






"""simple docstring"""



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,
)


_UpperCamelCase						:    Union[str, Any]		=						{
    'vit_b32': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 3_2,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_b16': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 1_6,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_l14': {
        'embed_dim': 7_6_8,
        'image_resolution': 8_4_0,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 2_4,
        'vision_width': 1_0_2_4,
        'vision_patch_size': 1_4,
        'transformer_width': 7_6_8,
        'transformer_heads': 1_2,
        'transformer_layers': 1_2,
    },
}
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    List[str]=""						,   __snake_case     :    List[str]="/"       ):





  '''simple docstring'''





  lowercase				       =			[]

  for k, v in params.items():
    lowercase				       =			parent_key + sep + k if parent_key else k

    if isinstance(__snake_case						,   collections.MutableMapping       ):
      items.extend(flatten_nested_dict(__snake_case						,   __snake_case						,   sep=__snake_case       ).items()       )
    else:
      items.append((new_key, v)       )
  return dict(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  return jax.tree_util.tree_map(lambda __snake_case       : x.astype(jnp.floataa       ) if x.dtype == jnp.bfloataa else x						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_weight.chunk(3						,   dim=0       )
  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_bias.chunk(3						,   dim=0       )

  lowercase				       =			pt_attn_layer.out_proj.weight
  lowercase				       =			pt_attn_layer.out_proj.bias

  lowercase				       =			q_proj
  lowercase				       =			q_proj_bias

  lowercase				       =			k_proj
  lowercase				       =			k_proj_bias

  lowercase				       =			v_proj
  lowercase				       =			v_proj_bias

  lowercase				       =			out_proj_weights
  lowercase				       =			out_proj_bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  copy_linear(hf_mlp.fca						,   pt_mlp.c_fc       )
  copy_linear(hf_mlp.fca						,   pt_mlp.c_proj       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			pt_linear.weight
  lowercase				       =			pt_linear.bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    str       ):





  '''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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  for hf_layer, pt_layer in zip(__snake_case						,   __snake_case       ):
    copy_layer(__snake_case						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.token_embedding.weight
  lowercase				       =			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					(       __snake_case     :    Optional[int]						,   __snake_case     :    List[Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			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
  lowercase				       =			pt_model.visual.conva.weight.data
  lowercase				       =			pt_model.visual.class_embedding
  lowercase				       =			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					(       __snake_case     :    Tuple						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			flatten_nested_dict(flax_params['backbone']['merged_class_token']       )

  lowercase				       =			torch.from_numpy(flax_class_token_params['scale']       )
  lowercase				       =			torch.from_numpy(flax_class_token_params['bias']       )
  lowercase				       =			nn.Parameter(__snake_case       )
  lowercase				       =			nn.Parameter(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			hf_model.state_dict()
  lowercase				       =			{}

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

  for flax_key, v in flax_class_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )
    lowercase				       =			torch_key.replace('Dense_0'						,   'dense0'       )
    lowercase				       =			'class_head.' + torch_key

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

    lowercase				       =			nn.Parameter(torch.from_numpy(__snake_case       )       )

  # Rename box prediction box flax params to pytorch HF
  lowercase				       =			flatten_nested_dict(flax_params['obj_box_head']       )

  for flax_key, v in flax_box_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )
    lowercase				       =			torch_key.replace('_'						,   ''       ).lower()
    lowercase				       =			'box_head.' + torch_key

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

    lowercase				       =			nn.Parameter(torch.from_numpy(__snake_case       )       )

  # Copy flax params to PyTorch params
  for name, param in new_params.items():
    if name in pt_params.keys():
      pt_params[name].copy_(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  for k, v in flax_attn_params.items():
    if k.startswith('transformer'       ):
      lowercase				       =			k.replace('transformer.resblocks'						,   'text_model.encoder.layers'       )
    else:
      lowercase				       =			k.replace('visual.transformer.resblocks'						,   'vision_model.encoder.layers'       )

    lowercase				       =			torch_key.replace('attn'						,   'self_attn'       )
    lowercase				       =			torch_key.replace('key'						,   'k_proj'       )
    lowercase				       =			torch_key.replace('value'						,   'v_proj'       )
    lowercase				       =			torch_key.replace('query'						,   'q_proj'       )
    lowercase				       =			torch_key.replace('out'						,   'out_proj'       )

    if "bias" in torch_key and v.ndim == 2:
      lowercase				       =			v.shape[0] * v.shape[1]
      lowercase				       =			v.reshape(__snake_case       )

    if "weight" in torch_key and "out" in torch_key:
      lowercase				       =			(v.shape[0] * v.shape[1], v.shape[2])
      lowercase				       =			v.reshape(__snake_case       ).T

    if "weight" in torch_key and "out" not in torch_key:
      lowercase				       =			(v.shape[0], v.shape[1] * v.shape[2])
      lowercase				       =			v.reshape(__snake_case       ).T

    # Copy flax CLIP attn params to HF PyTorch params
    lowercase				       =			torch.from_numpy(__snake_case       )
    hf_backbone.state_dict()[torch_key].copy_(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			{}
  lowercase				       =			[]

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

      processed_attn_layers.append(__snake_case       )
      lowercase				       =			params[base + 'out.weight'].shape[-1]
      lowercase				       =			params[base + 'out.weight'].reshape(__snake_case						,   __snake_case       ).T
      lowercase				       =			params[base + 'out.bias']
    else:
      lowercase				       =			v
  return new_params
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  lowercase				       =			CLIP(**__snake_case       )
  torch_model.eval()
  lowercase				       =			torch_model.state_dict()

  lowercase				       =			flatten_nested_dict(flax_params['backbone']['clip']       )
  lowercase				       =			{}

  for flax_key, v in flax_clip_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			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'       )
    ):
      lowercase				       =			torch_key[5:]

    lowercase				       =			torch_key.replace('text_projection.kernel'						,   'text_projection'       )
    lowercase				       =			torch_key.replace('visual.proj.kernel'						,   'visual.proj'       )
    lowercase				       =			torch_key.replace('.scale'						,   '.weight'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )

    if "conv" in torch_key or "downsample.0.weight" in torch_key:
      lowercase				       =			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
      lowercase				       =			v.T

    lowercase				       =			v

  lowercase				       =			_convert_attn_layers(__snake_case       )
  new_torch_params.update(__snake_case       )
  lowercase				       =			{}

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

  return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    Tuple						,   __snake_case     :    Optional[int]						,   __snake_case     :    Tuple						,   __snake_case     :    Tuple=None       ):





  '''simple docstring'''





  lowercase				       =			Repository(__snake_case						,   clone_from=f'google/{pytorch_dump_folder_path}'       )
  repo.git_pull()

  if config_path is not None:
    lowercase				       =			OwlViTConfig.from_pretrained(__snake_case       )
  else:
    lowercase				       =			OwlViTConfig()

  lowercase				       =			OwlViTModel(__snake_case       ).eval()
  lowercase				       =			OwlViTForObjectDetection(__snake_case       ).eval()

  copy_text_model_and_projection(__snake_case						,   __snake_case       )
  copy_vision_model_and_projection(__snake_case						,   __snake_case       )
  lowercase				       =			pt_backbone.logit_scale
  copy_flax_attn_params(__snake_case						,   __snake_case       )

  lowercase				       =			hf_backbone
  copy_class_merge_token(__snake_case						,   __snake_case       )
  copy_class_box_heads(__snake_case						,   __snake_case       )

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

  # Initialize image processor
  lowercase				       =			OwlViTImageProcessor(
      size=config.vision_config.image_size						,   crop_size=config.vision_config.image_size       )
  # Initialize tokenizer
  lowercase				       =			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'						,   pad_token='!'						,   model_max_length=16       )

  # Initialize processor
  lowercase				       =			OwlViTProcessor(image_processor=__snake_case						,   tokenizer=__snake_case       )
  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__":
    _UpperCamelCase						:    Any		=						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.'
    )
    _UpperCamelCase						:    Tuple		=						parser.parse_args()

    # Initialize PyToch clip model
    _UpperCamelCase						:    Union[str, Any]		=						args.owlvit_version
    if model_name == "clip_b16":
        _UpperCamelCase						:    List[Any]		=						CONFIGS['vit_b16']
    elif model_name == "clip_b32":
        _UpperCamelCase						:    Optional[Any]		=						CONFIGS['vit_b32']
    elif model_name == "clip_l14":
        _UpperCamelCase						:    Any		=						CONFIGS['vit_l14']

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

    # Convert CLIP backbone
    _UpperCamelCase,						_UpperCamelCase,						_UpperCamelCase						:    Optional[Any]		=						convert_clip_backbone(flax_params, torch_config)

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





'''simple docstring'''







import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


_lowerCamelCase    =  {}
for task, _ in pipeline_test_mapping.items():
							_lowerCamelCase    =  {'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
_lowerCamelCase    =  {
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):

	if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "pt"
	elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "tf"
	elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
		return "flax"
	else:
		return None






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	# Use the cached results
	if PIPELINE_TEST_MAPPING[task].get(snake_case_							,       snake_case_	) is not None:
		return PIPELINE_TEST_MAPPING[task][framework]

	_lowercase							=     pipeline_test_mapping[task]["""test"""]
	_lowercase							=     None

	if framework == "pt":
		_lowercase							=     getattr(snake_case_							,       """model_mapping"""							,       snake_case_	)
	elif framework == "tf":
		_lowercase							=     getattr(snake_case_							,       """tf_model_mapping"""							,       snake_case_	)

	if mapping is not None:
		_lowercase							=     dict(mapping.items()	)

	# cache the results
	_lowercase							=     mapping
	return mapping






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	_lowercase							=     get_framework(snake_case_	)
	if framework is None:
		return None
	_lowercase							=     get_mapping_for_task(snake_case_							,       snake_case_	)
	if mapping is None:
		return None

	_lowercase							=     list({model_class.config_class for model_class in test_class.all_model_classes}	)
	if len(snake_case_	) != 1:
		raise ValueError("""There should be exactly one configuration class from `test_class.all_model_classes`."""	)

	# This could be a list/tuple of model classes, but it's rare.
	_lowercase							=     mapping.get(config_classes[0]							,       snake_case_	)
	if isinstance(snake_case_							,       (tuple, list)	):
		_lowercase							=     sorted(snake_case_							,       key=lambda snake_case_	: x.__name__	)

	return model_class






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     [(task, get_model_for_pipeline_test(snake_case_							,       snake_case_	)) for task in pipeline_test_mapping]
	_lowercase							=     sorted([(task, model) for task, model in mapping if model is not None]							,       key=lambda snake_case_	: x[0]	)

	return dict(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     get_framework(snake_case_	)
	if framework == "pt":
		_lowercase							=     """torch"""
	_lowercase							=     """{}"""

	_lowercase							=     get_pipeline_model_mapping(snake_case_	)
	if len(snake_case_	) == 0:
		return ""

	_lowercase							=     []
	for task, model_classes in mapping.items():
		if isinstance(snake_case_							,       (tuple, list)	):
			# A list/tuple of model classes
			_lowercase							=     """(""" + """, """.join([x.__name__ for x in model_classes]	) + """)"""
		else:
			# A single model class
			_lowercase							=     model_classes.__name__
		texts.append(F"""\"{task}\": {value}"""	)
	_lowercase							=     """{""" + """, """.join(snake_case_	) + """}"""
	_lowercase							=     F"""pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"""

	return text






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     {"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
	if not issubclass(snake_case_							,       unittest.TestCase	):
		return False
	return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]	)	) > 0






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     [x for x in get_test_classes(snake_case_	) if is_valid_test_class(snake_case_	)]

	_lowercase							=     None
	for test_class in test_classes:
		# If a test class has defined `pipeline_model_mapping`, let's take it
		if getattr(snake_case_							,       """pipeline_model_mapping"""							,       snake_case_	) is not None:
			_lowercase							=     test_class
			break
    # Take the test class with the shortest name (just a heuristic)
	if target_test_class is None and len(snake_case_	) > 0:
		_lowercase							=     sorted(snake_case_							,       key=lambda snake_case_	: (len(x.__name__	), x.__name__)	)[0]

	return target_test_class






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     start_idx
	for idx, line in enumerate(lines[start_idx:]	):
		_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
		if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
			_lowercase							=     start_idx + idx
		elif idx > 0 and indent <= indent_level:
			# Outside the definition block of `pipeline_model_mapping`
			break

	return end_idx






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False	):
	if getattr(snake_case_							,       """pipeline_model_mapping"""							,       snake_case_	) is not None:
		if not overwrite:
			return "", -1

	_lowercase							=     get_pipeline_model_mapping_string(snake_case_	)
	if len(snake_case_	) == 0:
		return "", -1
	_lowercase							=     line_to_add + """\n"""

	# The code defined the class `test_class`
	_lowercase		,		_lowercase							=     inspect.getsourcelines(snake_case_	)
	# `inspect` gives the code for an object, including decorator(s) if any.
	# We (only) need the exact line of the class definition.
	for idx, line in enumerate(snake_case_	):
		if line.lstrip().startswith("""class """	):
			_lowercase							=     class_lines[idx:]
			class_start_line_no += idx
			break
	_lowercase							=     class_start_line_no + len(snake_case_	) - 1

	# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
	# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
	# block that appears in a `test_class`.
	_lowercase							=     None
	# The indent level of the line at `class_lines[start_idx]` (if defined)
	_lowercase							=     0
	# To record if `pipeline_model_mapping` is found in `test_class`.
	_lowercase							=     None
	for idx, line in enumerate(snake_case_	):
		if line.strip().startswith("""all_model_classes = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
		elif line.strip().startswith("""all_generative_model_classes = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
		elif line.strip().startswith("""pipeline_model_mapping = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
			_lowercase							=     line
			break

	if start_idx is None:
		return "", -1
	# Find the ending index (inclusive) of the above found block.
	_lowercase							=     find_block_ending(snake_case_							,       snake_case_							,       snake_case_	)

	# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
	# `is_timm_available()` instead of `is_torch_available()`.
	# Keep leading and trailing whitespaces
	_lowercase							=     re.compile(r"""\s(is_\S+?_available\(\))\s"""	)
	for line in class_lines[start_idx : end_idx + 1]:
		_lowercase							=     r.search(snake_case_	)
		if backend_condition is not None:
			# replace the leading and trailing whitespaces to the space character " ".
			_lowercase							=     """ """ + backend_condition[0][1:-1] + """ """
			_lowercase							=     r.sub(snake_case_							,       snake_case_	)
			break

	if def_line is None:
		# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
		# `all_model_classes` or `all_generative_model_classes`.
		_lowercase							=     end_idx
	else:
		# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
		_lowercase							=     start_idx - 1
		# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
		for idx in range(snake_case_							,       end_idx + 1	):
			# These lines are going to be removed before writing to the test file.
			_lowercase							=     None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
	_lowercase							=     [x.__name__ for x in test_class.__bases__]
	if "PipelineTesterMixin" not in parent_classes:
		# Put `PipelineTesterMixin` just before `unittest.TestCase`
		_lowercase							=     [x for x in parent_classes if x != """TestCase"""] + ["""PipelineTesterMixin"""]
		if "TestCase" in parent_classes:
			# Here we **assume** the original string is always with `unittest.TestCase`.
			_parent_classes.append("""unittest.TestCase"""	)
		_lowercase							=     """, """.join(_parent_classes	)
		for idx, line in enumerate(snake_case_	):
			# Find the ending of the declaration of `test_class`
			if line.strip().endswith("""):"""	):
				# mark the lines of the declaration of `test_class` to be removed
				for _idx in range(idx + 1	):
					_lowercase							=     None  # noqa
				break
        # Add the new, one-line, class declaration for `test_class`
		_lowercase							=     F"""class {test_class.__name__}({parent_classes}):\n"""

	# Add indentation
	_lowercase							=     """ """ * indent_level + line_to_add
	# Insert `pipeline_model_mapping` to `class_lines`.
	# (The line at `target_idx` should be kept by definition!)
	_lowercase							=     class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
	# Remove the lines that are marked to be removed
	_lowercase							=     [x for x in class_lines if x is not None]

	# Move from test class to module (in order to write to the test file)
	_lowercase							=     inspect.getsourcelines(inspect.getmodule(snake_case_	)	)[0]
	# Be careful with the 1-off between line numbers and array indices
	_lowercase							=     module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
	_lowercase							=     """""".join(snake_case_	)

	_lowercase							=     inspect.getsourcefile(snake_case_	)
	with open(snake_case_							,       """w"""							,       encoding="""UTF-8"""							,       newline="""\n"""	) as fp:
		fp.write(snake_case_	)

	return line_to_add






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False	):
	_lowercase							=     find_test_class(snake_case_	)
	if test_class:
		add_pipeline_model_mapping(snake_case_							,       overwrite=snake_case_	)


if __name__ == "__main__":
							_lowerCamelCase    =  argparse.ArgumentParser()
							parser.add_argument(
							    '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
							)
							parser.add_argument(
							    '--all',
							    action='store_true',
							    help='If to check and modify all test files.',
							)
							parser.add_argument(
							    '--overwrite',
							    action='store_true',
							    help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
							)
							_lowerCamelCase    =  parser.parse_args()

							if not args.all and not args.test_file:
														raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
							elif args.all and args.test_file:
														raise ValueError('Only one of `--test_file` and `--all` could be specified.')

							_lowerCamelCase    =  []
							if args.test_file:
														_lowerCamelCase    =  [args.test_file]
							else:
														_lowerCamelCase    =  os.path.join('tests', 'models', '**', 'test_modeling_*.py')
														for test_file in glob.glob(pattern):
																					# `Flax` is not concerned at this moment
																					if not test_file.startswith('test_modeling_flax_'):
																												test_files.append(test_file)

							for test_file in test_files:
														if test_file in TEST_FILE_TO_IGNORE:
																					print(F"""[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.""")
																					continue
														add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)




