.walk(lowerCamelCase_       ):
								if "__init__.py" in files:
												lowercase__     				=    os.path.join(lowerCamelCase_	,     '''__init__.py'''       )
												lowercase__     				=    parse_init(lowerCamelCase_       )
												if objects is not None:
																lowercase__     				=    analyze_results(*lowerCamelCase_       )
																if len(lowerCamelCase_       ) > 0:
																				lowercase__     				=    F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																				failures.append('''\n'''.join(lowerCamelCase_       )       )
				if len(lowerCamelCase_       ) > 0:
								raise ValueError('''\n\n'''.join(lowerCamelCase_       )       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    []
				for path, directories, files in os.walk(lowerCamelCase_       ):
								for folder in directories:
												# Ignore private modules
												if folder.startswith('''_'''       ):
																directories.remove(lowerCamelCase_       )
																continue
												# Ignore leftovers from branches (empty folders apart from pycache)
												if len(list((Path(lowerCamelCase_       ) / folder).glob('''*.py'''       )       )       ) == 0:
																continue
												lowercase__     				=    str((Path(lowerCamelCase_       ) / folder).relative_to(lowerCamelCase_       )       )
												lowercase__     				=    short_path.replace(os.path.sep	,     '''.'''       )
												submodules.append(lowerCamelCase_       )
								for fname in files:
												if fname == "__init__.py":
																continue
												lowercase__     				=    str((Path(lowerCamelCase_       ) / fname).relative_to(lowerCamelCase_       )       )
												lowercase__     				=    short_path.replace('''.py'''	,     ''''''       ).replace(os.path.sep	,     '''.'''       )
												if len(submodule.split('''.'''       )       ) == 1:
																submodules.append(lowerCamelCase_       )
				return submodules


A__			:    List[str]         =				[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]






def 						a    (		):






				'''simple docstring'''
				from transformers.utils import direct_transformers_import

				lowercase__     				=    direct_transformers_import(lowerCamelCase_       )

				lowercase__     				=    set(transformers._import_structure.keys()       )
				# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
				# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
				# (potentiall re-) add them.
				with open(os.path.join(lowerCamelCase_	,     '''__init__.py'''       )	,     '''r'''       ) as f:
								lowercase__     				=    f.read()
				import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]'''	,     lowerCamelCase_       )       )       )

				lowercase__     				=    [
				    module
				    for module in get_transformers_submodules()
				    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
				]

				if len(lowerCamelCase_       ) > 0:
								lowercase__     				=    '''\n'''.join(F"""- {module}""" for module in module_not_registered       )
								raise ValueError(
								    '''The following submodules are not properly registed in the main init of Transformers:\n'''
								    F"""{list_of_modules}\n"""
								    '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''       )


if __name__ == "__main__":
		check_all_inits()
		check_submodules()












import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
				lowercase__     				=    Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_       ).raw       ).convert('''RGB'''       )

				lowercase__     				=    transforms.Compose(
				    [
				        transforms.Resize((image_size, image_size)	,     interpolation=InterpolationMode.BICUBIC       ),
				        transforms.ToTensor(),
				        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)	,     (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)       ),
				    ]       )
				lowercase__     				=    transform(lowerCamelCase_       ).unsqueeze(0       ).to(lowerCamelCase_       )
				return image






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if "visual_encoder" in key:
								lowercase__     				=    re.sub('''visual_encoder*'''	,     '''vision_model.encoder'''	,     lowerCamelCase_       )
				if "blocks" in key:
								lowercase__     				=    re.sub(r'''blocks'''	,     '''layers'''	,     lowerCamelCase_       )
				if "attn" in key:
								lowercase__     				=    re.sub(r'''attn'''	,     '''self_attn'''	,     lowerCamelCase_       )
				if "norm1" in key:
								lowercase__     				=    re.sub(r'''norm1'''	,     '''layer_norm1'''	,     lowerCamelCase_       )
				if "norm2" in key:
								lowercase__     				=    re.sub(r'''norm2'''	,     '''layer_norm2'''	,     lowerCamelCase_       )
				if "encoder.norm" in key:
								lowercase__     				=    re.sub(r'''encoder.norm'''	,     '''post_layernorm'''	,     lowerCamelCase_       )
				if "encoder.patch_embed.proj" in key:
								lowercase__     				=    re.sub(r'''encoder.patch_embed.proj'''	,     '''embeddings.patch_embedding'''	,     lowerCamelCase_       )

				if "encoder.pos_embed" in key:
								lowercase__     				=    re.sub(r'''encoder.pos_embed'''	,     '''embeddings.position_embedding'''	,     lowerCamelCase_       )
				if "encoder.cls_token" in key:
								lowercase__     				=    re.sub(r'''encoder.cls_token'''	,     '''embeddings.class_embedding'''	,     lowerCamelCase_       )

				if "self_attn" in key:
								lowercase__     				=    re.sub(r'''self_attn.proj'''	,     '''self_attn.projection'''	,     lowerCamelCase_       )

				return key






@torch.no_grad()
def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None       ):






				'''simple docstring'''
				if config_path is not None:
								lowercase__     				=    BlipConfig.from_pretrained(lowerCamelCase_       )
				else:
								lowercase__     				=    BlipConfig(projection_dim=512	,     text_config={}	,     vision_config={}       )

				lowercase__     				=    BlipForConditionalGeneration(lowerCamelCase_       ).eval()

				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

				lowercase__     				=    blip_decoder(pretrained=lowerCamelCase_	,     image_size=384	,     vit='''base'''       )
				lowercase__     				=    pt_model.eval()

				lowercase__     				=    pt_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				hf_model.load_state_dict(lowerCamelCase_       )

				lowercase__     				=    384
				lowercase__     				=    load_demo_image(image_size=lowerCamelCase_	,     device='''cpu'''       )
				lowercase__     				=    BertTokenizer.from_pretrained('''bert-base-uncased'''       )
				lowercase__     				=    tokenizer(['''a picture of''']       ).input_ids

				lowercase__     				=    hf_model.generate(lowerCamelCase_	,     lowerCamelCase_       )

				assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

				lowercase__     				=    hf_model.generate(lowerCamelCase_       )

				assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

				if pytorch_dump_folder_path is not None:
								hf_model.save_pretrained(lowerCamelCase_       )

				# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
				lowercase__     				=    (
				    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
				)

				lowercase__     				=    blip_vqa(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				vqa_model.eval()

				lowercase__     				=    vqa_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForQuestionAnswering(lowerCamelCase_       )

				hf_vqa_model.load_state_dict(lowerCamelCase_       )

				lowercase__     				=    ['''How many dogs are in this image?''']
				lowercase__     				=    tokenizer(lowerCamelCase_	,     return_tensors='''pt'''       ).input_ids

				lowercase__     				=    hf_vqa_model.generate(lowerCamelCase_	,     lowerCamelCase_       )
				print(tokenizer.decode(answer[0]       )       )

				assert tokenizer.decode(answer[0]       ) == "[UNK] 1 [SEP]"
				if pytorch_dump_folder_path is not None:
								hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''       )

				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

				lowercase__     				=    blip_itm(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				itm_model.eval()

				lowercase__     				=    itm_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForImageTextRetrieval(lowerCamelCase_       )

				lowercase__     				=    ['''A picture of a woman with a dog sitting in a beach''']
				lowercase__     				=    tokenizer(
				    lowerCamelCase_	,     return_tensors='''pt'''	,     padding='''max_length'''	,     truncation=lowerCamelCase_	,     max_length=35	,     ).input_ids

				hf_itm_model.load_state_dict(lowerCamelCase_       )
				hf_itm_model.eval()

				lowercase__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )
				lowercase__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )

				assert out[0].item() == 0.21_10_68_74_94_27_79_54
				assert torch.nn.functional.softmax(out_itm[0]	,     dim=1       )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

				if pytorch_dump_folder_path is not None:
								hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''       )


if __name__ == "__main__":
							A__			:    Optional[int]         =				argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							A__			:    List[Any]         =				parser.parse_args()

							convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




