
import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:Any							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				__A   			= SwinConfig.from_pretrained(
				    "microsoft/swin-tiny-patch4-window7-224"		,   out_features=["stage1", "stage2", "stage3", "stage4"]						)
				__A   			= MaskFormerConfig(backbone_config=a_						)

				__A   			= "huggingface/label-files"
				if "ade20k-full" in model_name:
								# this should be ok
								__A   			= 8_4_7
								__A   			= "maskformer-ade20k-full-id2label.json"
				elif "ade" in model_name:
								# this should be ok
								__A   			= 1_5_0
								__A   			= "ade20k-id2label.json"
				elif "coco-stuff" in model_name:
								# this should be ok
								__A   			= 1_7_1
								__A   			= "maskformer-coco-stuff-id2label.json"
				elif "coco" in model_name:
								# TODO
								__A   			= 1_3_3
								__A   			= "coco-panoptic-id2label.json"
				elif "cityscapes" in model_name:
								# this should be ok
								__A   			= 1_9
								__A   			= "cityscapes-id2label.json"
				elif "vistas" in model_name:
								# this should be ok
								__A   			= 6_5
								__A   			= "mapillary-vistas-id2label.json"

				__A   			= json.load(open(hf_hub_download(a_		,   a_		,   repo_type="dataset"						)		,   "r"						)						)
				__A   			= {int(a_						): v for k, v in idalabel.items()}

				return config


def 						UpperCAmelCase			(	a_						)		->	Any:







				"""simple docstring"""



				__A   			= []
				# stem
				# fmt: off
				rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")						)
				rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")						)
				rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")						)
				rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')						)
												rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')						)

								if i < 3:
												rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')						)
												rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')						)
												rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')						)
								rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')						)
								rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')						)

				# FPN
				rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")						)
				rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")						)
				rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")						)
				for source_index, target_index in zip(range(3		,   0		,   -1						)		,   range(0		,   3						)						):
								rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')						)
								rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')						)
								rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')						)
								rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')						)
								rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')						)
								rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')						)
				rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")						)
				rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")						)

				# Transformer decoder
				for idx in range(config.decoder_config.decoder_layers						):
								# self-attention out projection
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')						)
								# cross-attention out projection
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')						)
								# MLP 1
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')						)
								# MLP 2
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')						)
								# layernorm 1 (self-attention layernorm)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')						)
								# layernorm 2 (cross-attention layernorm)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')						)
								# layernorm 3 (final layernorm)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')						)

				rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")						)
				rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")						)

				# heads on top
				rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")						)

				rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")						)
				rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")						)

				rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")						)
				rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")						)

				for i in range(3						):
								rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')						)
								rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')						)
				# fmt: on

				return rename_keys


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= dct.pop(a_						)
				__A   			= val


def 						UpperCAmelCase			(	a_		,   a_						)		->	Dict:







				"""simple docstring"""



				__A   			= [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
				for i in range(len(backbone_config.depths						)						):
								__A   			= num_features[i]
								for j in range(backbone_config.depths[i]						):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												__A   			= state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
												__A   			= state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''						)
												# next, add query, keys and values (in that order) to the state dict
												__A   			= in_proj_weight[:dim, :]
												__A   			= in_proj_bias[: dim]
												__A   			= in_proj_weight[
												    dim : dim * 2, :
												]
												__A   			= in_proj_bias[
												    dim : dim * 2
												]
												__A   			= in_proj_weight[
												    -dim :, :
												]
												__A   			= in_proj_bias[-dim :]
												# fmt: on


def 						UpperCAmelCase			(	a_		,   a_						)		->	str:







				"""simple docstring"""



				__A   			= config.decoder_config.hidden_size
				for idx in range(config.decoder_config.decoder_layers						):
								# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__A   			= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''						)
								__A   			= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''						)
								# next, add query, keys and values (in that order) to the state dict
								__A   			= in_proj_weight[: hidden_size, :]
								__A   			= in_proj_bias[:config.hidden_size]
								__A   			= in_proj_weight[hidden_size : hidden_size * 2, :]
								__A   			= in_proj_bias[hidden_size : hidden_size * 2]
								__A   			= in_proj_weight[-hidden_size :, :]
								__A   			= in_proj_bias[-hidden_size :]
								# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__A   			= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''						)
								__A   			= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''						)
								# next, add query, keys and values (in that order) to the state dict
								__A   			= in_proj_weight[: hidden_size, :]
								__A   			= in_proj_bias[:config.hidden_size]
								__A   			= in_proj_weight[hidden_size : hidden_size * 2, :]
								__A   			= in_proj_bias[hidden_size : hidden_size * 2]
								__A   			= in_proj_weight[-hidden_size :, :]
								__A   			= in_proj_bias[-hidden_size :]
				# fmt: on


def 						UpperCAmelCase			(	)		->	torch.Tensor:







				"""simple docstring"""



				__A   			= "http://images.cocodataset.org/val2017/000000039769.jpg"
				__A   			= Image.open(requests.get(a_		,   stream=a_						).raw						)
				return im


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_ = False						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= get_maskformer_config(a_						)

				# load original state_dict
				with open(a_		,   "rb"						) as f:
								__A   			= pickle.load(a_						)
				__A   			= data["model"]

				# for name, param in state_dict.items():
				#     print(name, param.shape)

				# rename keys
				__A   			= create_rename_keys(a_						)
				for src, dest in rename_keys:
								rename_key(a_		,   a_		,   a_						)
				read_in_swin_q_k_v(a_		,   config.backbone_config						)
				read_in_decoder_q_k_v(a_		,   a_						)

				# update to torch tensors
				for key, value in state_dict.items():
								__A   			= torch.from_numpy(a_						)

				# load 🤗 model
				__A   			= MaskFormerForInstanceSegmentation(a_						)
				model.eval()

				for name, param in model.named_parameters():
								print(a_		,   param.shape						)

				__A			,							__A   			= model.load_state_dict(a_		,   strict=a_						)
				assert missing_keys == [
				    "model.pixel_level_module.encoder.model.layernorm.weight",
				    "model.pixel_level_module.encoder.model.layernorm.bias",
				]
				assert len(a_						) == 0, F'''Unexpected keys: {unexpected_keys}'''

				# verify results
				__A   			= prepare_img()
				if "vistas" in model_name:
								__A   			= 6_5
				elif "cityscapes" in model_name:
								__A   			= 6_5_5_3_5
				else:
								__A   			= 2_5_5
				__A   			= True if "ade" in model_name else False
				__A   			= MaskFormerImageProcessor(ignore_index=a_		,   reduce_labels=a_						)

				__A   			= image_processor(a_		,   return_tensors="pt"						)

				__A   			= model(**a_						)

				print("Logits:"		,   outputs.class_queries_logits[0, :3, :3]						)

				if model_name == "maskformer-swin-tiny-ade":
								__A   			= torch.tensor(
								    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]						)
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,   a_		,   atol=1E-4						)
				print("Looks ok!"						)

				if pytorch_dump_folder_path is not None:
								print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''						)
								Path(a_						).mkdir(exist_ok=a_						)
								model.save_pretrained(a_						)
								image_processor.save_pretrained(a_						)

				if push_to_hub:
								print("Pushing model and image processor to the hub..."						)
								model.push_to_hub(F'''nielsr/{model_name}'''						)
								image_processor.push_to_hub(F'''nielsr/{model_name}'''						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='maskformer-swin-tiny-ade',
					    type=str,
					    help=('Name of the MaskFormer model you\'d like to convert',),
					)
					parser.add_argument(
					    '--checkpoint_path',
					    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
					    type=str,
					    help='Path to the original state dict (.pth file).',
					)
					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.'
					)

					SCREAMING_SNAKE_CASE							:Optional[Any]							 =				parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)




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.
A      :    Union[str, Any]		=					{"LayoutLMv2Config", "LayoutLMv3Config"}







@is_pipeline_test
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       A__											=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       A__											=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              A__											=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              A__											=			{
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }





       @require_torch
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"       )

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

             lowerCamelCase__		:				List[str]    =  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}]       )

             lowerCamelCase__		:				Optional[int]    =  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}],
                 ]					,      )

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

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

             lowerCamelCase__		:				str    =  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}]]       )

             lowerCamelCase__		:				Optional[Any]    =  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}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  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 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             import torch

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

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





       @require_tf
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="tf"       )

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





       @slow
       @require_torch
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  pipeline("text-classification"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Tuple    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       @slow
       @require_tf
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  pipeline("text-classification"					,      framework="tf"       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[Any]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Dict    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  TextClassificationPipeline(model=__lowerCamelCase					,      tokenizer=__lowerCamelCase       )
             return text_classifier, ["HuggingFace is in", "This is another test"]





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             lowerCamelCase__		:				List[Any]    =  "HuggingFace is in"
             lowerCamelCase__		:				Tuple    =  text_classifier(__lowerCamelCase       )

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

             lowerCamelCase__		:				Optional[int]    =  ["HuggingFace is in ", "Paris is in France"]
             lowerCamelCase__		:				Dict    =  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
             lowerCamelCase__		:				List[Any]    =  text_classifier(__lowerCamelCase					,      top_k=__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  len(model.config.idalabel.values()       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N, [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N]					,      )

             lowerCamelCase__		:				Optional[int]    =  {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
             lowerCamelCase__		:				List[Any]    =  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.
             lowerCamelCase__		:				Any    =  [["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
             lowerCamelCase__		:				int    =  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()       )


