
"""simple docstring"""


import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def    _snake_case					(		):
	A     = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
	A     = Image.open(requests.get(snake_case__						,   stream=snake_case__  ).raw  ).convert('RGB'  )

	return image






def    _snake_case					(		snake_case__ :  Dict  ):
	A     = []
	# fmt: off

	# vision encoder
	rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')  )
	rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')  )
	rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')  )
	rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')  )
	rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')  )
	rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')  )

	for i in range(config.vision_config.num_hidden_layers  ):
		rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)  )
		rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')  )
		rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')  )

	# QFormer
	rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight')  )
	rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')  )

	# fmt: on
	return rename_keys






def    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  int						,   snake_case__ :  str  ):
	A     = dct.pop(snake_case__  )
	A     = val






def    _snake_case					(		snake_case__ :  Optional[int]						,   snake_case__ :  Union[str, Any]  ):
	for i in range(config.vision_config.num_hidden_layers  ):
		# read in original q and v biases
		A     = state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'  )
		A     = state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'  )

		# next, set bias in the state dict
		A     = torch.cat((q_bias, torch.zeros_like(snake_case__						,   requires_grad=snake_case__  ), v_bias)  )
		A     = qkv_bias






def    _snake_case					(		snake_case__ :  Optional[int]						,   snake_case__ :  int  ):
	A     = 364 if 'coco' in model_name else 224
	A     = BlipaVisionConfig(image_size=snake_case__  ).to_dict()

	# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
	# seems like flan-T5 models don't have bos_token_id properly set?
	if "opt-2.7b" in model_name:
		A     = OPTConfig.from_pretrained('facebook/opt-2.7b'						,   eos_token_id=snake_case__  ).to_dict()
	elif "opt-6.7b" in model_name:
		A     = OPTConfig.from_pretrained('facebook/opt-6.7b'						,   eos_token_id=snake_case__  ).to_dict()
	elif "t5-xl" in model_name:
		A     = TaConfig.from_pretrained('google/flan-t5-xl'						,   dense_act_fn='gelu'						,   bos_token_id=1  ).to_dict()
	elif "t5-xxl" in model_name:
		A     = TaConfig.from_pretrained('google/flan-t5-xxl'						,   dense_act_fn='gelu'						,   bos_token_id=1  ).to_dict()

	A     = BlipaConfig(vision_config=snake_case__						,   text_config=snake_case__  )

	return config, image_size






@torch.no_grad()
def    _snake_case					(		snake_case__ :  Optional[Any]						,   snake_case__ :  List[Any]=None						,   snake_case__ :  List[Any]=False  ):
	A     = (
	    AutoTokenizer.from_pretrained('facebook/opt-2.7b'  )
	    if 'opt' in model_name
	    else AutoTokenizer.from_pretrained('google/flan-t5-xl'  )
	)
	A     = tokenizer('\n'						,   add_special_tokens=snake_case__  ).input_ids[0]
	A ,   A     = get_blipa_config(snake_case__						,   eos_token_id=snake_case__  )

	A     = BlipaForConditionalGeneration(snake_case__  ).eval()

	A     = {
	    'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
	    'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
	    'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
	    'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
	    'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
	    'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
	    'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
	}

	A ,   A     = model_name_to_original[model_name]

	# load original model
	print('Loading original model...'  )
	A     = 'cuda' if torch.cuda.is_available() else 'cpu'
	A ,   A ,   A     = load_model_and_preprocess(
	    name=snake_case__						,   model_type=snake_case__						,   is_eval=snake_case__						,   device=snake_case__  )
	original_model.eval()
	print('Done!'  )

	# update state dict keys
	A     = original_model.state_dict()
	A     = create_rename_keys(snake_case__  )
	for src, dest in rename_keys:
		rename_key(snake_case__						,   snake_case__						,   snake_case__  )

	# some keys can be renamed efficiently
	for key, val in state_dict.copy().items():
		A     = state_dict.pop(snake_case__  )
		if key.startswith('Qformer.bert'  ):
			A     = key.replace('Qformer.bert'						,   'qformer'  )
		if "attention.self" in key:
			A     = key.replace('self'						,   'attention'  )
		if "opt_proj" in key:
			A     = key.replace('opt_proj'						,   'language_projection'  )
		if "t5_proj" in key:
			A     = key.replace('t5_proj'						,   'language_projection'  )
		if key.startswith('opt'  ):
			A     = key.replace('opt'						,   'language'  )
		if key.startswith('t5'  ):
			A     = key.replace('t5'						,   'language'  )
		A     = val

	# read in qv biases
	read_in_q_v_bias(snake_case__						,   snake_case__  )

	A ,   A     = hf_model.load_state_dict(snake_case__						,   strict=snake_case__  )
	assert len(snake_case__  ) == 0
	assert unexpected_keys == ["qformer.embeddings.position_ids"]

	A     = load_demo_image()
	A     = vis_processors['eval'](snake_case__  ).unsqueeze(0  ).to(snake_case__  )
	A     = tokenizer(['\n']						,   return_tensors='pt'  ).input_ids.to(snake_case__  )

	# create processor
	A     = BlipImageProcessor(
	    size={'height': image_size, 'width': image_size}						,   image_mean=snake_case__						,   image_std=snake_case__  )
	A     = BlipaProcessor(image_processor=snake_case__						,   tokenizer=snake_case__  )
	A     = processor(images=snake_case__						,   return_tensors='pt'  ).pixel_values.to(snake_case__  )

	# make sure processor creates exact same pixel values
	assert torch.allclose(snake_case__						,   snake_case__  )

	original_model.to(snake_case__  )
	hf_model.to(snake_case__  )
	with torch.no_grad():
		if "opt" in model_name:
			A     = original_model({'image': original_pixel_values, 'text_input': ['']}  ).logits
			A     = hf_model(snake_case__						,   snake_case__  ).logits
		else:
			A     = original_model(
			    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}  ).logits
			A     = input_ids.masked_fill(input_ids == tokenizer.pad_token_id						,   -100  )
			A     = hf_model(snake_case__						,   snake_case__						,   labels=snake_case__  ).logits

	assert original_logits.shape == logits.shape
	print('First values of original logits:'						,   original_logits[0, :3, :3]  )
	print('First values of HF logits:'						,   logits[0, :3, :3]  )

	# assert values
	if model_name == "blip2-flan-t5-xl":
		A     = torch.tensor(
		    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]						,   device=snake_case__  )
		assert torch.allclose(logits[0, :3, :3]						,   snake_case__						,   atol=1e-4  )
	elif model_name == "blip2-flan-t5-xl-coco":
		A     = torch.tensor(
		    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]						,   device=snake_case__  )
	else:
		# cast to same type
		A     = logits.dtype
		assert torch.allclose(original_logits.to(snake_case__  )						,   snake_case__						,   atol=1e-2  )
	print('Looks ok!'  )

	print('Generating a caption...'  )
	A     = ''
	A     = tokenizer(snake_case__						,   return_tensors='pt'  ).input_ids.to(snake_case__  )

	A     = original_model.generate({'image': original_pixel_values}  )
	A     = hf_model.generate(
	    snake_case__						,   snake_case__						,   do_sample=snake_case__						,   num_beams=5						,   max_length=30						,   min_length=1						,   top_p=0.9						,   repetition_penalty=1.0						,   length_penalty=1.0						,   temperature=1						,   )
	print('Original generation:'						,   snake_case__  )
	A     = input_ids.shape[1]
	A     = processor.batch_decode(outputs[:, prompt_length:]						,   skip_special_tokens=snake_case__  )
	A     = [text.strip() for text in output_text]
	print('HF generation:'						,   snake_case__  )

	if pytorch_dump_folder_path is not None:
		processor.save_pretrained(snake_case__  )
		hf_model.save_pretrained(snake_case__  )

	if push_to_hub:
		processor.push_to_hub(F'nielsr/{model_name}'  )
		hf_model.push_to_hub(F'nielsr/{model_name}'  )


if __name__ == "__main__":
		_lowercase				 =					argparse.ArgumentParser()
		_lowercase				 =					[
		    '''blip2-opt-2.7b''',
		    '''blip2-opt-6.7b''',
		    '''blip2-opt-2.7b-coco''',
		    '''blip2-opt-6.7b-coco''',
		    '''blip2-flan-t5-xl''',
		    '''blip2-flan-t5-xl-coco''',
		    '''blip2-flan-t5-xxl''',
		]
		parser.add_argument(
		    '''--model_name''',
		    default='''blip2-opt-2.7b''',
		    choices=choices,
		    type=str,
		    help='''Path to hf config.json of model to convert''',
		)
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether to push the model and processor to the hub after converting''',
		)

		_lowercase				 =					parser.parse_args()

		convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase				 =					{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					['''ElectraTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ElectraForCausalLM''',
		    '''ElectraForMaskedLM''',
		    '''ElectraForMultipleChoice''',
		    '''ElectraForPreTraining''',
		    '''ElectraForQuestionAnswering''',
		    '''ElectraForSequenceClassification''',
		    '''ElectraForTokenClassification''',
		    '''ElectraModel''',
		    '''ElectraPreTrainedModel''',
		    '''load_tf_weights_in_electra''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFElectraForMaskedLM''',
		    '''TFElectraForMultipleChoice''',
		    '''TFElectraForPreTraining''',
		    '''TFElectraForQuestionAnswering''',
		    '''TFElectraForSequenceClassification''',
		    '''TFElectraForTokenClassification''',
		    '''TFElectraModel''',
		    '''TFElectraPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''FlaxElectraForCausalLM''',
		    '''FlaxElectraForMaskedLM''',
		    '''FlaxElectraForMultipleChoice''',
		    '''FlaxElectraForPreTraining''',
		    '''FlaxElectraForQuestionAnswering''',
		    '''FlaxElectraForSequenceClassification''',
		    '''FlaxElectraForTokenClassification''',
		    '''FlaxElectraModel''',
		    '''FlaxElectraPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
		from .tokenization_electra import ElectraTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_electra_fast import ElectraTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_electra import (
				    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				    ElectraPreTrainedModel,
				    load_tf_weights_in_electra,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_electra import (
				    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFElectraForMaskedLM,
				    TFElectraForMultipleChoice,
				    TFElectraForPreTraining,
				    TFElectraForQuestionAnswering,
				    TFElectraForSequenceClassification,
				    TFElectraForTokenClassification,
				    TFElectraModel,
				    TFElectraPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				    FlaxElectraPreTrainedModel,
				)

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)