






"""simple docstring"""







import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image





class   UpperCamelCase_ :



  def __init__(      self ,		snake_case__ ,		snake_case__=2 ,		snake_case__=True ,		snake_case__=False ,		snake_case__=10 ,		snake_case__=3 ,		snake_case__=32 * 8 ,		snake_case__=32 * 8 ,		snake_case__=4 ,		snake_case__=64 ,		)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			is_training
         UpperCAmelCase	    =			use_auxiliary_loss
         UpperCAmelCase	    =			num_queries
         UpperCAmelCase	    =			num_channels
         UpperCAmelCase	    =			min_size
         UpperCAmelCase	    =			max_size
         UpperCAmelCase	    =			num_labels
         UpperCAmelCase	    =			hidden_dim
         UpperCAmelCase	    =			hidden_dim



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



         """simple docstring"""





         UpperCAmelCase	    =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
             snake_case__				)

         UpperCAmelCase	    =			torch.ones([self.batch_size, self.min_size, self.max_size] ,		device=snake_case__				)

         UpperCAmelCase	    =			(
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,		device=snake_case__				) > 0.5
         ).float()
         UpperCAmelCase	    =			(torch.rand((self.batch_size, self.num_labels) ,		device=snake_case__				) > 0.5).long()

         UpperCAmelCase	    =			self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerConfig(
             hidden_size=self.hidden_dim ,		)
         UpperCAmelCase	    =			self.num_queries
         UpperCAmelCase	    =			self.num_labels

         UpperCAmelCase	    =			[1, 1, 1, 1]
         UpperCAmelCase	    =			self.num_channels

         UpperCAmelCase	    =			64
         UpperCAmelCase	    =			1_28
         UpperCAmelCase	    =			self.hidden_dim
         UpperCAmelCase	    =			self.hidden_dim
         UpperCAmelCase	    =			self.hidden_dim
         return config



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



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			self.prepare_config_and_inputs()
         UpperCAmelCase	    =			{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
         return config, inputs_dict



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			output.encoder_hidden_states
         UpperCAmelCase	    =			output.pixel_decoder_hidden_states
         UpperCAmelCase	    =			output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(snake_case__				) ,		len(config.backbone_config.depths				)				)
         self.parent.assertTrue(len(snake_case__				) ,		len(config.backbone_config.depths				)				)
         self.parent.assertTrue(len(snake_case__				) ,		config.decoder_layers				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=False				)			->			Optional[Any]:



         """simple docstring"""





         with torch.no_grad():
                UpperCAmelCase	    =			MaskaFormerModel(config=snake_case__				)
                model.to(snake_case__				)
                model.eval()

                UpperCAmelCase	    =			model(pixel_values=snake_case__ ,		pixel_mask=snake_case__				)
                UpperCAmelCase	    =			model(snake_case__ ,		output_hidden_states=snake_case__				)

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape ,		(self.batch_size, self.num_queries, self.hidden_dim) ,		)
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
         self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

         if output_hidden_states:
                self.check_output_hidden_state(snake_case__ ,		snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation(config=snake_case__				)
         model.to(snake_case__				)
         model.eval()

         def comm_check_on_output(snake_case__				):
                # let's still check that all the required stuff is there
                self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
                self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
                self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
                # okay, now we need to check the logits shape
                # due to the encoder compression, masks have a //4 spatial size
                self.parent.assertEqual(
                    result.masks_queries_logits.shape ,		(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,		)
                # + 1 for null class
                self.parent.assertEqual(
                    result.class_queries_logits.shape ,		(self.batch_size, self.num_queries, self.num_labels + 1)				)

         with torch.no_grad():
                UpperCAmelCase	    =			model(pixel_values=snake_case__ ,		pixel_mask=snake_case__				)
                UpperCAmelCase	    =			model(snake_case__				)

                comm_check_on_output(snake_case__				)

                UpperCAmelCase	    =			model(
                    pixel_values=snake_case__ ,		pixel_mask=snake_case__ ,		mask_labels=snake_case__ ,		class_labels=snake_case__				)

         comm_check_on_output(snake_case__				)

         self.parent.assertTrue(result.loss is not None				)
         self.parent.assertEqual(result.loss.shape ,		torch.Size([1]				)				)





@require_torch
class   UpperCamelCase_ (       a_   ,					a_   ,					unittest.TestCase  ):
  _A       :					Union[str, Any]				=	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  _A       :					Optional[Any]				=	{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

  _A       :					List[str]				=	False
  _A       :					Union[str, Any]				=	False
  _A       :					Tuple				=	False
  _A       :					int				=	False



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



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerModelTester(self				)
         UpperCAmelCase	    =			ConfigTester(self ,		config_class=snake_case__ ,		has_text_modality=snake_case__				)



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



         """simple docstring"""





         self.config_tester.run_common_tests()



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



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(snake_case__ ,		**snake_case__ ,		output_hidden_states=snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case__				)



  @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""				)
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         pass



  @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""				)
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         pass



  @unittest.skip(reason="""Mask2Former is not a generative model"""				)
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         pass



  @unittest.skip(reason="""Mask2Former does not use token embeddings"""				)
  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         pass



  @require_torch_multi_gpu
  @unittest.skip(
      reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""				)
  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         pass



  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase	    =			model_class(snake_case__				)
                UpperCAmelCase	    =			inspect.signature(model.forward				)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                UpperCAmelCase	    =			[*signature.parameters.keys()]

                UpperCAmelCase	    =			["""pixel_values"""]
                self.assertListEqual(arg_names[:1] ,		snake_case__				)



  @slow
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                UpperCAmelCase	    =			MaskaFormerModel.from_pretrained(snake_case__				)
                self.assertIsNotNone(snake_case__				)



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



         """simple docstring"""





         UpperCAmelCase	    =			(self.model_tester.min_size,) * 2
         UpperCAmelCase	    =			{
             """pixel_values""": torch.randn((2, 3, *size) ,		device=snake_case__				),
             """mask_labels""": torch.randn((2, 10, *size) ,		device=snake_case__				),
             """class_labels""": torch.zeros(2 ,		10 ,		device=snake_case__				).long(),
         }
         UpperCAmelCase	    =			self.model_tester.get_config()

         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation(snake_case__				).to(snake_case__				)
         UpperCAmelCase	    =			model(**snake_case__				)
         self.assertTrue(outputs.loss is not None				)



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



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(snake_case__ ,		**snake_case__ ,		output_hidden_states=snake_case__				)



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



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase	    =			model_class(snake_case__				).to(snake_case__				)
                UpperCAmelCase	    =			model(**snake_case__ ,		output_attentions=snake_case__				)
                self.assertTrue(outputs.attentions is not None				)



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



         """simple docstring"""





         if not self.model_tester.is_training:
                return

         UpperCAmelCase	    =			self.all_model_classes[1]
         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()

         UpperCAmelCase	    =			model_class(snake_case__				)
         model.to(snake_case__				)
         model.train()

         UpperCAmelCase	    =			model(snake_case__ ,		mask_labels=snake_case__ ,		class_labels=snake_case__				).loss
         loss.backward()



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.all_model_classes[1]
         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         UpperCAmelCase	    =			True
         UpperCAmelCase	    =			True

         UpperCAmelCase	    =			model_class(snake_case__				).to(snake_case__				)
         model.train()

         UpperCAmelCase	    =			model(snake_case__ ,		mask_labels=snake_case__ ,		class_labels=snake_case__				)

         UpperCAmelCase	    =			outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         UpperCAmelCase	    =			outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         UpperCAmelCase	    =			outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         UpperCAmelCase	    =			outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=snake_case__				)

         self.assertIsNotNone(encoder_hidden_states.grad				)
         self.assertIsNotNone(pixel_decoder_hidden_states.grad				)
         self.assertIsNotNone(transformer_decoder_hidden_states.grad				)
         self.assertIsNotNone(attentions.grad				)





lowerCAmelCase_	:    Union[str, Any]            =      1E-4






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       return image





@require_vision
@slow
class   UpperCamelCase_ (       unittest.TestCase  ):



  @cached_property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         return "facebook/mask2former-swin-small-coco-instance"



  @cached_property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(snake_case__				)
         UpperCAmelCase	    =			self.default_image_processor
         UpperCAmelCase	    =			prepare_img()
         UpperCAmelCase	    =			image_processor(snake_case__ ,		return_tensors="""pt"""				).to(snake_case__				)
         UpperCAmelCase	    =			inputs["""pixel_values"""].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
         # check size
         self.assertEqual(snake_case__ ,		(1, 3, 3_84, 3_84)				)

         with torch.no_grad():
                UpperCAmelCase	    =			model(**snake_case__				)

         UpperCAmelCase	    =			torch.tensor(
             [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]				).to(snake_case__				)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)

         UpperCAmelCase	    =			torch.tensor(
             [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]				).to(snake_case__				)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)

         UpperCAmelCase	    =			torch.tensor(
             [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]				).to(snake_case__				)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(snake_case__				).eval()
         UpperCAmelCase	    =			self.default_image_processor
         UpperCAmelCase	    =			prepare_img()
         UpperCAmelCase	    =			image_processor(snake_case__ ,		return_tensors="""pt"""				).to(snake_case__				)
         UpperCAmelCase	    =			inputs["""pixel_values"""].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
         # check size
         self.assertEqual(snake_case__ ,		(1, 3, 3_84, 3_84)				)

         with torch.no_grad():
                UpperCAmelCase	    =			model(**snake_case__				)
         # masks_queries_logits
         UpperCAmelCase	    =			outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape ,		(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
         UpperCAmelCase	    =			[
             [-8.7_839, -9.0_056, -8.8_121],
             [-7.4_104, -7.0_313, -6.5_401],
             [-6.6_105, -6.3_427, -6.4_675],
         ]
         UpperCAmelCase	    =			torch.tensor(snake_case__				).to(snake_case__				)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)
         # class_queries_logits
         UpperCAmelCase	    =			outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape ,		(1, model.config.num_queries, model.config.num_labels + 1)				)
         UpperCAmelCase	    =			torch.tensor(
             [
                 [1.8_324, -8.0_835, -4.1_922],
                 [0.8_450, -9.0_050, -3.6_053],
                 [0.3_045, -7.7_293, -3.0_275],
             ]				).to(snake_case__				)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(snake_case__				).eval()
         UpperCAmelCase	    =			self.default_image_processor

         UpperCAmelCase	    =			image_processor(
             [np.zeros((3, 8_00, 13_33)				), np.zeros((3, 8_00, 13_33)				)] ,		segmentation_maps=[np.zeros((3_84, 3_84)				).astype(np.floataa				), np.zeros((3_84, 3_84)				).astype(np.floataa				)] ,		return_tensors="""pt""" ,		)

         UpperCAmelCase	    =			inputs["""pixel_values"""].to(snake_case__				)
         UpperCAmelCase	    =			[el.to(snake_case__				) for el in inputs["""mask_labels"""]]
         UpperCAmelCase	    =			[el.to(snake_case__				) for el in inputs["""class_labels"""]]

         with torch.no_grad():
                UpperCAmelCase	    =			model(**snake_case__				)

         self.assertTrue(outputs.loss is not None				)








"""simple docstring"""







import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

lowerCAmelCase_	:    List[str]            =      {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=False	,      lowerCAmelCase=True		):







       '''simple docstring'''
       if model_type not in MODEL_CLASSES:
              raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       UpperCAmelCase	    =			config_class.from_json_file(lowerCAmelCase		)
       UpperCAmelCase	    =			True
       UpperCAmelCase	    =			True
       print(F'''Building TensorFlow model from configuration: {config}'''		)
       UpperCAmelCase	    =			model_class(lowerCAmelCase		)

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              UpperCAmelCase	    =			cached_file(
                  lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       # Load PyTorch checkpoint in tf2 model:
       UpperCAmelCase	    =			load_pytorch_checkpoint_in_tfa_model(lowerCAmelCase	,      lowerCAmelCase		)

       if compare_with_pt_model:
              UpperCAmelCase	    =			tf_model(tf_model.dummy_inputs	,      training=lowerCAmelCase		)  # build the network

              UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)
              UpperCAmelCase	    =			pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowerCAmelCase	,      config=lowerCAmelCase	,      state_dict=lowerCAmelCase		)

              with torch.no_grad():
                     UpperCAmelCase	    =			pt_model(**pt_model.dummy_inputs		)

              UpperCAmelCase	    =			pto[0].numpy()
              UpperCAmelCase	    =			tfo[0].numpy()
              UpperCAmelCase	    =			np.amax(np.abs(np_pt - np_tf		)		)
              print(F'''Max absolute difference between models outputs {diff}'''		)
              assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

       # Save pytorch-model
       print(F'''Save TensorFlow model to {tf_dump_path}'''		)
       tf_model.save_weights(lowerCAmelCase	,      save_format="""h5"""		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      ):







       '''simple docstring'''
       if args_model_type is None:
              UpperCAmelCase	    =			list(MODEL_CLASSES.keys()		)
       else:
              UpperCAmelCase	    =			[args_model_type]

       for j, model_type in enumerate(lowerCAmelCase	,      start=1		):
              print("""=""" * 100		)
              print(F''' Converting model type {j}/{len(lowerCAmelCase		)}: {model_type}'''		)
              print("""=""" * 100		)
              if model_type not in MODEL_CLASSES:
                     raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			list(aws_model_maps.keys()		)
              if config_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowerCAmelCase	,      lowerCAmelCase		)	,      start=1		):
                     print("""-""" * 100		)
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''		)
                                   continue
                            UpperCAmelCase	    =			model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                            continue
                     print(
                         F'''    Converting checkpoint {i}/{len(lowerCAmelCase		)}: {model_shortcut_name} - model_type {model_type}'''		)
                     print("""-""" * 100		)

                     if config_shortcut_name in aws_config_map:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			model_shortcut_name

                     if os.path.isfile(lowerCAmelCase		):
                            UpperCAmelCase	    =			"""converted_model"""

                     convert_pt_checkpoint_to_tf(
                         model_type=lowerCAmelCase	,      pytorch_checkpoint_path=lowerCAmelCase	,      config_file=lowerCAmelCase	,      tf_dump_path=os.path.join(lowerCAmelCase	,      model_shortcut_name + """-tf_model.h5"""		)	,      compare_with_pt_model=lowerCAmelCase	,      )
                     if remove_cached_files:
                            os.remove(lowerCAmelCase		)
                            os.remove(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    str            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
 )
 parser.add_argument(
     '''--model_type''',
     default=None,
     type=str,
     help=(
         F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
         '''convert all the models from AWS.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_checkpoint_path''',
     default=None,
     type=str,
     help=(
         '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
         '''If not given, will download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     help=(
         '''The config json file corresponding to the pre-trained model. \n'''
         '''This specifies the model architecture. If not given and '''
         '''--pytorch_checkpoint_path is not given or is a shortcut name '''
         '''use the configuration associated to the shortcut name on the AWS'''
     ),
 )
 parser.add_argument(
     '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
 )
 parser.add_argument(
     '''--use_cached_models''',
     action='''store_true''',
     help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
 )
 parser.add_argument(
     '''--remove_cached_files''',
     action='''store_true''',
     help='''Remove pytorch models after conversion (save memory when converting in batches).''',
 )
 parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
 lowerCAmelCase_	:    Optional[int]            =      parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )
