
import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
		from transformers import (
		    FlaxBertModel,
		    FlaxCLIPVisionModel,
		    FlaxVisionTextDualEncoderModel,
		    FlaxViTModel,
		    VisionTextDualEncoderConfig,
		    VisionTextDualEncoderProcessor,
		)
		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)


if is_torch_available():
		import torch

		from transformers import VisionTextDualEncoderModel

if is_vision_available():
		from PIL import Image


def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		if isinstance(_SCREAMING_SNAKE_CASE						,  collections.abc.Iterable ):
				return x
		return (x, x)







@require_flax
class        lowerCAmelCase__      :

						def  A_				(			self       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

								pass

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								pass

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								pass

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	np.abs((a - b)				).max()
								self.assertLessEqual(__UpperCamelCase       ,							__UpperCamelCase       ,							F'Difference between torch and flax is {diff} (>= {tol}).'				)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a=None       ,							**a				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase       ,							__UpperCamelCase				)

								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel(__UpperCamelCase				)

								_UpperCamelCase						    =	model(input_ids=__UpperCamelCase       ,							pixel_values=__UpperCamelCase       ,							attention_mask=__UpperCamelCase				)

								self.assertEqual(output["""text_embeds"""].shape       ,							(input_ids.shape[0], config.projection_dim)				)
								self.assertEqual(output["""image_embeds"""].shape       ,							(pixel_values.shape[0], config.projection_dim)				)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a=None       ,							**a				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.get_vision_text_model(__UpperCamelCase       ,							__UpperCamelCase				)
								_UpperCamelCase						    =	{"""vision_model""": vision_model, """text_model""": text_model}
								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase				)

								_UpperCamelCase						    =	model(input_ids=__UpperCamelCase       ,							pixel_values=__UpperCamelCase       ,							attention_mask=__UpperCamelCase				)

								self.assertEqual(output["""text_embeds"""].shape       ,							(input_ids.shape[0], model.config.projection_dim)				)
								self.assertEqual(output["""image_embeds"""].shape       ,							(pixel_values.shape[0], model.config.projection_dim)				)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a=None       ,							**a				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.get_vision_text_model(__UpperCamelCase       ,							__UpperCamelCase				)
								_UpperCamelCase						    =	{"""vision_model""": vision_model, """text_model""": text_model}
								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase				)

								_UpperCamelCase						    =	model(input_ids=__UpperCamelCase       ,							pixel_values=__UpperCamelCase       ,							attention_mask=__UpperCamelCase				)
								_UpperCamelCase						    =	output[0]

								with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__UpperCamelCase				)
										_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase				)

										_UpperCamelCase						    =	model(input_ids=__UpperCamelCase       ,							pixel_values=__UpperCamelCase       ,							attention_mask=__UpperCamelCase				)
										_UpperCamelCase						    =	after_output[0]
										_UpperCamelCase						    =	np.amax(np.abs(out_a - out_a				)				)
										self.assertLessEqual(__UpperCamelCase       ,							1e-3				)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a=None       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.get_vision_text_model(__UpperCamelCase       ,							__UpperCamelCase				)
								_UpperCamelCase						    =	{"""vision_model""": vision_model, """text_model""": text_model}
								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase				)

								_UpperCamelCase						    =	model(
								    input_ids=__UpperCamelCase       ,							pixel_values=__UpperCamelCase       ,							attention_mask=__UpperCamelCase       ,							output_attentions=__UpperCamelCase				)

								_UpperCamelCase						    =	output.vision_model_output.attentions
								self.assertEqual(len(__UpperCamelCase				)       ,							vision_config.num_hidden_layers				)

								# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
								_UpperCamelCase						    =	to_atuple(vision_model.config.image_size				)
								_UpperCamelCase						    =	to_atuple(vision_model.config.patch_size				)
								_UpperCamelCase						    =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								_UpperCamelCase						    =	num_patches + 1
								self.assertEqual(vision_attentions[0].shape[-3:]       ,							(vision_config.num_attention_heads, seq_len, seq_len)				)

								_UpperCamelCase						    =	output.text_model_output.attentions
								self.assertEqual(len(__UpperCamelCase				)       ,							text_config.num_hidden_layers				)

								self.assertEqual(
								    text_attentions[0].shape[-3:]       ,							(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])       ,							)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								pt_model.to(__UpperCamelCase				)
								pt_model.eval()

								# prepare inputs
								_UpperCamelCase						    =	inputs_dict
								_UpperCamelCase						    =	{k: torch.tensor(v.tolist()				) for k, v in flax_inputs.items()}

								with torch.no_grad():
										_UpperCamelCase						    =	pt_model(**__UpperCamelCase				).to_tuple()

								_UpperCamelCase						    =	fx_model(**__UpperCamelCase				).to_tuple()
								self.assertEqual(len(__UpperCamelCase				)       ,							len(__UpperCamelCase				)       ,							"""Output lengths differ between Flax and PyTorch"""				)
								for fx_output, pt_output in zip(fx_outputs[:4]       ,							pt_outputs[:4]				):
										self.assert_almost_equals(__UpperCamelCase       ,							pt_output.numpy()       ,							4e-2				)

								# PT -> Flax
								with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(__UpperCamelCase				)
										_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase       ,							from_pt=__UpperCamelCase				)

								_UpperCamelCase						    =	fx_model_loaded(**__UpperCamelCase				).to_tuple()
								self.assertEqual(len(__UpperCamelCase				)       ,							len(__UpperCamelCase				)       ,							"""Output lengths differ between Flax and PyTorch"""				)
								for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]       ,							pt_outputs[:4]				):
										self.assert_almost_equals(__UpperCamelCase       ,							pt_output.numpy()       ,							4e-2				)

								# Flax -> PT
								with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(__UpperCamelCase				)
										_UpperCamelCase						    =	VisionTextDualEncoderModel.from_pretrained(__UpperCamelCase       ,							from_flax=__UpperCamelCase				)

								pt_model_loaded.to(__UpperCamelCase				)
								pt_model_loaded.eval()

								with torch.no_grad():
										_UpperCamelCase						    =	pt_model_loaded(**__UpperCamelCase				).to_tuple()

								self.assertEqual(len(__UpperCamelCase				)       ,							len(__UpperCamelCase				)       ,							"""Output lengths differ between Flax and PyTorch"""				)
								for fx_output, pt_output_loaded in zip(fx_outputs[:4]       ,							pt_outputs_loaded[:4]				):
										self.assert_almost_equals(__UpperCamelCase       ,							pt_output_loaded.numpy()       ,							4e-2				)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase       ,							__UpperCamelCase				)

								_UpperCamelCase						    =	VisionTextDualEncoderModel(__UpperCamelCase				)
								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel(__UpperCamelCase				)

								_UpperCamelCase						    =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,							__UpperCamelCase				)
								_UpperCamelCase						    =	fx_state

								self.check_pt_flax_equivalence(__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase       ,							__UpperCamelCase				)

								_UpperCamelCase						    =	VisionTextDualEncoderModel(__UpperCamelCase				)
								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel(__UpperCamelCase				)

								_UpperCamelCase						    =	load_flax_weights_in_pytorch_model(__UpperCamelCase       ,							fx_model.params				)

								self.check_pt_flax_equivalence(__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**__UpperCamelCase				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**__UpperCamelCase				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.prepare_config_and_inputs()
								self.check_save_load(**__UpperCamelCase				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**__UpperCamelCase				)

						@is_pt_flax_cross_test
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.prepare_config_and_inputs()
								_UpperCamelCase						    =	config_inputs_dict.pop("""vision_config"""				)
								_UpperCamelCase						    =	config_inputs_dict.pop("""text_config"""				)

								_UpperCamelCase						    =	config_inputs_dict

								self.check_equivalence_pt_to_flax(__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase				)
								self.check_equivalence_flax_to_pt(__UpperCamelCase       ,							__UpperCamelCase       ,							__UpperCamelCase				)







						@slow
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.get_pretrained_model_and_inputs()

								_UpperCamelCase						    =	model_a(**__UpperCamelCase				)
								_UpperCamelCase						    =	outputs[0]

								with tempfile.TemporaryDirectory() as tmp_dirname:
										model_a.save_pretrained(__UpperCamelCase				)
										_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase				)

										_UpperCamelCase						    =	model_a(**__UpperCamelCase				)
										_UpperCamelCase						    =	after_outputs[0]
										_UpperCamelCase						    =	np.amax(np.abs(out_a - out_a				)				)
										self.assertLessEqual(__UpperCamelCase       ,							1e-5				)







@require_flax
class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):

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


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-vit"""       ,							"""hf-internal-testing/tiny-bert"""       ,							vision_from_pt=__UpperCamelCase       ,							text_from_pt=__UpperCamelCase       ,							)
								_UpperCamelCase						    =	13
								_UpperCamelCase						    =	floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]				)
								_UpperCamelCase						    =	ids_tensor([batch_size, 4]       ,							model.config.text_config.vocab_size				)
								_UpperCamelCase						    =	random_attention_mask([batch_size, 4]				)
								_UpperCamelCase						    =	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

								return model, inputs

						def  A_				(			self       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxViTModel(__UpperCamelCase				)
								_UpperCamelCase						    =	FlaxBertModel(__UpperCamelCase				)
								return vision_model, text_model







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


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxViTModelTester(self				)
								_UpperCamelCase						    =	FlaxBertModelTester(self				)
								_UpperCamelCase						    =	vit_model_tester.prepare_config_and_inputs()
								_UpperCamelCase						    =	bert_model_tester.prepare_config_and_inputs()

								_UpperCamelCase    ,	_UpperCamelCase						    =	vision_config_and_inputs

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}







@require_torch
class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):

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


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-clip"""       ,							"""hf-internal-testing/tiny-bert"""       ,							vision_from_pt=__UpperCamelCase       ,							text_from_pt=__UpperCamelCase       ,							)
								_UpperCamelCase						    =	13
								_UpperCamelCase						    =	floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]				)
								_UpperCamelCase						    =	ids_tensor([batch_size, 4]       ,							model.config.text_config.vocab_size				)
								_UpperCamelCase						    =	random_attention_mask([batch_size, 4]				)
								_UpperCamelCase						    =	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

								return model, inputs

						def  A_				(			self       ,							a       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxCLIPVisionModel(__UpperCamelCase				)
								_UpperCamelCase						    =	FlaxBertModel(__UpperCamelCase				)
								return vision_model, text_model







						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxCLIPVisionModelTester(self				)
								_UpperCamelCase						    =	FlaxBertModelTester(self				)
								_UpperCamelCase						    =	clip_model_tester.prepare_config_and_inputs()
								_UpperCamelCase						    =	bert_model_tester.prepare_config_and_inputs()

								_UpperCamelCase    ,	_UpperCamelCase						    =	vision_config_and_inputs

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}







@require_flax
@require_vision
class        lowerCAmelCase__      (   unittest.TestCase	):

						@slow
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""       ,							logit_scale_init_value=1.0				)
								_UpperCamelCase						    =	VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""				)

								_UpperCamelCase						    =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
								_UpperCamelCase						    =	processor(
								    text=["""una foto di un gatto""", """una foto di un cane"""]       ,							images=__UpperCamelCase       ,							padding=__UpperCamelCase       ,							return_tensors="""np"""				)

								_UpperCamelCase						    =	model(**__UpperCamelCase				)

								# verify the logits
								self.assertEqual(outputs.logits_per_image.shape       ,							(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])				)
								self.assertEqual(
								    outputs.logits_per_text.shape       ,							(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])       ,							)

								_UpperCamelCase						    =	np.array([[1.228_4727, 0.310_4122]]				)

								self.assertTrue(np.allclose(outputs.logits_per_image       ,							__UpperCamelCase       ,							atol=1e-3				)				)





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		return ConvertCommand(
		    args.model_type						,  args.tf_checkpoint						,  args.pytorch_dump_output						,  args.config						,  args.finetuning_task_name )


lowerCamelCase__	=							"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser(
								    """convert"""       ,							help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""       ,							)
								train_parser.add_argument("""--model_type"""       ,							type=a       ,							required=a       ,							help="""Model's type."""				)
								train_parser.add_argument(
								    """--tf_checkpoint"""       ,							type=a       ,							required=a       ,							help="""TensorFlow checkpoint path or folder."""				)
								train_parser.add_argument(
								    """--pytorch_dump_output"""       ,							type=a       ,							required=a       ,							help="""Path to the PyTorch saved model output."""				)
								train_parser.add_argument("""--config"""       ,							type=a       ,							default=""""""       ,							help="""Configuration file path or folder."""				)
								train_parser.add_argument(
								    """--finetuning_task_name"""       ,							type=a       ,							default=a       ,							help="""Optional fine-tuning task name if the TF model was a finetuned model."""       ,							)
								train_parser.set_defaults(func=a				)

						def __init__(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							*a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	logging.get_logger("""transformers-cli/converting"""				)

								self._logger.info(F'Loading model {model_type}'				)
								_UpperCamelCase						    =	model_type
								_UpperCamelCase						    =	tf_checkpoint
								_UpperCamelCase						    =	pytorch_dump_output
								_UpperCamelCase						    =	config
								_UpperCamelCase						    =	finetuning_task_name







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								if self._model_type == "albert":
										try:
												from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "bert":
										try:
												from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "funnel":
										try:
												from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "t5":
										try:
												from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "gpt":
										from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
										    convert_openai_checkpoint_to_pytorch,
										)

										convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "transfo_xl":
										try:
												from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
												    convert_transfo_xl_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										if "ckpt" in self._tf_checkpoint.lower():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										convert_transfo_xl_checkpoint_to_pytorch(
										    a       ,							self._config       ,							self._pytorch_dump_output       ,							a				)
								elif self._model_type == "gpt2":
										try:
												from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
												    convert_gpta_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "xlnet":
										try:
												from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
												    convert_xlnet_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_xlnet_checkpoint_to_pytorch(
										    self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output       ,							self._finetuning_task_name				)
								elif self._model_type == "xlm":
										from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
										    convert_xlm_checkpoint_to_pytorch,
										)

										convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "lxmert":
										from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
										    convert_lxmert_checkpoint_to_pytorch,
										)

										convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "rembert":
										from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
										    convert_rembert_tf_checkpoint_to_pytorch,
										)

										convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								else:
										raise ValueError(
										    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""				)



