



from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin





@dataclass
class 		__SCREAMING_SNAKE_CASE      (				lowerCAmelCase_      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  42





class 		__SCREAMING_SNAKE_CASE      (				lowerCAmelCase_					,			lowerCAmelCase_      ):






				"""simple docstring"""

				@register_to_config
				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 16						,	SCREAMING_SNAKE_CASE__ = 88						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 1						,	SCREAMING_SNAKE_CASE__ = 0.0						,	SCREAMING_SNAKE_CASE__ = 32						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = "geglu"						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = "layer_norm"						,	SCREAMING_SNAKE_CASE__ = True						,	):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							List[Any]					     =   use_linear_projection
					_snake_case					:							Dict					     =   num_attention_heads
					_snake_case					:							List[str]					     =   attention_head_dim
					_snake_case					:							Any					     =   num_attention_heads * attention_head_dim

					# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
					# Define whether input is continuous or discrete depending on configuration
					_snake_case					:							Any					     =   (in_channels is not None) and (patch_size is None)
					_snake_case					:							Optional[Any]					     =   num_vector_embeds is not None
					_snake_case					:							List[Any]					     =   in_channels is not None and patch_size is not None

					if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
						_snake_case					:							Union[str, Any]					     =   (
						    f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
						    """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
						    """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
						    """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
						    """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
						)
						deprecate("""norm_type!=num_embeds_ada_norm"""						,	"""1.0.0"""						,	lowerCamelCase_						,	standard_warn=lowerCamelCase_      )
						_snake_case					:							str					     =   """ada_norm"""

					if self.is_input_continuous and self.is_input_vectorized:
						raise ValueError(
						    f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
						    """ sure that either `in_channels` or `num_vector_embeds` is None."""      )
					elif self.is_input_vectorized and self.is_input_patches:
						raise ValueError(
						    f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
						    """ sure that either `num_vector_embeds` or `num_patches` is None."""      )
					elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
						raise ValueError(
						    f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
						    f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''      )

					# 2. Define input layers
					if self.is_input_continuous:
						_snake_case					:							Tuple					     =   in_channels

						_snake_case					:							str					     =   torch.nn.GroupNorm(num_groups=lowerCamelCase_						,	num_channels=lowerCamelCase_						,	eps=1e-6						,	affine=lowerCamelCase_      )
						if use_linear_projection:
							_snake_case					:							Any					     =   nn.Linear(lowerCamelCase_						,	lowerCamelCase_      )
						else:
							_snake_case					:							int					     =   nn.Convad(lowerCamelCase_						,	lowerCamelCase_						,	kernel_size=1						,	stride=1						,	padding=0      )
					elif self.is_input_vectorized:
						assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
						assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

						_snake_case					:							int					     =   sample_size
						_snake_case					:							Any					     =   sample_size
						_snake_case					:							Optional[int]					     =   num_vector_embeds
						_snake_case					:							Tuple					     =   self.height * self.width

						_snake_case					:							Optional[int]					     =   ImagePositionalEmbeddings(
						    num_embed=lowerCamelCase_						,	embed_dim=lowerCamelCase_						,	height=self.height						,	width=self.width      )
					elif self.is_input_patches:
						assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

						_snake_case					:							Optional[Any]					     =   sample_size
						_snake_case					:							Optional[Any]					     =   sample_size

						_snake_case					:							Optional[Any]					     =   patch_size
						_snake_case					:							Union[str, Any]					     =   PatchEmbed(
						    height=lowerCamelCase_						,	width=lowerCamelCase_						,	patch_size=lowerCamelCase_						,	in_channels=lowerCamelCase_						,	embed_dim=lowerCamelCase_						,	)

					# 3. Define transformers blocks
					_snake_case					:							Dict					     =   nn.ModuleList(
					    [
					        BasicTransformerBlock(
					            lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_						,	dropout=lowerCamelCase_						,	cross_attention_dim=lowerCamelCase_						,	activation_fn=lowerCamelCase_						,	num_embeds_ada_norm=lowerCamelCase_						,	attention_bias=lowerCamelCase_						,	only_cross_attention=lowerCamelCase_						,	upcast_attention=lowerCamelCase_						,	norm_type=lowerCamelCase_						,	norm_elementwise_affine=lowerCamelCase_						,	)
					        for d in range(lowerCamelCase_      )
					    ]      )

					# 4. Define output layers
					_snake_case					:							Optional[int]					     =   in_channels if out_channels is None else out_channels
					if self.is_input_continuous:
						# TODO: should use out_channels for continuous projections
						if use_linear_projection:
							_snake_case					:							List[Any]					     =   nn.Linear(lowerCamelCase_						,	lowerCamelCase_      )
						else:
							_snake_case					:							Union[str, Any]					     =   nn.Convad(lowerCamelCase_						,	lowerCamelCase_						,	kernel_size=1						,	stride=1						,	padding=0      )
					elif self.is_input_vectorized:
						_snake_case					:							Any					     =   nn.LayerNorm(lowerCamelCase_      )
						_snake_case					:							Union[str, Any]					     =   nn.Linear(lowerCamelCase_						,	self.num_vector_embeds - 1      )
					elif self.is_input_patches:
						_snake_case					:							str					     =   nn.LayerNorm(lowerCamelCase_						,	elementwise_affine=lowerCamelCase_						,	eps=1e-6      )
						_snake_case					:							Optional[int]					     =   nn.Linear(lowerCamelCase_						,	2 * inner_dim      )
						_snake_case					:							Any					     =   nn.Linear(lowerCamelCase_						,	patch_size * patch_size * self.out_channels      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = True						,	):






					"""simple docstring"""

					if attention_mask is not None and attention_mask.ndim == 2:
						# assume that mask is expressed as:
						#   (1 = keep,      0 = discard)
						# convert mask into a bias that can be added to attention scores:
						#       (keep = +0,     discard = -10000.0)
						_snake_case					:							Optional[Any]					     =   (1 - attention_mask.to(hidden_states.dtype      )) * -1_00_00.0
						_snake_case					:							int					     =   attention_mask.unsqueeze(1      )

					# convert encoder_attention_mask to a bias the same way we do for attention_mask
					if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
						_snake_case					:							Dict					     =   (1 - encoder_attention_mask.to(hidden_states.dtype      )) * -1_00_00.0
						_snake_case					:							Union[str, Any]					     =   encoder_attention_mask.unsqueeze(1      )

					# 1. Input
					if self.is_input_continuous:
						_snake_case     , _snake_case     , _snake_case     , _snake_case					:							Tuple					     =   hidden_states.shape
						_snake_case					:							Optional[Any]					     =   hidden_states

						_snake_case					:							Dict					     =   self.norm(lowerCamelCase_      )
						if not self.use_linear_projection:
							_snake_case					:							Union[str, Any]					     =   self.proj_in(lowerCamelCase_      )
							_snake_case					:							Any					     =   hidden_states.shape[1]
							_snake_case					:							Any					     =   hidden_states.permute(0						,	2						,	3						,	1      ).reshape(lowerCamelCase_						,	height * width						,	lowerCamelCase_      )
						else:
							_snake_case					:							Union[str, Any]					     =   hidden_states.shape[1]
							_snake_case					:							Optional[int]					     =   hidden_states.permute(0						,	2						,	3						,	1      ).reshape(lowerCamelCase_						,	height * width						,	lowerCamelCase_      )
							_snake_case					:							Tuple					     =   self.proj_in(lowerCamelCase_      )
					elif self.is_input_vectorized:
						_snake_case					:							Any					     =   self.latent_image_embedding(lowerCamelCase_      )
					elif self.is_input_patches:
						_snake_case					:							Optional[Any]					     =   self.pos_embed(lowerCamelCase_      )

					# 2. Blocks
					for block in self.transformer_blocks:
						_snake_case					:							List[Any]					     =   block(
						    lowerCamelCase_						,	attention_mask=lowerCamelCase_						,	encoder_hidden_states=lowerCamelCase_						,	encoder_attention_mask=lowerCamelCase_						,	timestep=lowerCamelCase_						,	cross_attention_kwargs=lowerCamelCase_						,	class_labels=lowerCamelCase_						,	)

					# 3. Output
					if self.is_input_continuous:
						if not self.use_linear_projection:
							_snake_case					:							int					     =   hidden_states.reshape(lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_      ).permute(0						,	3						,	1						,	2      ).contiguous()
							_snake_case					:							Dict					     =   self.proj_out(lowerCamelCase_      )
						else:
							_snake_case					:							Optional[int]					     =   self.proj_out(lowerCamelCase_      )
							_snake_case					:							Any					     =   hidden_states.reshape(lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_      ).permute(0						,	3						,	1						,	2      ).contiguous()

						_snake_case					:							List[Any]					     =   hidden_states + residual
					elif self.is_input_vectorized:
						_snake_case					:							str					     =   self.norm_out(lowerCamelCase_      )
						_snake_case					:							Union[str, Any]					     =   self.out(lowerCamelCase_      )
						# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
						_snake_case					:							Any					     =   logits.permute(0						,	2						,	1      )

						# log(p(x_0))
						_snake_case					:							int					     =   F.log_softmax(logits.double()						,	dim=1      ).float()
					elif self.is_input_patches:
						# TODO: cleanup!
						_snake_case					:							str					     =   self.transformer_blocks[0].norma.emb(
						    lowerCamelCase_						,	lowerCamelCase_						,	hidden_dtype=hidden_states.dtype      )
						_snake_case     , _snake_case					:							Tuple					     =   self.proj_out_a(F.silu(lowerCamelCase_      )      ).chunk(2						,	dim=1      )
						_snake_case					:							List[Any]					     =   self.norm_out(lowerCamelCase_      ) * (1 + scale[:, None]) + shift[:, None]
						_snake_case					:							Optional[int]					     =   self.proj_out_a(lowerCamelCase_      )

						# unpatchify
						_snake_case					:							Optional[int]					     =   int(hidden_states.shape[1] ** 0.5      )
						_snake_case					:							List[str]					     =   hidden_states.reshape(
						    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
						_snake_case					:							Any					     =   torch.einsum("""nhwpqc->nchpwq"""						,	lowerCamelCase_      )
						_snake_case					:							Union[str, Any]					     =   hidden_states.reshape(
						    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)      )

					if not return_dict:
						return (output,)

					return TransformeraDModelOutput(sample=lowerCamelCase_      )







"""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


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


# DO **NOT** add item to this set (unless the reason is approved)
SCREAMING_SNAKE_CASE_  					= {
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							str:

   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       __UpperCAmelCase		(		a_      :		Optional[Any]     ,	a_      :		int					)  ->							List[Any]:
   # Use the cached results
   if PIPELINE_TEST_MAPPING[task].get(a_     ,	a_					) is not None:
      return PIPELINE_TEST_MAPPING[task][framework]

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

   if framework == "pt":
      _UpperCAmelCase 						=       getattr(a_     ,	"model_mapping"     ,	a_					)
   elif framework == "tf":
      _UpperCAmelCase 						=       getattr(a_     ,	"tf_model_mapping"     ,	a_					)

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

   # cache the results
   _UpperCAmelCase 						=       mapping
   return mapping
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework is None:
      return None
   _UpperCAmelCase 						=       get_mapping_for_task(a_     ,	a_					)
   if mapping is None:
      return None

   _UpperCAmelCase 						=       list({model_class.config_class for model_class in test_class.all_model_classes}					)
   if len(a_					) != 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.
   _UpperCAmelCase 						=       mapping.get(config_classes[0]     ,	a_					)
   if isinstance(a_     ,	(tuple, list)					):
      _UpperCAmelCase 						=       sorted(a_     ,	key=lambda a_					: x.__name__					)

   return model_class
def       __UpperCAmelCase		(		a_      :		Tuple					)  ->							Tuple:
   _UpperCAmelCase 						=       [(task, get_model_for_pipeline_test(a_     ,	a_					)) for task in pipeline_test_mapping]
   _UpperCAmelCase 						=       sorted([(task, model) for task, model in mapping if model is not None]     ,	key=lambda a_					: x[0]					)

   return dict(a_					)
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework == "pt":
      _UpperCAmelCase 						=       "torch"
   _UpperCAmelCase 						=       "{}"

   _UpperCAmelCase 						=       get_pipeline_model_mapping(a_					)
   if len(a_					) == 0:
      return ""

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

   return text
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
   if not issubclass(a_     ,	unittest.TestCase					):
      return False
   return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							Any:
   _UpperCAmelCase 						=       [x for x in get_test_classes(a_					) if is_valid_test_class(a_					)]

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

   return target_test_class
def       __UpperCAmelCase		(		a_      :		Union[str, Any]     ,	a_      :		str     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       start_idx
   for idx, line in enumerate(lines[start_idx:]					):
      _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
      if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
         _UpperCAmelCase 						=       start_idx + idx
      elif idx > 0 and indent <= indent_level:
         # Outside the definition block of `pipeline_model_mapping`
         break

   return end_idx
def       __UpperCAmelCase		(		a_      :		Tuple     ,	a_      :		Union[str, Any]=False					)  ->							str:
   if getattr(a_     ,	"pipeline_model_mapping"     ,	a_					) is not None:
      if not overwrite:
         return "", -1

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

   # The code defined the class `test_class`
   _UpperCAmelCase						,				_UpperCAmelCase 						=       inspect.getsourcelines(a_					)
   # `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(a_					):
      if line.lstrip().startswith("class "					):
         _UpperCAmelCase 						=       class_lines[idx:]
         class_start_line_no += idx
         break
   _UpperCAmelCase 						=       class_start_line_no + len(a_					) - 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`.
   _UpperCAmelCase 						=       None
   # The indent level of the line at `class_lines[start_idx]` (if defined)
   _UpperCAmelCase 						=       0
   # To record if `pipeline_model_mapping` is found in `test_class`.
   _UpperCAmelCase 						=       None
   for idx, line in enumerate(a_					):
      if line.strip().startswith("all_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("all_generative_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("pipeline_model_mapping = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
         _UpperCAmelCase 						=       line
         break

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

   # 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
   _UpperCAmelCase 						=       re.compile(r"\s(is_\S+?_available\(\))\s"					)
   for line in class_lines[start_idx : end_idx + 1]:
      _UpperCAmelCase 						=       r.search(a_					)
      if backend_condition is not None:
         # replace the leading and trailing whitespaces to the space character " ".
         _UpperCAmelCase 						=       " " + backend_condition[0][1:-1] + " "
         _UpperCAmelCase 						=       r.sub(a_     ,	a_					)
         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`.
      _UpperCAmelCase 						=       end_idx
   else:
      # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
      _UpperCAmelCase 						=       start_idx - 1
      # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
      for idx in range(a_     ,	end_idx + 1					):
         # These lines are going to be removed before writing to the test file.
         _UpperCAmelCase 						=       None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
   _UpperCAmelCase 						=       [x.__name__ for x in test_class.__bases__]
   if "PipelineTesterMixin" not in parent_classes:
      # Put `PipelineTesterMixin` just before `unittest.TestCase`
      _UpperCAmelCase 						=       [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"					)
      _UpperCAmelCase 						=       ", ".join(_parent_classes					)
      for idx, line in enumerate(a_					):
         # 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					):
               _UpperCAmelCase 						=       None  # noqa
            break
        # Add the new, one-line, class declaration for `test_class`
      _UpperCAmelCase 						=       F'''class {test_class.__name__}({parent_classes}):\n'''

   # Add indentation
   _UpperCAmelCase 						=       " " * indent_level + line_to_add
   # Insert `pipeline_model_mapping` to `class_lines`.
   # (The line at `target_idx` should be kept by definition!)
   _UpperCAmelCase 						=       class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
   # Remove the lines that are marked to be removed
   _UpperCAmelCase 						=       [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)
   _UpperCAmelCase 						=       inspect.getsourcelines(inspect.getmodule(a_					)					)[0]
   # Be careful with the 1-off between line numbers and array indices
   _UpperCAmelCase 						=       module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
   _UpperCAmelCase 						=       "".join(a_					)

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

   return line_to_add
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		Optional[Any]=False					)  ->							Dict:
   _UpperCAmelCase 						=       find_test_class(a_					)
   if test_class:
      add_pipeline_model_mapping(a_     ,	overwrite=a_					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_  					= 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`.',
  )
  SCREAMING_SNAKE_CASE_  					= 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.')

  SCREAMING_SNAKE_CASE_  					= []
  if args.test_file:
    SCREAMING_SNAKE_CASE_  					= [args.test_file]
  else:
    SCREAMING_SNAKE_CASE_  					= 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)

