


from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		)

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor







class        __lowercase       :



		"""simple docstring"""

		def __init__( self		,      A		,      A=2		,      A=3		,      A=4		,      A=2		,      A=7		,      A=True		,      A=True		,      A=True		,      A=True		,      A=99		,      A=36		,      A=2		,      A=4		,      A=37		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=16		,      A=2		,      A=0.02		,      A=6		,      A=6		,      A=3		,      A=4		,      A=None		,      A=10_00		,      )  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  parent
					lowerCamelCase    		=  batch_size
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  image_size
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  is_training
					lowerCamelCase    		=  use_input_mask
					lowerCamelCase    		=  use_token_type_ids
					lowerCamelCase    		=  use_labels
					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  max_position_embeddings
					lowerCamelCase    		=  type_vocab_size
					lowerCamelCase    		=  type_sequence_label_size
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  coordinate_size
					lowerCamelCase    		=  shape_size
					lowerCamelCase    		=  num_labels
					lowerCamelCase    		=  num_choices
					lowerCamelCase    		=  scope
					lowerCamelCase    		=  range_bbox

					# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
					lowerCamelCase    		=  text_seq_length
					lowerCamelCase    		=  (image_size // patch_size) ** 2 + 1
					lowerCamelCase    		=  self.text_seq_length + self.image_seq_length







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  ids_tensor([self.batch_size, self.text_seq_length]		,      self.vocab_size	)

					lowerCamelCase    		=  ids_tensor([self.batch_size, self.text_seq_length, 4]		,      self.range_bbox	)
					lowerCamelCase    		=  bbox.numpy()
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]	):
								for j in range(bbox.shape[1]	):
											if bbox[i, j, 3] < bbox[i, j, 1]:
														lowerCamelCase    		=  bbox[i, j, 3]
														lowerCamelCase    		=  bbox[i, j, 1]
														lowerCamelCase    		=  tmp_coordinate
											if bbox[i, j, 2] < bbox[i, j, 0]:
														lowerCamelCase    		=  bbox[i, j, 2]
														lowerCamelCase    		=  bbox[i, j, 0]
														lowerCamelCase    		=  tmp_coordinate
					lowerCamelCase    		=  tf.constant(A	)

					lowerCamelCase    		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

					lowerCamelCase    		=  None
					if self.use_input_mask:
								lowerCamelCase    		=  random_attention_mask([self.batch_size, self.text_seq_length]	)

					lowerCamelCase    		=  None
					if self.use_token_type_ids:
								lowerCamelCase    		=  ids_tensor([self.batch_size, self.text_seq_length]		,      self.type_vocab_size	)

					lowerCamelCase    		=  None
					lowerCamelCase    		=  None
					if self.use_labels:
								lowerCamelCase    		=  ids_tensor([self.batch_size]		,      self.type_sequence_label_size	)
								lowerCamelCase    		=  ids_tensor([self.batch_size, self.text_seq_length]		,      self.num_labels	)

					lowerCamelCase    		=  LayoutLMvaConfig(
					    vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      type_vocab_size=self.type_vocab_size		,      initializer_range=self.initializer_range		,      coordinate_size=self.coordinate_size		,      shape_size=self.shape_size		,      input_size=self.image_size		,      patch_size=self.patch_size		,      )

					return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







		def 					__A   ( self		,      A		,      A		,      A		,      A		,      A		,      A	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  TFLayoutLMvaModel(config=A	)

					# text + image
					lowerCamelCase    		=  model(A		,      pixel_values=A		,      training=A	)
					lowerCamelCase    		=  model(
					    A		,      bbox=A		,      pixel_values=A		,      attention_mask=A		,      token_type_ids=A		,      training=A		,      )
					lowerCamelCase    		=  model(A		,      bbox=A		,      pixel_values=A		,      training=A	)

					self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)	)

					# text only
					lowerCamelCase    		=  model(A		,      training=A	)
					self.parent.assertEqual(
					    result.last_hidden_state.shape		,      (self.batch_size, self.text_seq_length, self.hidden_size)	)

					# image only
					lowerCamelCase    		=  model({"""pixel_values""": pixel_values}		,      training=A	)
					self.parent.assertEqual(
					    result.last_hidden_state.shape		,      (self.batch_size, self.image_seq_length, self.hidden_size)	)







		def 					__A   ( self		,      A		,      A		,      A		,      A		,      A		,      A		,      A	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  self.num_labels
					lowerCamelCase    		=  TFLayoutLMvaForSequenceClassification(config=A	)
					lowerCamelCase    		=  model(
					    A		,      bbox=A		,      pixel_values=A		,      attention_mask=A		,      token_type_ids=A		,      labels=A		,      training=A		,      )
					self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)	)







		def 					__A   ( self		,      A		,      A		,      A		,      A		,      A		,      A		,      A	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  self.num_labels
					lowerCamelCase    		=  TFLayoutLMvaForTokenClassification(config=A	)
					lowerCamelCase    		=  model(
					    A		,      bbox=A		,      pixel_values=A		,      attention_mask=A		,      token_type_ids=A		,      labels=A		,      training=A		,      )
					self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.text_seq_length, self.num_labels)	)







		def 					__A   ( self		,      A		,      A		,      A		,      A		,      A		,      A		,      A	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  2
					lowerCamelCase    		=  TFLayoutLMvaForQuestionAnswering(config=A	)
					lowerCamelCase    		=  model(
					    A		,      bbox=A		,      pixel_values=A		,      attention_mask=A		,      token_type_ids=A		,      start_positions=A		,      end_positions=A		,      training=A		,      )
					self.parent.assertEqual(result.start_logits.shape		,      (self.batch_size, self.seq_length)	)
					self.parent.assertEqual(result.end_logits.shape		,      (self.batch_size, self.seq_length)	)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  self.prepare_config_and_inputs()
					((lowerCamelCase)		,		(lowerCamelCase)		,		(lowerCamelCase)		,		(lowerCamelCase)		,		(lowerCamelCase)		,		(lowerCamelCase)		,		(lowerCamelCase)		,		(lowerCamelCase))    		=  config_and_inputs
					lowerCamelCase    		=  {
					    """input_ids""": input_ids,
					    """bbox""": bbox,
					    """pixel_values""": pixel_values,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": input_mask,
					}
					return config, inputs_dict







@require_tf
class        __lowercase       (							a_			,							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		(
		    (
		        TFLayoutLMvaModel,
		        TFLayoutLMvaForQuestionAnswering,
		        TFLayoutLMvaForSequenceClassification,
		        TFLayoutLMvaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		UpperCamelCase     :					List[str]			       =		(
		    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
		    if is_tf_available()
		    else {}
		)

		UpperCamelCase     :					List[str]			       =		False
		UpperCamelCase     :					Any			       =		False
		UpperCamelCase     :					Optional[int]			       =		False







		def 					__A   ( self		,      A		,      A		,      A		,      A		,      A	)  -> Any:







					'''simple docstring'''







					return True







		def 					__A   ( self		,      A		,      A		,      A=False	)  -> dict:







					'''simple docstring'''







					lowerCamelCase    		=  copy.deepcopy(A	)

					if model_class in get_values(A	):
								lowerCamelCase    		=  {
								    k: tf.tile(tf.expand_dims(A		,      1	)		,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)	)
								    if isinstance(A		,      tf.Tensor	) and v.ndim > 0
								    else v
								    for k, v in inputs_dict.items()
								}

					if return_labels:
								if model_class in get_values(A	):
											lowerCamelCase    		=  tf.ones(self.model_tester.batch_size		,      dtype=tf.intaa	)
								elif model_class in get_values(A	):
											lowerCamelCase    		=  tf.zeros(self.model_tester.batch_size		,      dtype=tf.intaa	)
											lowerCamelCase    		=  tf.zeros(self.model_tester.batch_size		,      dtype=tf.intaa	)
								elif model_class in get_values(A	):
											lowerCamelCase    		=  tf.zeros(self.model_tester.batch_size		,      dtype=tf.intaa	)
								elif model_class in get_values(A	):
											lowerCamelCase    		=  tf.zeros(
											    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,      dtype=tf.intaa	)

					return inputs_dict







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  TFLayoutLMvaModelTester(self	)
					lowerCamelCase    		=  ConfigTester(self		,      config_class=A		,      hidden_size=37	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					self.config_tester.run_common_tests()







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase    		=  self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
								lowerCamelCase    		=  model_class(A	)
								if getattr(A		,      """hf_compute_loss"""		,      A	):
											# The number of elements in the loss should be the same as the number of elements in the label
											lowerCamelCase    		=  self._prepare_for_class(inputs_dict.copy()		,      A		,      return_labels=A	)
											lowerCamelCase    		=  prepared_for_class[
											    sorted(prepared_for_class.keys() - inputs_dict.keys()		,      reverse=A	)[0]
											]
											lowerCamelCase    		=  added_label.shape.as_list()[:1]

											# Test that model correctly compute the loss with kwargs
											lowerCamelCase    		=  self._prepare_for_class(inputs_dict.copy()		,      A		,      return_labels=A	)
											lowerCamelCase    		=  prepared_for_class.pop("""input_ids"""	)

											lowerCamelCase    		=  model(A		,      **A	)[0]
											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

											# Test that model correctly compute the loss when we mask some positions
											lowerCamelCase    		=  self._prepare_for_class(inputs_dict.copy()		,      A		,      return_labels=A	)
											lowerCamelCase    		=  prepared_for_class.pop("""input_ids"""	)
											if "labels" in prepared_for_class:
														lowerCamelCase    		=  prepared_for_class["""labels"""].numpy()
														if len(labels.shape	) > 1 and labels.shape[1] != 1:
																	lowerCamelCase    		=  -1_00
																	lowerCamelCase    		=  tf.convert_to_tensor(A	)
																	lowerCamelCase    		=  model(A		,      **A	)[0]
																	self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)
																	self.assertTrue(not np.any(np.isnan(loss.numpy()	)	)	)

                # Test that model correctly compute the loss with a dict
											lowerCamelCase    		=  self._prepare_for_class(inputs_dict.copy()		,      A		,      return_labels=A	)
											lowerCamelCase    		=  model(A	)[0]
											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

											# Test that model correctly compute the loss with a tuple
											lowerCamelCase    		=  self._prepare_for_class(inputs_dict.copy()		,      A		,      return_labels=A	)

											# Get keys that were added with the _prepare_for_class function
											lowerCamelCase    		=  prepared_for_class.keys() - inputs_dict.keys()
											lowerCamelCase    		=  inspect.signature(model.call	).parameters
											lowerCamelCase    		=  list(signature.keys()	)

											# Create a dictionary holding the location of the tensors in the tuple
											lowerCamelCase    		=  {0: """input_ids"""}
											for label_key in label_keys:
														lowerCamelCase    		=  signature_names.index(A	)
														lowerCamelCase    		=  label_key
											lowerCamelCase    		=  sorted(tuple_index_mapping.items()	)
											# Initialize a list with their default values, update the values and convert to a tuple
											lowerCamelCase    		=  []

											for name in signature_names:
														if name != "kwargs":
																	list_input.append(signature[name].default	)

											for index, value in sorted_tuple_index_mapping:
														lowerCamelCase    		=  prepared_for_class[value]

											lowerCamelCase    		=  tuple(A	)

											# Send to model
											lowerCamelCase    		=  model(tuple_input[:-1]	)[0]

											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					(
					    (
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		
					)    		=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(A		,      A		,      A		,      A		,      A		,      A	)







		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					(
					    (
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		
					)    		=  self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
								lowerCamelCase    		=  type
								self.model_tester.create_and_check_model(A		,      A		,      A		,      A		,      A		,      A	)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					(
					    (
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		
					)    		=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(
					    A		,      A		,      A		,      A		,      A		,      A		,      A	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					(
					    (
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		
					)    		=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(
					    A		,      A		,      A		,      A		,      A		,      A		,      A	)







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					(
					    (
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		(
					    lowerCamelCase
					)		,		
					)    		=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(
					    A		,      A		,      A		,      A		,      A		,      A		,      A	)







		@slow
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCamelCase    		=  TFLayoutLMvaModel.from_pretrained(A	)
								self.assertIsNotNone(A	)






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
			return image







@require_tf
class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		@cached_property
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					return LayoutLMvaImageProcessor(apply_ocr=A	) if is_vision_available() else None







		@slow
		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""	)

					lowerCamelCase    		=  self.default_image_processor
					lowerCamelCase    		=  prepare_img()
					lowerCamelCase    		=  image_processor(images=A		,      return_tensors="""tf"""	).pixel_values

					lowerCamelCase    		=  tf.constant([[1, 2]]	)
					lowerCamelCase    		=  tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]	)		,      axis=0	)

					# forward pass
					lowerCamelCase    		=  model(input_ids=A		,      bbox=A		,      pixel_values=A		,      training=A	)

					# verify the logits
					lowerCamelCase    		=  (1, 1_99, 7_68)
					self.assertEqual(outputs.last_hidden_state.shape		,      A	)

					lowerCamelCase    		=  tf.constant(
					    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	)

					self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]		,      A		,      atol=1e-4	)	)





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

UpperCAmelCase   :    Tuple     =		[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

UpperCAmelCase   :    Any     =		[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

UpperCAmelCase   :    int     =		[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    Dict     =		f"""input_blocks.{3*i + j + 1}.0."""
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						UpperCAmelCase   :    List[Any]     =		f"""down_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Optional[int]     =		f"""input_blocks.{3*i + j + 1}.1."""
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		f"""output_blocks.{3*i + j}.0."""
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Tuple     =		f"""output_blocks.{3*i + j}.1."""
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				UpperCAmelCase   :    int     =		f"""down_blocks.{i}.downsamplers.0.conv."""
				UpperCAmelCase   :    Union[str, Any]     =		f"""input_blocks.{3*(i+1)}.0.op."""
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    str     =		f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCAmelCase   :    str     =		"mid_block.attentions.0."
UpperCAmelCase   :    int     =		"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		UpperCAmelCase   :    int     =		f"""mid_block.resnets.{j}."""
		UpperCAmelCase   :    List[str]     =		f"""middle_block.{2*j}."""
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  {k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						lowerCamelCase    		=  sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
									lowerCamelCase    		=  v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v
			lowerCamelCase    		=  {v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

UpperCAmelCase   :    int     =		[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				UpperCAmelCase   :    List[Any]     =		f"""encoder.down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		f"""encoder.down.{i}.block.{j}."""
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.downsamplers.0."""
				UpperCAmelCase   :    Tuple     =		f"""down.{i}.downsample."""
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    List[str]     =		f"""up.{3-i}.upsample."""
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				UpperCAmelCase   :    Any     =		f"""decoder.up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    str     =		f"""decoder.up.{3-i}.block.{j}."""
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		UpperCAmelCase   :    Dict     =		f"""mid_block.resnets.{i}."""
		UpperCAmelCase   :    Union[str, Any]     =		f"""mid.block_{i+1}."""
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCAmelCase   :    Tuple     =		[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			return w.reshape(*w.shape  ,							1  ,							1      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
									lowerCamelCase    		=  v
			lowerCamelCase    		=  {v: vae_state_dict[k] for k, v in mapping.items()}
			lowerCamelCase    		=  ["""q""", """k""", """v""", """proj_out"""]
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if f'mid.attn_1.{weight_name}.weight' in k:
												print(f'Reshaping {k} for SD format'      )
												lowerCamelCase    		=  reshape_weight_for_sd(lowerCamelCase__      )
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


UpperCAmelCase   :    Union[str, Any]     =		[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
UpperCAmelCase   :    str     =		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCAmelCase   :    List[Any]     =		re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCAmelCase   :    int     =		{"q": 0, "k": 1, "v": 2}






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith(""".self_attn.q_proj.weight"""      )
						    or k.endswith(""".self_attn.k_proj.weight"""      )
						    or k.endswith(""".self_attn.v_proj.weight"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.weight"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.weight"""      )]
									if k_pre not in capture_qkv_weight:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  v
									continue

						if (
						    k.endswith(""".self_attn.q_proj.bias"""      )
						    or k.endswith(""".self_attn.k_proj.bias"""      )
						    or k.endswith(""".self_attn.v_proj.bias"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.bias"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.bias"""      )]
									if k_pre not in capture_qkv_bias:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  v
									continue

						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  torch.cat(lowerCamelCase__      )

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  torch.cat(lowerCamelCase__      )

			return new_state_dict






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			return text_enc_dict


if __name__ == "__main__":
		UpperCAmelCase   :    List[str]     =		argparse.ArgumentParser()

		parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
		parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
		parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
		parser.add_argument(
		    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
		)

		UpperCAmelCase   :    List[str]     =		parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		UpperCAmelCase   :    int     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "text_encoder", "model.safetensors")

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				UpperCAmelCase   :    Tuple     =		load_file(unet_path, device="cpu")
		else:
				UpperCAmelCase   :    Tuple     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    List[Any]     =		torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				UpperCAmelCase   :    Any     =		load_file(vae_path, device="cpu")
		else:
				UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    Dict     =		torch.load(vae_path, map_location="cpu")

		if osp.exists(text_enc_path):
				UpperCAmelCase   :    str     =		load_file(text_enc_path, device="cpu")
		else:
				UpperCAmelCase   :    Optional[int]     =		osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
				UpperCAmelCase   :    str     =		torch.load(text_enc_path, map_location="cpu")

		# Convert the UNet model
		UpperCAmelCase   :    List[str]     =		convert_unet_state_dict(unet_state_dict)
		UpperCAmelCase   :    List[str]     =		{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		UpperCAmelCase   :    Optional[Any]     =		convert_vae_state_dict(vae_state_dict)
		UpperCAmelCase   :    Union[str, Any]     =		{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		UpperCAmelCase   :    str     =		"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				UpperCAmelCase   :    Optional[Any]     =		{"transformer." + k: v for k, v in text_enc_dict.items()}
				UpperCAmelCase   :    List[str]     =		convert_text_enc_state_dict_vaa(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				UpperCAmelCase   :    int     =		convert_text_enc_state_dict(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		UpperCAmelCase   :    Dict     =		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				UpperCAmelCase   :    List[Any]     =		{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				UpperCAmelCase   :    Union[str, Any]     =		{"state_dict": state_dict}
				torch.save(state_dict, args.checkpoint_path)

