


import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


snake_case__     							= {
    'vit_b32': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 4_9408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 32,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_b16': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 4_9408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 16,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_l14': {
        'embed_dim': 768,
        'image_resolution': 840,
        'context_length': 16,
        'vocab_size': 4_9408,
        'vision_layers': 24,
        'vision_width': 1024,
        'vision_patch_size': 14,
        'transformer_width': 768,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :Tuple=""      ,						lowercase__  :List[str]="/"				)       ->  Union[str, Any]:
						__magic_name__				:							Optional[Any]				=      []

						for k, v in params.items():
												__magic_name__				:							Optional[int]				=      parent_key + sep + k if parent_key else k

												if isinstance(lowercase__      ,						collections.MutableMapping				):
																		items.extend(flatten_nested_dict(lowercase__      ,						lowercase__      ,						sep=lowercase__				).items()				)
												else:
																		items.append((new_key, v)				)
						return dict(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]				)       ->  int:
						return jax.tree_util.tree_map(lambda lowercase__				: x.astype(jnp.floataa				) if x.dtype == jnp.bfloataa else x      ,						lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :List[str]				)       ->  List[str]:
						__magic_name__		, __magic_name__		, __magic_name__				:							Dict				=      pt_attn_layer.in_proj_weight.chunk(3      ,						dim=0				)
						__magic_name__		, __magic_name__		, __magic_name__				:							List[Any]				=      pt_attn_layer.in_proj_bias.chunk(3      ,						dim=0				)

						__magic_name__				:							int				=      pt_attn_layer.out_proj.weight
						__magic_name__				:							Tuple				=      pt_attn_layer.out_proj.bias

						__magic_name__				:							int				=      q_proj
						__magic_name__				:							Optional[int]				=      q_proj_bias

						__magic_name__				:							Optional[Any]				=      k_proj
						__magic_name__				:							str				=      k_proj_bias

						__magic_name__				:							Optional[int]				=      v_proj
						__magic_name__				:							Tuple				=      v_proj_bias

						__magic_name__				:							Optional[Any]				=      out_proj_weights
						__magic_name__				:							Tuple				=      out_proj_bias
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :str				)       ->  str:
						copy_linear(hf_mlp.fca      ,						pt_mlp.c_fc				)
						copy_linear(hf_mlp.fca      ,						pt_mlp.c_proj				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :int				)       ->  Dict:
						__magic_name__				:							List[str]				=      pt_linear.weight
						__magic_name__				:							Optional[int]				=      pt_linear.bias
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :str				)       ->  str:
						# copy layer norms
						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  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :Optional[Any]				)       ->  Dict:
						for hf_layer, pt_layer in zip(lowercase__      ,						lowercase__				):
												copy_layer(lowercase__      ,						lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  str:
						# copy  embeds
						__magic_name__				:							Optional[int]				=      pt_model.token_embedding.weight
						__magic_name__				:							str				=      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  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :List[str]				)       ->  Tuple:
						# copy projection
						__magic_name__				:							List[str]				=      pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model      ,						lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :int				)       ->  Optional[Any]:
						# copy projection
						__magic_name__				:							List[str]				=      pt_model.visual.proj.data.T

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

						# copy embeds
						__magic_name__				:							Optional[int]				=      pt_model.visual.conva.weight.data
						__magic_name__				:							Union[str, Any]				=      pt_model.visual.class_embedding
						__magic_name__				:							Tuple				=      pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers      ,						pt_model.visual.transformer.resblocks				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :Any				)       ->  List[str]:
						__magic_name__				:							List[str]				=      flatten_nested_dict(flax_params['backbone']['merged_class_token']				)

						__magic_name__				:							List[str]				=      torch.from_numpy(flax_class_token_params['scale']				)
						__magic_name__				:							Dict				=      torch.from_numpy(flax_class_token_params['bias']				)
						__magic_name__				:							Tuple				=      nn.Parameter(lowercase__				)
						__magic_name__				:							str				=      nn.Parameter(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :Union[str, Any]				)       ->  Dict:
						__magic_name__				:							List[str]				=      hf_model.state_dict()
						__magic_name__				:							Tuple				=      {}

						# Rename class prediction head flax params to pytorch HF
						__magic_name__				:							Tuple				=      flatten_nested_dict(flax_params['class_head']				)

						for flax_key, v in flax_class_params.items():
												__magic_name__				:							Tuple				=      flax_key.replace('/'      ,						'.'				)
												__magic_name__				:							List[str]				=      torch_key.replace('.kernel'      ,						'.weight'				)
												__magic_name__				:							List[Any]				=      torch_key.replace('Dense_0'      ,						'dense0'				)
												__magic_name__				:							Optional[Any]				=      'class_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		__magic_name__				:							Dict				=      v.T

												__magic_name__				:							List[str]				=      nn.Parameter(torch.from_numpy(lowercase__				)				)

						# Rename box prediction box flax params to pytorch HF
						__magic_name__				:							Optional[int]				=      flatten_nested_dict(flax_params['obj_box_head']				)

						for flax_key, v in flax_box_params.items():
												__magic_name__				:							List[Any]				=      flax_key.replace('/'      ,						'.'				)
												__magic_name__				:							List[Any]				=      torch_key.replace('.kernel'      ,						'.weight'				)
												__magic_name__				:							str				=      torch_key.replace('_'      ,						''				).lower()
												__magic_name__				:							List[str]				=      'box_head.' + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		__magic_name__				:							Optional[int]				=      v.T

												__magic_name__				:							Any				=      nn.Parameter(torch.from_numpy(lowercase__				)				)

						# Copy flax params to PyTorch params
						for name, param in new_params.items():
												if name in pt_params.keys():
																		pt_params[name].copy_(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :Tuple				)       ->  Tuple:
						for k, v in flax_attn_params.items():
												if k.startswith('transformer'				):
																		__magic_name__				:							Dict				=      k.replace('transformer.resblocks'      ,						'text_model.encoder.layers'				)
												else:
																		__magic_name__				:							int				=      k.replace('visual.transformer.resblocks'      ,						'vision_model.encoder.layers'				)

												__magic_name__				:							int				=      torch_key.replace('attn'      ,						'self_attn'				)
												__magic_name__				:							Any				=      torch_key.replace('key'      ,						'k_proj'				)
												__magic_name__				:							Any				=      torch_key.replace('value'      ,						'v_proj'				)
												__magic_name__				:							List[Any]				=      torch_key.replace('query'      ,						'q_proj'				)
												__magic_name__				:							int				=      torch_key.replace('out'      ,						'out_proj'				)

												if "bias" in torch_key and v.ndim == 2:
																		__magic_name__				:							Any				=      v.shape[0] * v.shape[1]
																		__magic_name__				:							Union[str, Any]				=      v.reshape(lowercase__				)

												if "weight" in torch_key and "out" in torch_key:
																		__magic_name__				:							str				=      (v.shape[0] * v.shape[1], v.shape[2])
																		__magic_name__				:							Optional[Any]				=      v.reshape(lowercase__				).T

												if "weight" in torch_key and "out" not in torch_key:
																		__magic_name__				:							List[Any]				=      (v.shape[0], v.shape[1] * v.shape[2])
																		__magic_name__				:							Dict				=      v.reshape(lowercase__				).T

												# Copy flax CLIP attn params to HF PyTorch params
												__magic_name__				:							Optional[int]				=      torch.from_numpy(lowercase__				)
												hf_backbone.state_dict()[torch_key].copy_(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]				)       ->  str:
						__magic_name__				:							int				=      {}
						__magic_name__				:							List[str]				=      []

						for k, v in params.items():
												if "attn." in k:
																		__magic_name__				:							Any				=      k[: k.rindex('attn.'				) + 5]
																		if base in processed_attn_layers:
																								continue

																		processed_attn_layers.append(lowercase__				)
																		__magic_name__				:							List[str]				=      params[base + 'out.weight'].shape[-1]
																		__magic_name__				:							Union[str, Any]				=      params[base + 'out.weight'].reshape(lowercase__      ,						lowercase__				).T
																		__magic_name__				:							List[Any]				=      params[base + 'out.bias']
												else:
																		__magic_name__				:							Any				=      v
						return new_params
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :int				)       ->  List[str]:
						__magic_name__				:							str				=      CLIP(**lowercase__				)
						torch_model.eval()
						__magic_name__				:							Union[str, Any]				=      torch_model.state_dict()

						__magic_name__				:							List[str]				=      flatten_nested_dict(flax_params['backbone']['clip']				)
						__magic_name__				:							Any				=      {}

						for flax_key, v in flax_clip_params.items():
												__magic_name__				:							List[str]				=      flax_key.replace('/'      ,						'.'				)
												__magic_name__				:							Optional[int]				=      torch_key.replace('text.token_embedding.embedding'      ,						'token_embedding.kernel'				)

												if (
												    torch_key.startswith('text.transformer'				)
												    or torch_key.startswith('text.text_projection'				)
												    or torch_key.startswith('text.ln_final'				)
												    or torch_key.startswith('text.positional_embedding'				)
												):
																		__magic_name__				:							Optional[Any]				=      torch_key[5:]

												__magic_name__				:							List[str]				=      torch_key.replace('text_projection.kernel'      ,						'text_projection'				)
												__magic_name__				:							str				=      torch_key.replace('visual.proj.kernel'      ,						'visual.proj'				)
												__magic_name__				:							Optional[Any]				=      torch_key.replace('.scale'      ,						'.weight'				)
												__magic_name__				:							Union[str, Any]				=      torch_key.replace('.kernel'      ,						'.weight'				)

												if "conv" in torch_key or "downsample.0.weight" in torch_key:
																		__magic_name__				:							Tuple				=      v.transpose(3      ,						2      ,						0      ,						1				)

												elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																		# Fully connected layers are transposed, embeddings are not
																		__magic_name__				:							List[Any]				=      v.T

												__magic_name__				:							List[str]				=      v

						__magic_name__				:							Dict				=      _convert_attn_layers(lowercase__				)
						new_torch_params.update(lowercase__				)
						__magic_name__				:							List[str]				=      {}

						# Copy flax CLIP backbone params to PyTorch params
						for name, param in new_torch_params.items():
												if name in torch_clip_params.keys():
																		__magic_name__				:							List[str]				=      torch.from_numpy(new_torch_params[name]				)
																		torch_clip_params[name].copy_(lowercase__				)
												else:
																		__magic_name__				:							List[Any]				=      param

						return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :List[Any]      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Any      ,						lowercase__  :str=None				)       ->  Any:
						__magic_name__				:							int				=      Repository(lowercase__      ,						clone_from=f"google/{pytorch_dump_folder_path}"				)
						repo.git_pull()

						if config_path is not None:
												__magic_name__				:							Dict				=      OwlViTConfig.from_pretrained(lowercase__				)
						else:
												__magic_name__				:							List[Any]				=      OwlViTConfig()

						__magic_name__				:							Union[str, Any]				=      OwlViTModel(lowercase__				).eval()
						__magic_name__				:							int				=      OwlViTForObjectDetection(lowercase__				).eval()

						copy_text_model_and_projection(lowercase__      ,						lowercase__				)
						copy_vision_model_and_projection(lowercase__      ,						lowercase__				)
						__magic_name__				:							List[str]				=      pt_backbone.logit_scale
						copy_flax_attn_params(lowercase__      ,						lowercase__				)

						__magic_name__				:							str				=      hf_backbone
						copy_class_merge_token(lowercase__      ,						lowercase__				)
						copy_class_box_heads(lowercase__      ,						lowercase__				)

						# Save HF model
						hf_model.save_pretrained(repo.local_dir				)

						# Initialize image processor
						__magic_name__				:							Optional[int]				=      OwlViTImageProcessor(
						    size=config.vision_config.image_size      ,						crop_size=config.vision_config.image_size				)
						# Initialize tokenizer
						__magic_name__				:							Optional[Any]				=      CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'      ,						pad_token='!'      ,						model_max_length=1_6				)

						# Initialize processor
						__magic_name__				:							str				=      OwlViTProcessor(image_processor=lowercase__      ,						tokenizer=lowercase__				)
						image_processor.save_pretrained(repo.local_dir				)
						processor.save_pretrained(repo.local_dir				)

						repo.git_add()
						repo.git_commit('Upload model and processor'				)
						repo.git_push()


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--owlvit_version',
			    default=None,
			    type=str,
			    required=True,
			    help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
			)
			parser.add_argument(
			    '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
			)
			parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
			parser.add_argument(
			    '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
			)
			snake_case__     							= parser.parse_args()

			# Initialize PyToch clip model
			snake_case__     							= args.owlvit_version
			if model_name == "clip_b16":
						snake_case__     							= CONFIGS['vit_b16']
			elif model_name == "clip_b32":
						snake_case__     							= CONFIGS['vit_b32']
			elif model_name == "clip_l14":
						snake_case__     							= CONFIGS['vit_l14']

			# Load from checkpoint and convert params to float-32
			snake_case__     							= checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
			snake_case__     							= jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
			del variables

			# Convert CLIP backbone
			snake_case__							,     snake_case__							,     snake_case__     							= convert_clip_backbone(flax_params, torch_config)

			convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict



class     snake_case__  (      _A  ):
				pass






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :Dict[str, Callable]      ,						lowercase__  :Union[str, Any]=None      ,						lowercase__  :str=False				)       ->  List[str]:
						try:
												__magic_name__				:							str				=      ast.parse(lowercase__				)
						except SyntaxError as e:
												print('The code generated by the agent is not valid.\n'      ,						lowercase__				)
												return
						if state is None:
												__magic_name__				:							Optional[Any]				=      {}
						__magic_name__				:							Dict				=      None
						for idx, node in enumerate(expression.body				):
												try:
																		__magic_name__				:							Any				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
												except InterpretorError as e:
																		__magic_name__				:							Tuple				=      f"Evaluation of the code stopped at line {idx} before the end because of the following error"
																		if chat_mode:
																								msg += (
																								    f". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"
																								)
																		else:
																								msg += f":\n{e}"
																		print(lowercase__				)
																		break
												if line_result is not None:
																		__magic_name__				:							List[str]				=      line_result

						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :ast.AST      ,						lowercase__  :Dict[str, Any]      ,						lowercase__  :Dict[str, Callable]				)       ->  Dict:
						if isinstance(lowercase__      ,						ast.Assign				):
												# Assignement -> we evaluate the assignement which should update the state
												# We return the variable assigned as it may be used to determine the final result.
												return evaluate_assign(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Call				):
												# Function call -> we return the value of the function call
												return evaluate_call(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Constant				):
												# Constant -> just return the value
												return expression.value
						elif isinstance(lowercase__      ,						ast.Dict				):
												# Dict -> evaluate all keys and values
												__magic_name__				:							Any				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for k in expression.keys]
												__magic_name__				:							Dict				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for v in expression.values]
												return dict(zip(lowercase__      ,						lowercase__				)				)
						elif isinstance(lowercase__      ,						ast.Expr				):
												# Expression -> evaluate the content
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.For				):
												# For loop -> execute the loop
												return evaluate_for(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.FormattedValue				):
												# Formatted value (part of f-string) -> evaluate the content and return
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.If				):
												# If -> execute the right branch
												return evaluate_if(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif hasattr(lowercase__      ,						'Index'				) and isinstance(lowercase__      ,						ast.Index				):
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.JoinedStr				):
												return "".join([str(evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)				) for v in expression.values]				)
						elif isinstance(lowercase__      ,						ast.List				):
												# List -> evaluate all elements
												return [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for elt in expression.elts]
						elif isinstance(lowercase__      ,						ast.Name				):
												# Name -> pick up the value in the state
												return evaluate_name(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Subscript				):
												# Subscript -> return the value of the indexing
												return evaluate_subscript(lowercase__      ,						lowercase__      ,						lowercase__				)
						else:
												# For now we refuse anything else. Let's add things as we need them.
												raise InterpretorError(f"{expression.__class__.__name__} is not supported."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]				)       ->  List[Any]:
						__magic_name__				:							Union[str, Any]				=      assign.targets
						__magic_name__				:							int				=      evaluate_ast(assign.value      ,						lowercase__      ,						lowercase__				)

						if len(lowercase__				) == 1:
												__magic_name__				:							Any				=      result
						else:
												if len(lowercase__				) != len(lowercase__				):
																		raise InterpretorError(f"Expected {len(lowercase__				)} values but got {len(lowercase__				)}."				)
												for var_name, r in zip(lowercase__      ,						lowercase__				):
																		__magic_name__				:							str				=      r
						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :List[str]      ,						lowercase__  :Tuple				)       ->  Optional[Any]:
						if not isinstance(call.func      ,						ast.Name				):
												raise InterpretorError(
												    f"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of "
												    f"type {type(call.func				)}."				)
						__magic_name__				:							Dict				=      call.func.id
						if func_name not in tools:
												raise InterpretorError(
												    f"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."				)

						__magic_name__				:							Tuple				=      tools[func_name]
						# Todo deal with args
						__magic_name__				:							List[str]				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for arg in call.args]
						__magic_name__				:							int				=      {keyword.arg: evaluate_ast(keyword.value      ,						lowercase__      ,						lowercase__				) for keyword in call.keywords}
						return func(*lowercase__      ,						**lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :int      ,						lowercase__  :int				)       ->  List[Any]:
						__magic_name__				:							Optional[int]				=      evaluate_ast(subscript.slice      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Union[str, Any]				=      evaluate_ast(subscript.value      ,						lowercase__      ,						lowercase__				)
						if isinstance(lowercase__      ,						(list, tuple)				):
												return value[int(lowercase__				)]
						if index in value:
												return value[index]
						if isinstance(lowercase__      ,						lowercase__				) and isinstance(lowercase__      ,						lowercase__				):
												__magic_name__				:							Any				=      difflib.get_close_matches(lowercase__      ,						list(value.keys()				)				)
												if len(lowercase__				) > 0:
																		return value[close_matches[0]]

						raise InterpretorError(f"Could not index {value} with '{index}'."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Optional[int]				)       ->  int:
						if name.id in state:
												return state[name.id]
						__magic_name__				:							Union[str, Any]				=      difflib.get_close_matches(name.id      ,						list(state.keys()				)				)
						if len(lowercase__				) > 0:
												return state[close_matches[0]]
						raise InterpretorError(f"The variable `{name.id}` is not defined."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Optional[Any]				)       ->  List[str]:
						if len(condition.ops				) > 1:
												raise InterpretorError('Cannot evaluate conditions with multiple operators'				)

						__magic_name__				:							int				=      evaluate_ast(condition.left      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Optional[int]				=      condition.ops[0]
						__magic_name__				:							Union[str, Any]				=      evaluate_ast(condition.comparators[0]      ,						lowercase__      ,						lowercase__				)

						if isinstance(lowercase__      ,						ast.Eq				):
												return left == right
						elif isinstance(lowercase__      ,						ast.NotEq				):
												return left != right
						elif isinstance(lowercase__      ,						ast.Lt				):
												return left < right
						elif isinstance(lowercase__      ,						ast.LtE				):
												return left <= right
						elif isinstance(lowercase__      ,						ast.Gt				):
												return left > right
						elif isinstance(lowercase__      ,						ast.GtE				):
												return left >= right
						elif isinstance(lowercase__      ,						ast.Is				):
												return left is right
						elif isinstance(lowercase__      ,						ast.IsNot				):
												return left is not right
						elif isinstance(lowercase__      ,						ast.In				):
												return left in right
						elif isinstance(lowercase__      ,						ast.NotIn				):
												return left not in right
						else:
												raise InterpretorError(f"Operator not supported: {comparator}"				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :str      ,						lowercase__  :Tuple				)       ->  int:
						__magic_name__				:							Optional[int]				=      None
						if evaluate_condition(if_statement.test      ,						lowercase__      ,						lowercase__				):
												for line in if_statement.body:
																		__magic_name__				:							List[Any]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							Tuple				=      line_result
						else:
												for line in if_statement.orelse:
																		__magic_name__				:							Optional[int]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							List[Any]				=      line_result
						return result

def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :List[str]				)       ->  Dict:
						__magic_name__				:							int				=      None
						__magic_name__				:							List[Any]				=      evaluate_ast(for_loop.iter      ,						lowercase__      ,						lowercase__				)
						for counter in iterator:
												__magic_name__				:							Tuple				=      counter
												for expression in for_loop.body:
																		__magic_name__				:							Dict				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							Dict				=      line_result
						return result


