




"""simple docstring"""






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 lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=2  ,      lowercase=3  ,      lowercase=4  ,      lowercase=2  ,      lowercase=7  ,      lowercase=True  ,      lowercase=True  ,      lowercase=True  ,      lowercase=True  ,      lowercase=99  ,      lowercase=36  ,      lowercase=2  ,      lowercase=4  ,      lowercase=37  ,      lowercase="gelu"  ,      lowercase=0.1  ,      lowercase=0.1  ,      lowercase=512  ,      lowercase=16  ,      lowercase=2  ,      lowercase=0.02  ,      lowercase=6  ,      lowercase=6  ,      lowercase=3  ,      lowercase=4  ,      lowercase=None  ,      lowercase=1000  ,      ):
							_lowerCamelCase     :			int    =    parent
							_lowerCamelCase     :			Optional[Any]    =    batch_size
							_lowerCamelCase     :			Union[str, Any]    =    num_channels
							_lowerCamelCase     :			Dict    =    image_size
							_lowerCamelCase     :			Union[str, Any]    =    patch_size
							_lowerCamelCase     :			Dict    =    is_training
							_lowerCamelCase     :			List[str]    =    use_input_mask
							_lowerCamelCase     :			int    =    use_token_type_ids
							_lowerCamelCase     :			Dict    =    use_labels
							_lowerCamelCase     :			Union[str, Any]    =    vocab_size
							_lowerCamelCase     :			List[Any]    =    hidden_size
							_lowerCamelCase     :			Optional[int]    =    num_hidden_layers
							_lowerCamelCase     :			List[str]    =    num_attention_heads
							_lowerCamelCase     :			int    =    intermediate_size
							_lowerCamelCase     :			List[str]    =    hidden_act
							_lowerCamelCase     :			Tuple    =    hidden_dropout_prob
							_lowerCamelCase     :			Optional[int]    =    attention_probs_dropout_prob
							_lowerCamelCase     :			List[Any]    =    max_position_embeddings
							_lowerCamelCase     :			Dict    =    type_vocab_size
							_lowerCamelCase     :			str    =    type_sequence_label_size
							_lowerCamelCase     :			Tuple    =    initializer_range
							_lowerCamelCase     :			Dict    =    coordinate_size
							_lowerCamelCase     :			Tuple    =    shape_size
							_lowerCamelCase     :			List[Any]    =    num_labels
							_lowerCamelCase     :			Tuple    =    num_choices
							_lowerCamelCase     :			int    =    scope
							_lowerCamelCase     :			Optional[Any]    =    range_bbox

							# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
							_lowerCamelCase     :			Dict    =    text_seq_length
							_lowerCamelCase     :			List[Any]    =    (image_size // patch_size) ** 2 + 1
							_lowerCamelCase     :			Union[str, Any]    =    self.text_seq_length + self.image_seq_length
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    ids_tensor([self.batch_size, self.text_seq_length]  ,      self.vocab_size  )

							_lowerCamelCase     :			Dict    =    ids_tensor([self.batch_size, self.text_seq_length, 4]  ,      self.range_bbox  )
							_lowerCamelCase     :			Optional[int]    =    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     :			Dict    =    bbox[i, j, 3]
																			_lowerCamelCase     :			Optional[int]    =    bbox[i, j, 1]
																			_lowerCamelCase     :			Any    =    tmp_coordinate
															if bbox[i, j, 2] < bbox[i, j, 0]:
																			_lowerCamelCase     :			List[str]    =    bbox[i, j, 2]
																			_lowerCamelCase     :			Optional[int]    =    bbox[i, j, 0]
																			_lowerCamelCase     :			Tuple    =    tmp_coordinate
							_lowerCamelCase     :			Union[str, Any]    =    tf.constant(UpperCamelCase__  )

							_lowerCamelCase     :			Any    =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

							_lowerCamelCase     :			Optional[int]    =    None
							if self.use_input_mask:
											_lowerCamelCase     :			Optional[int]    =    random_attention_mask([self.batch_size, self.text_seq_length]  )

							_lowerCamelCase     :			Optional[int]    =    None
							if self.use_token_type_ids:
											_lowerCamelCase     :			List[Any]    =    ids_tensor([self.batch_size, self.text_seq_length]  ,      self.type_vocab_size  )

							_lowerCamelCase     :			str    =    None
							_lowerCamelCase     :			Optional[int]    =    None
							if self.use_labels:
											_lowerCamelCase     :			Any    =    ids_tensor([self.batch_size]  ,      self.type_sequence_label_size  )
											_lowerCamelCase     :			Tuple    =    ids_tensor([self.batch_size, self.text_seq_length]  ,      self.num_labels  )

							_lowerCamelCase     :			Any    =    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  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    TFLayoutLMvaModel(config=UpperCamelCase__  )

							# text + image
							_lowerCamelCase     :			Any    =    model(UpperCamelCase__  ,      pixel_values=UpperCamelCase__  ,      training=UpperCamelCase__  )
							_lowerCamelCase     :			Optional[int]    =    model(
							    UpperCamelCase__  ,      bbox=UpperCamelCase__  ,      pixel_values=UpperCamelCase__  ,      attention_mask=UpperCamelCase__  ,      token_type_ids=UpperCamelCase__  ,      training=UpperCamelCase__  ,      )
							_lowerCamelCase     :			Any    =    model(UpperCamelCase__  ,      bbox=UpperCamelCase__  ,      pixel_values=UpperCamelCase__  ,      training=UpperCamelCase__  )

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

							# text only
							_lowerCamelCase     :			Dict    =    model(UpperCamelCase__  ,      training=UpperCamelCase__  )
							self.parent.assertEqual(
							    result.last_hidden_state.shape  ,      (self.batch_size, self.text_seq_length, self.hidden_size)  )

							# image only
							_lowerCamelCase     :			Union[str, Any]    =    model({'pixel_values': pixel_values}  ,      training=UpperCamelCase__  )
							self.parent.assertEqual(
							    result.last_hidden_state.shape  ,      (self.batch_size, self.image_seq_length, self.hidden_size)  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			str    =    self.num_labels
							_lowerCamelCase     :			Optional[Any]    =    TFLayoutLMvaForSequenceClassification(config=UpperCamelCase__  )
							_lowerCamelCase     :			str    =    model(
							    UpperCamelCase__  ,      bbox=UpperCamelCase__  ,      pixel_values=UpperCamelCase__  ,      attention_mask=UpperCamelCase__  ,      token_type_ids=UpperCamelCase__  ,      labels=UpperCamelCase__  ,      training=UpperCamelCase__  ,      )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			int    =    self.num_labels
							_lowerCamelCase     :			str    =    TFLayoutLMvaForTokenClassification(config=UpperCamelCase__  )
							_lowerCamelCase     :			Tuple    =    model(
							    UpperCamelCase__  ,      bbox=UpperCamelCase__  ,      pixel_values=UpperCamelCase__  ,      attention_mask=UpperCamelCase__  ,      token_type_ids=UpperCamelCase__  ,      labels=UpperCamelCase__  ,      training=UpperCamelCase__  ,      )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.text_seq_length, self.num_labels)  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    2
							_lowerCamelCase     :			Dict    =    TFLayoutLMvaForQuestionAnswering(config=UpperCamelCase__  )
							_lowerCamelCase     :			Tuple    =    model(
							    UpperCamelCase__  ,      bbox=UpperCamelCase__  ,      pixel_values=UpperCamelCase__  ,      attention_mask=UpperCamelCase__  ,      token_type_ids=UpperCamelCase__  ,      start_positions=UpperCamelCase__  ,      end_positions=UpperCamelCase__  ,      training=UpperCamelCase__  ,      )
							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  ):
							_lowerCamelCase     :			List[Any]    =    self.prepare_config_and_inputs()
							(_lowerCamelCase)     :			List[str]    =    config_and_inputs
							_lowerCamelCase     :			Dict    =    {
							    "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 lowerCAmelCase__	(     lowerCAmelCase__,	lowerCAmelCase__,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			(
			    (
			        TFLayoutLMvaModel,
			        TFLayoutLMvaForQuestionAnswering,
			        TFLayoutLMvaForSequenceClassification,
			        TFLayoutLMvaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__      =			(
			    {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__      =			False
			lowerCamelCase__      =			False
			lowerCamelCase__      =			False
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							return True
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Optional[Any]    =    copy.deepcopy(UpperCamelCase__  )

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

							if return_labels:
											if model_class in get_values(UpperCamelCase__  ):
															_lowerCamelCase     :			Dict    =    tf.ones(self.model_tester.batch_size  ,      dtype=tf.intaa  )
											elif model_class in get_values(UpperCamelCase__  ):
															_lowerCamelCase     :			Dict    =    tf.zeros(self.model_tester.batch_size  ,      dtype=tf.intaa  )
															_lowerCamelCase     :			Optional[Any]    =    tf.zeros(self.model_tester.batch_size  ,      dtype=tf.intaa  )
											elif model_class in get_values(UpperCamelCase__  ):
															_lowerCamelCase     :			Dict    =    tf.zeros(self.model_tester.batch_size  ,      dtype=tf.intaa  )
											elif model_class in get_values(UpperCamelCase__  ):
															_lowerCamelCase     :			Union[str, Any]    =    tf.zeros(
															    (self.model_tester.batch_size, self.model_tester.text_seq_length)  ,      dtype=tf.intaa  )

							return inputs_dict
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    TFLayoutLMvaModelTester(self  )
							_lowerCamelCase     :			List[Any]    =    ConfigTester(self  ,      config_class=UpperCamelCase__  ,      hidden_size=37  )
			def 					A_    (  self  ):
							self.config_tester.run_common_tests()
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
											_lowerCamelCase     :			List[Any]    =    model_class(UpperCamelCase__  )
											if getattr(UpperCamelCase__  ,      'hf_compute_loss'  ,      UpperCamelCase__  ):
															# The number of elements in the loss should be the same as the number of elements in the label
															_lowerCamelCase     :			Optional[int]    =    self._prepare_for_class(inputs_dict.copy()  ,      UpperCamelCase__  ,      return_labels=UpperCamelCase__  )
															_lowerCamelCase     :			Union[str, Any]    =    prepared_for_class[
															    sorted(prepared_for_class.keys() - inputs_dict.keys()  ,      reverse=UpperCamelCase__  )[0]
															]
															_lowerCamelCase     :			Optional[Any]    =    added_label.shape.as_list()[:1]

															# Test that model correctly compute the loss with kwargs
															_lowerCamelCase     :			int    =    self._prepare_for_class(inputs_dict.copy()  ,      UpperCamelCase__  ,      return_labels=UpperCamelCase__  )
															_lowerCamelCase     :			Dict    =    prepared_for_class.pop('input_ids'  )

															_lowerCamelCase     :			Optional[int]    =    model(UpperCamelCase__  ,      **UpperCamelCase__  )[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     :			List[Any]    =    self._prepare_for_class(inputs_dict.copy()  ,      UpperCamelCase__  ,      return_labels=UpperCamelCase__  )
															_lowerCamelCase     :			Dict    =    prepared_for_class.pop('input_ids'  )
															if "labels" in prepared_for_class:
																			_lowerCamelCase     :			List[str]    =    prepared_for_class["labels"].numpy()
																			if len(labels.shape  ) > 1 and labels.shape[1] != 1:
																							_lowerCamelCase     :			Optional[int]    =    -100
																							_lowerCamelCase     :			Any    =    tf.convert_to_tensor(UpperCamelCase__  )
																							_lowerCamelCase     :			Dict    =    model(UpperCamelCase__  ,      **UpperCamelCase__  )[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     :			str    =    self._prepare_for_class(inputs_dict.copy()  ,      UpperCamelCase__  ,      return_labels=UpperCamelCase__  )
															_lowerCamelCase     :			Optional[int]    =    model(UpperCamelCase__  )[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     :			Optional[Any]    =    self._prepare_for_class(inputs_dict.copy()  ,      UpperCamelCase__  ,      return_labels=UpperCamelCase__  )

															# Get keys that were added with the _prepare_for_class function
															_lowerCamelCase     :			Any    =    prepared_for_class.keys() - inputs_dict.keys()
															_lowerCamelCase     :			Any    =    inspect.signature(model.call  ).parameters
															_lowerCamelCase     :			List[str]    =    list(signature.keys()  )

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

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

															for index, value in sorted_tuple_index_mapping:
																			_lowerCamelCase     :			Optional[Any]    =    prepared_for_class[value]

															_lowerCamelCase     :			Any    =    tuple(UpperCamelCase__  )

															# Send to model
															_lowerCamelCase     :			List[Any]    =    model(tuple_input[:-1]  )[0]

															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
			def 					A_    (  self  ):
							(
							    _lowerCamelCase      
							)     :			Optional[Any]    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							(
							    _lowerCamelCase      
							)     :			List[str]    =    self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											_lowerCamelCase     :			List[str]    =    type
											self.model_tester.create_and_check_model(UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							(
							    _lowerCamelCase      
							)     :			Tuple    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(
							    UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							(
							    _lowerCamelCase      
							)     :			str    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(
							    UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							(
							    _lowerCamelCase      
							)     :			Optional[int]    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(
							    UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  ,      UpperCamelCase__  )






			@slow
			def 					A_    (  self  ):
							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase     :			Dict    =    TFLayoutLMvaModel.from_pretrained(UpperCamelCase__  )
											self.assertIsNotNone(UpperCamelCase__  )







def      _snake_case			(				):
				_lowerCamelCase     :			Optional[Any]    =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
				return image






@require_tf
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			@cached_property
			def 					A_    (  self  ):
							return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__  ) if is_vision_available() else None






			@slow
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'  )

							_lowerCamelCase     :			Any    =    self.default_image_processor
							_lowerCamelCase     :			Optional[Any]    =    prepare_img()
							_lowerCamelCase     :			Dict    =    image_processor(images=UpperCamelCase__  ,      return_tensors='tf'  ).pixel_values

							_lowerCamelCase     :			Union[str, Any]    =    tf.constant([[1, 2]]  )
							_lowerCamelCase     :			Dict    =    tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]  )  ,      axis=0  )

							# forward pass
							_lowerCamelCase     :			List[Any]    =    model(input_ids=UpperCamelCase__  ,      bbox=UpperCamelCase__  ,      pixel_values=UpperCamelCase__  ,      training=UpperCamelCase__  )

							# verify the logits
							_lowerCamelCase     :			Dict    =    (1, 199, 768)
							self.assertEqual(outputs.last_hidden_state.shape  ,      UpperCamelCase__  )

							_lowerCamelCase     :			Dict    =    tf.constant(
							    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]  )

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





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type',
           choices=['rag_sequence', 'rag_token'],
           required=True,
           type=str,
           help='RAG model type: rag_sequence, rag_token',
       )
       parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
       parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
       parser.add_argument(
           '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
       )

       parser.add_argument(
           '--generator_tokenizer_name_or_path',
           type=str,
           help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
       )
       parser.add_argument(
           '--question_encoder_tokenizer_name_or_path',
           type=str,
           help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
       )
       parser.add_argument(
           '--config_name_or_path',
           type=str,
           help=(
               'Identifier of the model config to use, if not provided, resolves to a base config for a given'
               ' ``model_type``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )






