






import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           XLMForMultipleChoice,
           XLMForQuestionAnswering,
           XLMForQuestionAnsweringSimple,
           XLMForSequenceClassification,
           XLMForTokenClassification,
           XLMModel,
           XLMWithLMHeadModel,
       )
       from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class        a       :



      """simple docstring"""


      def __init__( self   :     Tuple						, __lowercase   :     Union[str, Any]						, __lowercase   :     Optional[Any]=13						, __lowercase   :     Union[str, Any]=7						, __lowercase   :     Union[str, Any]=True						, __lowercase   :     Dict=True						, __lowercase   :     Any=True						, __lowercase   :     Any=True						, __lowercase   :     List[Any]=True						, __lowercase   :     List[str]=False						, __lowercase   :     Optional[int]=False						, __lowercase   :     List[str]=False						, __lowercase   :     Dict=2						, __lowercase   :     List[Any]=99						, __lowercase   :     Optional[Any]=0						, __lowercase   :     Dict=32						, __lowercase   :     int=5						, __lowercase   :     Optional[Any]=4						, __lowercase   :     Dict=0.1						, __lowercase   :     int=0.1						, __lowercase   :     Optional[int]=512						, __lowercase   :     Tuple=2						, __lowercase   :     str=0.02						, __lowercase   :     Optional[Any]=2						, __lowercase   :     Dict=4						, __lowercase   :     int="last"						, __lowercase   :     List[Any]=True						, __lowercase   :     Optional[Any]=None						, __lowercase   :     Optional[int]=0						, )				-> Any:
        __UpperCAmelCase				:      List[Any]	 =    parent
        __UpperCAmelCase				:      Dict	 =    batch_size
        __UpperCAmelCase				:      Union[str, Any]	 =    seq_length
        __UpperCAmelCase				:      List[Any]	 =    is_training
        __UpperCAmelCase				:      Tuple	 =    use_input_lengths
        __UpperCAmelCase				:      int	 =    use_token_type_ids
        __UpperCAmelCase				:      Any	 =    use_labels
        __UpperCAmelCase				:      Dict	 =    gelu_activation
        __UpperCAmelCase				:      List[str]	 =    sinusoidal_embeddings
        __UpperCAmelCase				:      int	 =    causal
        __UpperCAmelCase				:      List[Any]	 =    asm
        __UpperCAmelCase				:      Tuple	 =    n_langs
        __UpperCAmelCase				:      Optional[Any]	 =    vocab_size
        __UpperCAmelCase				:      Any	 =    n_special
        __UpperCAmelCase				:      Optional[int]	 =    hidden_size
        __UpperCAmelCase				:      List[Any]	 =    num_hidden_layers
        __UpperCAmelCase				:      List[Any]	 =    num_attention_heads
        __UpperCAmelCase				:      Union[str, Any]	 =    hidden_dropout_prob
        __UpperCAmelCase				:      int	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      int	 =    max_position_embeddings
        __UpperCAmelCase				:      List[str]	 =    type_sequence_label_size
        __UpperCAmelCase				:      List[str]	 =    initializer_range
        __UpperCAmelCase				:      Any	 =    num_labels
        __UpperCAmelCase				:      List[Any]	 =    num_choices
        __UpperCAmelCase				:      int	 =    summary_type
        __UpperCAmelCase				:      str	 =    use_proj
        __UpperCAmelCase				:      List[str]	 =    scope
        __UpperCAmelCase				:      List[Any]	 =    bos_token_id
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Optional[int]:
        __UpperCAmelCase				:      Tuple	 =    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size   )
        __UpperCAmelCase				:      List[Any]	 =    random_attention_mask([self.batch_size, self.seq_length]   )

        __UpperCAmelCase				:      Optional[int]	 =    None
        if self.use_input_lengths:
          __UpperCAmelCase				:      List[str]	 =    (
              ids_tensor([self.batch_size]						, vocab_size=2   ) + self.seq_length - 2
          )  # small variation of seq_length

        __UpperCAmelCase				:      List[str]	 =    None
        if self.use_token_type_ids:
          __UpperCAmelCase				:      Any	 =    ids_tensor([self.batch_size, self.seq_length]						, self.n_langs   )

        __UpperCAmelCase				:      Any	 =    None
        __UpperCAmelCase				:      int	 =    None
        __UpperCAmelCase				:      Tuple	 =    None
        if self.use_labels:
          __UpperCAmelCase				:      Any	 =    ids_tensor([self.batch_size]						, self.type_sequence_label_size   )
          __UpperCAmelCase				:      Optional[Any]	 =    ids_tensor([self.batch_size, self.seq_length]						, self.num_labels   )
          __UpperCAmelCase				:      int	 =    ids_tensor([self.batch_size]						, 2   ).float()
          __UpperCAmelCase				:      int	 =    ids_tensor([self.batch_size]						, self.num_choices   )

        __UpperCAmelCase				:      List[Any]	 =    self.get_config()

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )
      def       UpperCAmelCase ( self   :     str   )				-> int:
        return XLMConfig(
            vocab_size=self.vocab_size						, n_special=self.n_special						, emb_dim=self.hidden_size						, n_layers=self.num_hidden_layers						, n_heads=self.num_attention_heads						, dropout=self.hidden_dropout_prob						, attention_dropout=self.attention_probs_dropout_prob						, gelu_activation=self.gelu_activation						, sinusoidal_embeddings=self.sinusoidal_embeddings						, asm=self.asm						, causal=self.causal						, n_langs=self.n_langs						, max_position_embeddings=self.max_position_embeddings						, initializer_range=self.initializer_range						, summary_type=self.summary_type						, use_proj=self.use_proj						, num_labels=self.num_labels						, bos_token_id=self.bos_token_id						, )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     List[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     List[Any]						, __lowercase   :     int						, __lowercase   :     List[str]						, __lowercase   :     List[str]						, __lowercase   :     Tuple						, __lowercase   :     Tuple						, __lowercase   :     str						, )				-> str:
        __UpperCAmelCase				:      int	 =    XLMModel(config=a__   )
        model.to(a__   )
        model.eval()
        __UpperCAmelCase				:      Any	 =    model(a__						, lengths=a__						, langs=a__   )
        __UpperCAmelCase				:      int	 =    model(a__						, langs=a__   )
        __UpperCAmelCase				:      str	 =    model(a__   )
        self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)   )
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     Dict						, __lowercase   :     Any						, __lowercase   :     Optional[int]						, __lowercase   :     Any						, __lowercase   :     int						, __lowercase   :     Any						, __lowercase   :     List[str]						, __lowercase   :     Optional[int]						, __lowercase   :     Optional[int]						, )				-> str:
        __UpperCAmelCase				:      str	 =    XLMWithLMHeadModel(a__   )
        model.to(a__   )
        model.eval()

        __UpperCAmelCase				:      int	 =    model(a__						, token_type_ids=a__						, labels=a__   )
        self.parent.assertEqual(result.loss.shape						, ()   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)   )
      def       UpperCAmelCase ( self   :     int						, __lowercase   :     Any						, __lowercase   :     int						, __lowercase   :     List[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Optional[int]						, __lowercase   :     Dict						, __lowercase   :     int						, __lowercase   :     Any						, __lowercase   :     Optional[int]						, )				-> Any:
        __UpperCAmelCase				:      Optional[Any]	 =    XLMForQuestionAnsweringSimple(a__   )
        model.to(a__   )
        model.eval()

        __UpperCAmelCase				:      Dict	 =    model(a__   )

        __UpperCAmelCase				:      Tuple	 =    model(a__						, start_positions=a__						, end_positions=a__   )
        __UpperCAmelCase				:      Optional[int]	 =    outputs
        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       UpperCAmelCase ( self   :     str						, __lowercase   :     Optional[int]						, __lowercase   :     Dict						, __lowercase   :     Tuple						, __lowercase   :     int						, __lowercase   :     int						, __lowercase   :     Union[str, Any]						, __lowercase   :     List[str]						, __lowercase   :     int						, __lowercase   :     Optional[int]						, )				-> Optional[Any]:
        __UpperCAmelCase				:      List[str]	 =    XLMForQuestionAnswering(a__   )
        model.to(a__   )
        model.eval()

        __UpperCAmelCase				:      Optional[Any]	 =    model(a__   )

        __UpperCAmelCase				:      Optional[Any]	 =    model(
            a__						, start_positions=a__						, end_positions=a__						, cls_index=a__						, is_impossible=a__						, p_mask=a__						, )

        __UpperCAmelCase				:      List[Any]	 =    model(
            a__						, start_positions=a__						, end_positions=a__						, cls_index=a__						, is_impossible=a__						, )

        ((__UpperCAmelCase      ) ,					      )				:      Dict	 =    result_with_labels.to_tuple()

        __UpperCAmelCase				:      Optional[Any]	 =    model(a__						, start_positions=a__						, end_positions=a__   )

        ((__UpperCAmelCase      ) ,					      )				:      List[Any]	 =    result_with_labels.to_tuple()

        self.parent.assertEqual(result_with_labels.loss.shape						, ()   )
        self.parent.assertEqual(result.start_top_log_probs.shape						, (self.batch_size, model.config.start_n_top)   )
        self.parent.assertEqual(result.start_top_index.shape						, (self.batch_size, model.config.start_n_top)   )
        self.parent.assertEqual(
            result.end_top_log_probs.shape						, (self.batch_size, model.config.start_n_top * model.config.end_n_top)   )
        self.parent.assertEqual(
            result.end_top_index.shape						, (self.batch_size, model.config.start_n_top * model.config.end_n_top)   )
        self.parent.assertEqual(result.cls_logits.shape						, (self.batch_size,)   )
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     List[str]						, __lowercase   :     Any						, __lowercase   :     Any						, __lowercase   :     Optional[int]						, __lowercase   :     Optional[int]						, __lowercase   :     Optional[int]						, __lowercase   :     List[Any]						, __lowercase   :     List[Any]						, __lowercase   :     Tuple						, )				-> Optional[Any]:
        __UpperCAmelCase				:      str	 =    XLMForSequenceClassification(a__   )
        model.to(a__   )
        model.eval()

        __UpperCAmelCase				:      Optional[int]	 =    model(a__   )
        __UpperCAmelCase				:      Optional[Any]	 =    model(a__						, labels=a__   )
        self.parent.assertEqual(result.loss.shape						, ()   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.type_sequence_label_size)   )
      def       UpperCAmelCase ( self   :     Dict						, __lowercase   :     Optional[Any]						, __lowercase   :     str						, __lowercase   :     List[Any]						, __lowercase   :     List[Any]						, __lowercase   :     Tuple						, __lowercase   :     int						, __lowercase   :     List[str]						, __lowercase   :     Any						, __lowercase   :     Dict						, )				-> List[str]:
        __UpperCAmelCase				:      List[Any]	 =    self.num_labels
        __UpperCAmelCase				:      int	 =    XLMForTokenClassification(a__   )
        model.to(a__   )
        model.eval()

        __UpperCAmelCase				:      List[Any]	 =    model(a__						, attention_mask=a__						, labels=a__   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.num_labels)   )
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     int						, __lowercase   :     Dict						, __lowercase   :     List[Any]						, __lowercase   :     Tuple						, __lowercase   :     Any						, __lowercase   :     List[Any]						, __lowercase   :     int						, __lowercase   :     List[Any]						, __lowercase   :     Tuple						, )				-> Dict:
        __UpperCAmelCase				:      List[str]	 =    self.num_choices
        __UpperCAmelCase				:      Tuple	 =    XLMForMultipleChoice(config=a__   )
        model.to(a__   )
        model.eval()
        __UpperCAmelCase				:      Dict	 =    input_ids.unsqueeze(1   ).expand(-1						, self.num_choices						, -1   ).contiguous()
        __UpperCAmelCase				:      Optional[int]	 =    token_type_ids.unsqueeze(1   ).expand(-1						, self.num_choices						, -1   ).contiguous()
        __UpperCAmelCase				:      Tuple	 =    input_mask.unsqueeze(1   ).expand(-1						, self.num_choices						, -1   ).contiguous()
        __UpperCAmelCase				:      Union[str, Any]	 =    model(
            a__						, attention_mask=a__						, token_type_ids=a__						, labels=a__						, )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_choices)   )




      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Optional[int]:
        __UpperCAmelCase				:      Optional[int]	 =    self.prepare_config_and_inputs()
        (
            (
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					(
            __UpperCAmelCase      
        ) ,					      
        )				:      List[Any]	 =    config_and_inputs
        __UpperCAmelCase				:      Tuple	 =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
        return config, inputs_dict


@require_torch
class        a       (					__snake_case				,			__snake_case				,			__snake_case				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Optional[Any]							=      (
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
              XLMForTokenClassification,
              XLMForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      a    :					Union[str, Any]							=      (
          (XLMWithLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      a    :					Tuple							=      (
          {
              'feature-extraction': XLMModel,
              'fill-mask': XLMWithLMHeadModel,
              'question-answering': XLMForQuestionAnsweringSimple,
              'text-classification': XLMForSequenceClassification,
              'text-generation': XLMWithLMHeadModel,
              'token-classification': XLMForTokenClassification,
              'zero-shot': XLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     int						, __lowercase   :     Optional[int]						, __lowercase   :     Optional[Any]						, __lowercase   :     int						, __lowercase   :     Optional[Any]   )				-> Union[str, Any]:
        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("""Fast"""   )
        ):
          # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
          # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
          # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
          return True

        return False
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     str						, __lowercase   :     List[str]						, __lowercase   :     Dict=False   )				-> Optional[Any]:
        __UpperCAmelCase				:      Dict	 =    super()._prepare_for_class(a__						, a__						, return_labels=a__   )

        if return_labels:
          if model_class.__name__ == "XLMForQuestionAnswering":
            __UpperCAmelCase				:      Dict	 =    torch.zeros(
                self.model_tester.batch_size						, dtype=torch.long						, device=a__   )
            __UpperCAmelCase				:      Dict	 =    torch.zeros(
                self.model_tester.batch_size						, dtype=torch.long						, device=a__   )

        return inputs_dict
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Union[str, Any]	 =    XLMModelTester(self   )
        __UpperCAmelCase				:      Optional[int]	 =    ConfigTester(self						, config_class=a__						, emb_dim=37   )
      def       UpperCAmelCase ( self   :     List[str]   )				-> str:
        self.config_tester.run_common_tests()
      def       UpperCAmelCase ( self   :     List[str]   )				-> Any:
        __UpperCAmelCase				:      List[Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_model(*a__   )
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Optional[int]:
        __UpperCAmelCase				:      List[Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_lm_head(*a__   )
      def       UpperCAmelCase ( self   :     int   )				-> Dict:
        __UpperCAmelCase				:      List[str]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_simple_qa(*a__   )
      def       UpperCAmelCase ( self   :     Any   )				-> Optional[Any]:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_qa(*a__   )
      def       UpperCAmelCase ( self   :     int   )				-> Optional[Any]:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_sequence_classif(*a__   )
      def       UpperCAmelCase ( self   :     str   )				-> Optional[Any]:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_token_classif(*a__   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Tuple:
        __UpperCAmelCase				:      Any	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_multiple_choice(*a__   )
      def       UpperCAmelCase ( self   :     str						, __lowercase   :     Any						, __lowercase   :     Optional[Any]						, __lowercase   :     str						, __lowercase   :     List[Any]						, __lowercase   :     Optional[int]						, __lowercase   :     Tuple=False						, __lowercase   :     Dict=1   )				-> List[str]:
        self.assertIsInstance(a__						, a__   )
        self.assertListEqual(
            [isinstance(a__						, a__   ) for iter_attentions in attentions]						, [True] * len(a__   )   )
        self.assertEqual(len(a__   )						, (max_length - min_length) * num_beam_groups   )

        for idx, iter_attentions in enumerate(a__   ):
          # adds PAD dummy token
          __UpperCAmelCase				:      List[Any]	 =    min_length + idx + 1
          __UpperCAmelCase				:      Tuple	 =    min_length + idx + 1

          __UpperCAmelCase				:      Tuple	 =    (
              batch_size * num_beam_groups,
              config.num_attention_heads,
              tgt_len,
              src_len,
          )
          # check attn size
          self.assertListEqual(
              [layer_attention.shape for layer_attention in iter_attentions]						, [expected_shape] * len(a__   )   )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Any						, __lowercase   :     Union[str, Any]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Dict						, __lowercase   :     int=False						, __lowercase   :     Dict=1   )				-> List[Any]:
        self.assertIsInstance(a__						, a__   )
        self.assertListEqual(
            [isinstance(a__						, a__   ) for iter_hidden_states in hidden_states]						, [True] * len(a__   )						, )
        self.assertEqual(len(a__   )						, (max_length - min_length) * num_beam_groups   )

        for idx, iter_hidden_states in enumerate(a__   ):
          # adds PAD dummy token
          __UpperCAmelCase				:      str	 =    min_length + idx + 1
          __UpperCAmelCase				:      Tuple	 =    (batch_size * num_beam_groups, seq_len, config.hidden_size)
          # check hidden size
          self.assertListEqual(
              [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]						, [expected_shape] * len(a__   )						, )
        pass




      @slow
      def       UpperCAmelCase ( self   :     int   )				-> Any:
        for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase				:      Dict	 =    XLMModel.from_pretrained(a__   )
          self.assertIsNotNone(a__   )


@require_torch
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      @slow
      def       UpperCAmelCase ( self   :     List[str]   )				-> List[Any]:
        __UpperCAmelCase				:      Optional[int]	 =    XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""   )
        model.to(a__   )
        __UpperCAmelCase				:      Any	 =    torch.tensor([[14, 447]]						, dtype=torch.long						, device=a__   )  # the president
        __UpperCAmelCase				:      List[Any]	 =    [
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
            14,
            447,
        ]  # the president the president the president the president the president the president the president the president the president the president
        # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
        __UpperCAmelCase				:      Optional[Any]	 =    model.generate(a__						, do_sample=a__   )
        self.assertListEqual(output_ids[0].cpu().numpy().tolist()						, a__   )






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					3_84
	if "tiny" in model_name:
		A             =					[3, 3, 9, 3]
		A             =					[96, 1_92, 3_84, 7_68]
	if "small" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[96, 1_92, 3_84, 7_68]
	if "base" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[1_28, 2_56, 5_12, 10_24]
		A             =					5_12
	if "large" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[1_92, 3_84, 7_68, 15_36]
		A             =					7_68
	if "xlarge" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[2_56, 5_12, 10_24, 20_48]
		A             =					10_24

	# set label information
	A             =					1_50
	A             =					"""huggingface/label-files"""
	A             =					"""ade20k-id2label.json"""
	A             =					json.load(open(hf_hub_download(UpperCamelCase__				,       UpperCamelCase__				,       repo_type="""dataset"""    )				,       """r"""    )    )
	A             =					{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
	A             =					{v: k for k, v in idalabel.items()}

	A             =					ConvNextConfig(
	    depths=UpperCamelCase__				,       hidden_sizes=UpperCamelCase__				,       out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    )
	A             =					UperNetConfig(
	    backbone_config=UpperCamelCase__				,       auxiliary_in_channels=UpperCamelCase__				,       num_labels=UpperCamelCase__				,       idalabel=UpperCamelCase__				,       labelaid=UpperCamelCase__				,       )

	return config





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> Dict:





	"""simple docstring"""







	A             =					[]

	# fmt: off
	# stem
	rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")    )
	rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")    )
	rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")    )
	rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")    )
	# stages
	for i in range(len(config.backbone_config.depths    )    ):
		for j in range(config.backbone_config.depths[i]    ):
			rename_keys.append((f'backbone.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')    )
			rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')    )
		if i > 0:
			rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight')    )
			rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias')    )
			rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight')    )
			rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias')    )

		rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight')    )
		rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias')    )

	# decode head
	rename_keys.extend(
	    [
	        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
	        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
	        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
	        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
	    ]    )
	# fmt: on

	return rename_keys





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  List[str]				,       UpperCamelCase__:  int    )			-> Optional[Any]:





	"""simple docstring"""







	A             =					dct.pop(UpperCamelCase__    )
	A             =					val





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  int    )			-> List[Any]:





	"""simple docstring"""







	A             =					{
	    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
	    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
	    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
	    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
	    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
	}
	A             =					model_name_to_url[model_name]
	A             =					torch.hub.load_state_dict_from_url(UpperCamelCase__				,       map_location="""cpu"""    )["""state_dict"""]

	A             =					get_upernet_config(UpperCamelCase__    )
	A             =					UperNetForSemanticSegmentation(UpperCamelCase__    )
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		A             =					state_dict.pop(UpperCamelCase__    )
		if "bn" in key:
			A             =					key.replace("""bn"""				,       """batch_norm"""    )
		A             =					val

	# rename keys
	A             =					create_rename_keys(UpperCamelCase__    )
	for src, dest in rename_keys:
		rename_key(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )

	model.load_state_dict(UpperCamelCase__    )

	# verify on image
	A             =					"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
	A             =					Image.open(requests.get(UpperCamelCase__				,       stream=UpperCamelCase__    ).raw    ).convert("""RGB"""    )

	A             =					SegformerImageProcessor()
	A             =					processor(UpperCamelCase__				,       return_tensors="""pt"""    ).pixel_values

	with torch.no_grad():
		A             =					model(UpperCamelCase__    )

	if model_name == "upernet-convnext-tiny":
		A             =					torch.tensor(
		    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]    )
	elif model_name == "upernet-convnext-small":
		A             =					torch.tensor(
		    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]    )
	elif model_name == "upernet-convnext-base":
		A             =					torch.tensor(
		    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]    )
	elif model_name == "upernet-convnext-large":
		A             =					torch.tensor(
		    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]    )
	elif model_name == "upernet-convnext-xlarge":
		A             =					torch.tensor(
		    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]    )
	print("""Logits:"""				,       outputs.logits[0, 0, :3, :3]    )
	assert torch.allclose(outputs.logits[0, 0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4    )
	print("""Looks ok!"""    )

	if pytorch_dump_folder_path is not None:
		print(f'Saving model {model_name} to {pytorch_dump_folder_path}'    )
		model.save_pretrained(UpperCamelCase__    )
		print(f'Saving processor to {pytorch_dump_folder_path}'    )
		processor.save_pretrained(UpperCamelCase__    )

	if push_to_hub:
		print(f'Pushing model and processor for {model_name} to hub'    )
		model.push_to_hub(f'openmmlab/{model_name}'    )
		processor.push_to_hub(f'openmmlab/{model_name}'    )


if __name__ == "__main__":
				_lowercase							:   List[str]						=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="upernet-convnext-tiny",
				    type=str,
				    choices=[f'''upernet-convnext-{size}''' for size in ["tiny", "small", "base", "large", "xlarge"]],
				    help="Name of the ConvNext UperNet model you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
				)
				parser.add_argument(
				    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
				)

				_lowercase							:   str						=						parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



