from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Dict					     =			'reformer'
 _lowerCamelCase    :						List[Any]					     =			['past_buckets_states']
 _lowerCamelCase    :						Any					     =			{}

 def __init__(      self							:		List[str]							,      lowerCamelCase							:		List[str]=6_4							,      lowerCamelCase							:		Any=["local", "lsh", "local", "lsh", "local", "lsh"]							,      lowerCamelCase							:		Optional[Any]=1.0							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		Union[str, Any]=[6_4, 6_4]							,      lowerCamelCase							:		List[Any]=[6_4, 1_9_2]							,      lowerCamelCase							:		Any=0							,      lowerCamelCase							:		Union[str, Any]=2							,      lowerCamelCase							:		List[Any]=5_1_2							,      lowerCamelCase							:		str=None							,      lowerCamelCase							:		Dict="relu"							,      lowerCamelCase							:		Optional[Any]=0.0_5							,      lowerCamelCase							:		Dict=2_5_6							,      lowerCamelCase							:		str=0.0_2							,      lowerCamelCase							:		Optional[Any]=False							,      lowerCamelCase							:		Optional[int]=1e-12							,      lowerCamelCase							:		Tuple=1							,      lowerCamelCase							:		Union[str, Any]=0							,      lowerCamelCase							:		str=0.0_5							,      lowerCamelCase							:		Tuple=6_4							,      lowerCamelCase							:		Union[str, Any]=6_4							,      lowerCamelCase							:		Optional[int]=0.0							,      lowerCamelCase							:		List[Any]=1							,      lowerCamelCase							:		Optional[Any]=0							,      lowerCamelCase							:		Dict=4_0_9_6							,      lowerCamelCase							:		Optional[int]=1_2							,      lowerCamelCase							:		List[Any]=None							,      lowerCamelCase							:		int=1							,      lowerCamelCase							:		Union[str, Any]=0							,      lowerCamelCase							:		int=3_2_0							,      lowerCamelCase							:		Optional[Any]=False							,      lowerCamelCase							:		Tuple=True							,      lowerCamelCase							:		int=None							,      **lowerCamelCase							:		Any							,      )			->				str:
        _UpperCamelCase  :							List[str]	    =	hash_seed
        _UpperCamelCase  :							List[str]	    =	vocab_size
        _UpperCamelCase  :							List[str]	    =	attention_head_size
        _UpperCamelCase  :							int	    =	hidden_size
        _UpperCamelCase  :							Optional[int]	    =	num_attention_heads
        _UpperCamelCase  :							Optional[int]	    =	num_hashes
        _UpperCamelCase  :							Optional[int]	    =	len(lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	tuple(lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else num_buckets
        _UpperCamelCase  :							Optional[int]	    =	lsh_attn_chunk_length
        _UpperCamelCase  :							str	    =	local_attn_chunk_length
        _UpperCamelCase  :							Union[str, Any]	    =	lsh_num_chunks_after
        _UpperCamelCase  :							Dict	    =	lsh_num_chunks_before
        _UpperCamelCase  :							Optional[int]	    =	local_num_chunks_after
        _UpperCamelCase  :							Any	    =	local_num_chunks_before
        _UpperCamelCase  :							Optional[int]	    =	hidden_act
        _UpperCamelCase  :							str	    =	feed_forward_size
        _UpperCamelCase  :							Optional[Any]	    =	hidden_dropout_prob
        _UpperCamelCase  :							List[str]	    =	lsh_attention_probs_dropout_prob
        _UpperCamelCase  :							str	    =	local_attention_probs_dropout_prob
        _UpperCamelCase  :							Union[str, Any]	    =	max_position_embeddings
        _UpperCamelCase  :							List[Any]	    =	initializer_range
        _UpperCamelCase  :							int	    =	layer_norm_eps
        _UpperCamelCase  :							List[str]	    =	axial_pos_embds
        _UpperCamelCase  :							List[str]	    =	tuple(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	tuple(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	axial_norm_std
        _UpperCamelCase  :							Optional[int]	    =	chunk_size_lm_head
        _UpperCamelCase  :							Dict	    =	attn_layers
        _UpperCamelCase  :							Union[str, Any]	    =	use_cache
        _UpperCamelCase  :							Optional[int]	    =	classifier_dropout
        super().__init__(
            pad_token_id=lowerCamelCase							,      eos_token_id=lowerCamelCase							,      is_decoder=lowerCamelCase							,      tie_word_embeddings=lowerCamelCase							,      **lowerCamelCase							,      )






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Tuple						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    pt_attn_layer.in_proj_weight.chunk(3				,							dim=0						)
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		str								=    pt_attn_layer.in_proj_bias.chunk(3				,							dim=0						)

	_UpperCamelCase :		Dict								=    pt_attn_layer.out_proj.weight
	_UpperCamelCase :		Tuple								=    pt_attn_layer.out_proj.bias

	_UpperCamelCase :		Tuple								=    q_proj
	_UpperCamelCase :		Dict								=    q_proj_bias

	_UpperCamelCase :		Dict								=    k_proj
	_UpperCamelCase :		str								=    k_proj_bias

	_UpperCamelCase :		Dict								=    v_proj
	_UpperCamelCase :		Optional[int]								=    v_proj_bias

	_UpperCamelCase :		Any								=    out_proj_weights
	_UpperCamelCase :		Optional[int]								=    out_proj_bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					List[Any]:




	'''simple docstring'''




	copy_linear(hf_mlp.fca				,							pt_mlp.c_fc						)
	copy_linear(hf_mlp.fca				,							pt_mlp.c_proj						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  List[str]						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    pt_linear.weight
	_UpperCamelCase :		int								=    pt_linear.bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Dict:




	'''simple docstring'''




	copy_linear(hf_layer.layer_norma				,							pt_layer.ln_a						)
	copy_linear(hf_layer.layer_norma				,							pt_layer.ln_a						)

	# copy MLP
	copy_mlp(hf_layer.mlp				,							pt_layer.mlp						)

	# copy attn
	copy_attn_layer(hf_layer.self_attn				,							pt_layer.attn						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  List[str]						)    ->					Dict:




	'''simple docstring'''




	for hf_layer, pt_layer in zip(UpperCAmelCase_				,							UpperCAmelCase_						):
		copy_layer(UpperCAmelCase_				,							UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Optional[Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    pt_model.token_embedding.weight
	_UpperCamelCase :		Tuple								=    pt_model.positional_embedding

	# copy layer norm
	copy_linear(hf_encoder.final_layer_norm				,							pt_model.ln_final						)

	# copy hidden layers
	copy_layers(hf_encoder.encoder.layers				,							pt_model.transformer.resblocks						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    pt_model.text_projection.data.T

	# copy text encoder
	copy_encoder(hf_model.text_model				,							UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    pt_model.visual.proj.data.T

	# copy layer norms
	copy_linear(hf_model.vision_model.pre_layrnorm				,							pt_model.visual.ln_pre						)
	copy_linear(hf_model.vision_model.post_layernorm				,							pt_model.visual.ln_post						)

	# copy embeds
	_UpperCamelCase :		Tuple								=    pt_model.visual.conva.weight.data
	_UpperCamelCase :		Dict								=    pt_model.visual.class_embedding
	_UpperCamelCase :		str								=    pt_model.visual.positional_embedding.data

	# copy encoder
	copy_layers(hf_model.vision_model.encoder.layers				,							pt_model.visual.transformer.resblocks						)






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[str]=None						)    ->					Union[str, Any]:




	'''simple docstring'''




	if config_path is not None:
		_UpperCamelCase :		Dict								=    CLIPConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Tuple								=    CLIPConfig(projection_dim=5_1_2				,							text_config={}				,							vision_config={}						)

	_UpperCamelCase :		Tuple								=    CLIPModel(UpperCAmelCase_						).eval()

	_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    load(UpperCAmelCase_				,							device='cpu'				,							jit=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.eval()

	copy_text_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	copy_vison_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.logit_scale

	_UpperCamelCase :		Optional[int]								=    torch.arange(0				,							7_7						).unsqueeze(0						)
	_UpperCamelCase :		Dict								=    torch.randn(1				,							3				,							2_2_4				,							2_2_4						)

	_UpperCamelCase :		Optional[Any]								=    hf_model(input_ids=UpperCAmelCase_				,							pixel_values=UpperCAmelCase_				,							return_dict=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    hf_outputs.logits_per_image
	_UpperCamelCase :		Optional[int]								=    hf_outputs.logits_per_text
	_UpperCamelCase						,		_UpperCamelCase :		int								=    pt_model(UpperCAmelCase_				,							UpperCAmelCase_						)

	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)
	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)

	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase__             =		parser.parse_args()

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




