



import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__magic_name__            =  {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		Dict    	=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _A						:		str    	=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _A						:		Optional[int]    	=     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _A						:		Any    	=     {
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }
      @require_torch
      def A_			(			self ):
          snake_case__						=				pipeline(
              task="text-classification"		,	model="hf-internal-testing/tiny-random-distilbert"		,	framework="pt" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )

          snake_case__						=				text_classifier("This is great !"		,	top_k=2 )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}] )

          snake_case__						=				text_classifier(["This is great !", "This is bad"]		,	top_k=2 )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
              ]		,	)

          snake_case__						=				text_classifier("This is great !"		,	top_k=1 )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )

          # Legacy behavior
          snake_case__						=				text_classifier("This is great !"		,	return_all_scores=lowerCamelCase )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )

          snake_case__						=				text_classifier("This is great !"		,	return_all_scores=lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]] )

          snake_case__						=				text_classifier(["This is great !", "Something else"]		,	return_all_scores=lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
              ]		,	)

          snake_case__						=				text_classifier(["This is great !", "Something else"]		,	return_all_scores=lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  {"label": "LABEL_0", "score": 0.5_0_4},
                  {"label": "LABEL_0", "score": 0.5_0_4},
              ]		,	)
      @require_torch
      def A_			(			self ):
          import torch

          snake_case__						=				pipeline(
              task="text-classification"		,	model="hf-internal-testing/tiny-random-distilbert"		,	framework="pt"		,	device=torch.device("cpu" )		,	)

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )
      @require_tf
      def A_			(			self ):
          snake_case__						=				pipeline(
              task="text-classification"		,	model="hf-internal-testing/tiny-random-distilbert"		,	framework="tf" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )
      @slow
      @require_torch
      def A_			(			self ):
          snake_case__						=				pipeline("text-classification" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("This is bad !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "NEGATIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("Birds are a type of animal" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 0.9_8_8}] )
      @slow
      @require_tf
      def A_			(			self ):
          snake_case__						=				pipeline("text-classification"		,	framework="tf" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("This is bad !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "NEGATIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("Birds are a type of animal" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 0.9_8_8}] )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				TextClassificationPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )
          return text_classifier, ["HuggingFace is in", "This is another test"]



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				text_classifier.model
          # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
          snake_case__						=				"HuggingFace is in"
          snake_case__						=				text_classifier(lowerCamelCase )

          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}] )
          self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )

          snake_case__						=				["HuggingFace is in ", "Paris is in France"]
          snake_case__						=				text_classifier(lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}, {"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}]		,	)
          self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
          self.assertTrue(outputs[1]["label"] in model.config.idalabel.values() )

          # Forcing to get all results with `top_k=None`
          # This is NOT the legacy format
          snake_case__						=				text_classifier(lowerCamelCase		,	top_k=lowerCamelCase )
          snake_case__						=				len(model.config.idalabel.values() )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}] * N, [{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}] * N]		,	)

          snake_case__						=				{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
          snake_case__						=				text_classifier(lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}		,	)
          self.assertTrue(outputs["label"] in model.config.idalabel.values() )

          # This might be used a text pair, but tokenizer + pipe interaction
          # makes it hard to understand that it's not using the pair properly
          # https://github.com/huggingface/transformers/issues/17305
          # We disabled this usage instead as it was outputting wrong outputs.
          snake_case__						=				[["HuggingFace is in ", "Paris is in France"]]
          with self.assertRaises(lowerCamelCase ):
              text_classifier(lowerCamelCase )

          # This used to be valid for doing text pairs
          # We're keeping it working because of backward compatibility
          snake_case__						=				text_classifier([[["HuggingFace is in ", "Paris is in France"]]] )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}]		,	)
          self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				SwinConfig()
    snake_case__						=				swin_name.split("_"							)

    snake_case__						=				name_split[1]
    snake_case__						=				int(name_split[4]							)
    snake_case__						=				int(name_split[3][-1]							)

    if model_size == "tiny":
        snake_case__						=				96
        snake_case__						=				(2, 2, 6, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "small":
        snake_case__						=				96
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "base":
        snake_case__						=				128
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(4, 8, 16, 32)
    else:
        snake_case__						=				192
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case__						=				21_841
    else:
        snake_case__						=				1_000
        snake_case__						=				"huggingface/label-files"
        snake_case__						=				"imagenet-1k-id2label.json"
        snake_case__						=				json.load(open(hf_hub_download(__lowerCAmelCase    ,		__lowerCAmelCase    ,		repo_type="dataset"							)    ,		"r"							)							)
        snake_case__						=				{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
        snake_case__						=				idalabel
        snake_case__						=				{v: k for k, v in idalabel.items()}

    snake_case__						=				img_size
    snake_case__						=				num_classes
    snake_case__						=				embed_dim
    snake_case__						=				depths
    snake_case__						=				num_heads
    snake_case__						=				window_size

    return config





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if "patch_embed.proj" in name:
        snake_case__						=				name.replace("patch_embed.proj"    ,		"embeddings.patch_embeddings.projection"							)
    if "patch_embed.norm" in name:
        snake_case__						=				name.replace("patch_embed.norm"    ,		"embeddings.norm"							)
    if "layers" in name:
        snake_case__						=				"encoder." + name
    if "attn.proj" in name:
        snake_case__						=				name.replace("attn.proj"    ,		"attention.output.dense"							)
    if "attn" in name:
        snake_case__						=				name.replace("attn"    ,		"attention.self"							)
    if "norm1" in name:
        snake_case__						=				name.replace("norm1"    ,		"layernorm_before"							)
    if "norm2" in name:
        snake_case__						=				name.replace("norm2"    ,		"layernorm_after"							)
    if "mlp.fc1" in name:
        snake_case__						=				name.replace("mlp.fc1"    ,		"intermediate.dense"							)
    if "mlp.fc2" in name:
        snake_case__						=				name.replace("mlp.fc2"    ,		"output.dense"							)

    if name == "norm.weight":
        snake_case__						=				"layernorm.weight"
    if name == "norm.bias":
        snake_case__						=				"layernorm.bias"

    if "head" in name:
        snake_case__						=				name.replace("head"    ,		"classifier"							)
    else:
        snake_case__						=				"swin." + name

    return name





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    for key in orig_state_dict.copy().keys():
        snake_case__						=				orig_state_dict.pop(__lowerCAmelCase							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case__						=				key.split("."							)
            snake_case__						=				int(key_split[1]							)
            snake_case__						=				int(key_split[3]							)
            snake_case__						=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case__						=				val[:dim, :]
                snake_case__						=				val[
                    dim : dim * 2, :
                ]
                snake_case__						=				val[-dim:, :]
            else:
                snake_case__						=				val[
                    :dim
                ]
                snake_case__						=				val[
                    dim : dim * 2
                ]
                snake_case__						=				val[
                    -dim:
                ]
        else:
            snake_case__						=				val

    return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				timm.create_model(__lowerCAmelCase    ,		pretrained=__lowerCAmelCase							)
    timm_model.eval()

    snake_case__						=				get_swin_config(__lowerCAmelCase							)
    snake_case__						=				SwinForImageClassification(__lowerCAmelCase							)
    model.eval()

    snake_case__						=				convert_state_dict(timm_model.state_dict()    ,		__lowerCAmelCase							)
    model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case__						=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,		"-"							)							)							)
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							)
    snake_case__						=				image_processor(images=__lowerCAmelCase    ,		return_tensors="pt"							)

    snake_case__						=				timm_model(inputs["pixel_values"]							)
    snake_case__						=				model(**__lowerCAmelCase							).logits

    assert torch.allclose(__lowerCAmelCase    ,		__lowerCAmelCase    ,		atol=1E-3							)

    print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(__lowerCAmelCase							)

    print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
    image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--swin_name''',
         default='''swin_tiny_patch4_window7_224''',
         type=str,
         help='''Name of the Swin timm 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.'''
     )

     __magic_name__            =  parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



