






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def        _a  (     UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				List[str]=True    ,   UpperCamelCase_					:				List[str]="pt"   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						{"add_prefix_space": True} if isinstance(UpperCamelCase_    ,   UpperCamelCase_   ) and not line.startswith(" "   ) else {}
						lowerCAmelCase__       =						padding_side
						return tokenizer(
						    [line]    ,   max_length=UpperCamelCase_    ,   padding="max_length" if pad_to_max_length else None    ,   truncation=UpperCamelCase_    ,   return_tensors=UpperCamelCase_    ,   add_special_tokens=UpperCamelCase_    ,   **UpperCamelCase_    ,   )


def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Dict=None    ,   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						input_ids.ne(UpperCamelCase_   ).any(dim=0   )
						if attention_mask is None:
												return input_ids[:, keep_column_mask]
						else:
												return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase="train"		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    __UpperCAmelCase=""		,    )->					Optional[Any]:







							'''simple docstring'''





							super().__init__()
							lowerCAmelCase__       =						Path(__UpperCAmelCase       ).joinpath(type_path + ".source"       )
							lowerCAmelCase__       =						Path(__UpperCAmelCase       ).joinpath(type_path + ".target"       )
							lowerCAmelCase__       =						self.get_char_lens(self.src_file       )
							lowerCAmelCase__       =						max_source_length
							lowerCAmelCase__       =						max_target_length
							assert min(self.src_lens       ) > 0, F"found empty line in {self.src_file}"
							lowerCAmelCase__       =						tokenizer
							lowerCAmelCase__       =						prefix
							if n_obs is not None:
													lowerCAmelCase__       =						self.src_lens[:n_obs]
							lowerCAmelCase__       =						src_lang
							lowerCAmelCase__       =						tgt_lang

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







							'''simple docstring'''





							return len(self.src_lens       )

	def __getitem__(							self		,    __UpperCAmelCase       )->					Dict[str, torch.Tensor]:







							'''simple docstring'''





							lowerCAmelCase__       =						index + 1  # linecache starts at 1
							lowerCAmelCase__       =						self.prefix + linecache.getline(str(self.src_file       )		,    __UpperCAmelCase       ).rstrip("\n"       )
							lowerCAmelCase__       =						linecache.getline(str(self.tgt_file       )		,    __UpperCAmelCase       ).rstrip("\n"       )
							assert source_line, F"empty source line for index {index}"
							assert tgt_line, F"empty tgt line for index {index}"

							# Need to add eos token manually for T5
							if isinstance(self.tokenizer		,    __UpperCAmelCase       ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							lowerCAmelCase__       =						(
							    self.tokenizer.question_encoder if isinstance(self.tokenizer		,    __UpperCAmelCase       ) else self.tokenizer
							)
							lowerCAmelCase__       =						self.tokenizer.generator if isinstance(self.tokenizer		,    __UpperCAmelCase       ) else self.tokenizer

							lowerCAmelCase__       =						encode_line(__UpperCAmelCase		,    __UpperCAmelCase		,    self.max_source_length		,    "right"       )
							lowerCAmelCase__       =						encode_line(__UpperCAmelCase		,    __UpperCAmelCase		,    self.max_target_length		,    "right"       )

							lowerCAmelCase__       =						source_inputs["input_ids"].squeeze()
							lowerCAmelCase__       =						target_inputs["input_ids"].squeeze()
							lowerCAmelCase__       =						source_inputs["attention_mask"].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}

	@staticmethod
	def 		UpperCAmelCase  (							__UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							return [len(__UpperCAmelCase       ) for x in Path(__UpperCAmelCase       ).open().readlines()]


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Dict[str, torch.Tensor]:







							'''simple docstring'''





							lowerCAmelCase__       =						torch.stack([x["input_ids"] for x in batch]       )
							lowerCAmelCase__       =						torch.stack([x["attention_mask"] for x in batch]       )
							lowerCAmelCase__       =						torch.stack([x["decoder_input_ids"] for x in batch]       )
							lowerCAmelCase__       =						(
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer		,    __UpperCAmelCase       )
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase__       =						(
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer		,    __UpperCAmelCase       )
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase__       =						trim_batch(__UpperCAmelCase		,    __UpperCAmelCase       )
							lowerCAmelCase__      ,							lowerCAmelCase__       =						trim_batch(__UpperCAmelCase		,    __UpperCAmelCase		,    attention_mask=__UpperCAmelCase       )
							lowerCAmelCase__       =						{
							    "input_ids": source_ids,
							    "attention_mask": source_mask,
							    "decoder_input_ids": y,
							}
							return batch





a_							=    getLogger(__name__)


def        _a  (     UpperCamelCase_					:				List[List]   ) ->   List[str]:
						"""simple docstring"""







						return list(itertools.chain.from_iterable(UpperCamelCase_   )   )


def        _a  (     UpperCamelCase_					:				str   ) ->   None:
						"""simple docstring"""







						lowerCAmelCase__       =						get_git_info()
						save_json(UpperCamelCase_    ,   os.path.join(UpperCamelCase_    ,   "git_log.json"   )   )


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				Dict=4    ,   **UpperCamelCase_					:				Tuple   ) ->   int:
						"""simple docstring"""







						with open(UpperCamelCase_    ,   "w"   ) as f:
												json.dump(UpperCamelCase_    ,   UpperCamelCase_    ,   indent=UpperCamelCase_    ,   **UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						with open(UpperCamelCase_   ) as f:
												return json.load(UpperCamelCase_   )


def        _a  (     ) ->   Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						git.Repo(search_parent_directories=UpperCamelCase_   )
						lowerCAmelCase__       =						{
						    "repo_id": str(UpperCamelCase_   ),
						    "repo_sha": str(repo.head.object.hexsha   ),
						    "repo_branch": str(repo.active_branch   ),
						    "hostname": str(socket.gethostname()   ),
						}
						return repo_infos


def        _a  (     UpperCamelCase_					:				Callable    ,   UpperCamelCase_					:				Iterable   ) ->   List:
						"""simple docstring"""







						return list(map(UpperCamelCase_    ,   UpperCamelCase_   )   )


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				List[Any]   ) ->   Optional[int]:
						"""simple docstring"""







						with open(UpperCamelCase_    ,   "wb"   ) as f:
												return pickle.dump(UpperCamelCase_    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Dict   ) ->   Tuple:
						"""simple docstring"""







						def remove_articles(UpperCamelCase_					:				List[str]   ):
												return re.sub(R"\b(a|an|the)\b"    ,   " "    ,   UpperCamelCase_   )

						def white_space_fix(UpperCamelCase_					:				Dict   ):
												return " ".join(text.split()   )

						def remove_punc(UpperCamelCase_					:				Union[str, Any]   ):
												lowerCAmelCase__       =						set(string.punctuation   )
												return "".join(ch for ch in text if ch not in exclude   )

						def lower(UpperCamelCase_					:				Optional[int]   ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_   )   )   )   )


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Union[str, Any]   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						normalize_answer(UpperCamelCase_   ).split()
						lowerCAmelCase__       =						normalize_answer(UpperCamelCase_   ).split()
						lowerCAmelCase__       =						Counter(UpperCamelCase_   ) & Counter(UpperCamelCase_   )
						lowerCAmelCase__       =						sum(common.values()   )
						if num_same == 0:
												return 0
						lowerCAmelCase__       =						1.0 * num_same / len(UpperCamelCase_   )
						lowerCAmelCase__       =						1.0 * num_same / len(UpperCamelCase_   )
						lowerCAmelCase__       =						(2 * precision * recall) / (precision + recall)
						return fa


def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				int   ) ->   List[str]:
						"""simple docstring"""







						return normalize_answer(UpperCamelCase_   ) == normalize_answer(UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				List[str]   ) ->   Dict:
						"""simple docstring"""







						assert len(UpperCamelCase_   ) == len(UpperCamelCase_   )
						lowerCAmelCase__       =						0
						for hypo, pred in zip(UpperCamelCase_    ,   UpperCamelCase_   ):
												em += exact_match_score(UpperCamelCase_    ,   UpperCamelCase_   )
						if len(UpperCamelCase_   ) > 0:
												em /= len(UpperCamelCase_   )
						return {"em": em}


def        _a  (     UpperCamelCase_					:				List[Any]   ) ->   Optional[Any]:
						"""simple docstring"""







						return model_prefix.startswith("rag"   )





def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Optional[int]   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						{p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						lowerCAmelCase__       =						"dropout_rate"
						for p in extra_params:
												if getattr(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   ):
																		if not hasattr(UpperCamelCase_    ,   UpperCamelCase_   ) and not hasattr(UpperCamelCase_    ,   equivalent_param[p]   ):
																								logger.info("config doesn't have a `{}` attribute".format(UpperCamelCase_   )   )
																								delattr(UpperCamelCase_    ,   UpperCamelCase_   )
																								continue
																		lowerCAmelCase__       =						p if hasattr(UpperCamelCase_    ,   UpperCamelCase_   ) else equivalent_param[p]
																		setattr(UpperCamelCase_    ,   UpperCamelCase_    ,   getattr(UpperCamelCase_    ,   UpperCamelCase_   )   )
																		delattr(UpperCamelCase_    ,   UpperCamelCase_   )
						return hparams, config














import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor


class      lowercase__     (			_UpperCAmelCase	):

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.config_class(**self.inputs_dict       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "hidden_sizes"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_attention_heads"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_encoder_blocks"       )       )





class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=64		,    __UpperCAmelCase=3		,    __UpperCAmelCase=4		,    __UpperCAmelCase=[2, 2, 2, 2]		,    __UpperCAmelCase=[8, 4, 2, 1]		,    __UpperCAmelCase=[16, 32, 64, 128]		,    __UpperCAmelCase=[1, 4, 8, 16]		,    __UpperCAmelCase=[1, 2, 4, 8]		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase="gelu"		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=3		,    __UpperCAmelCase=None		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						image_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						num_encoder_blocks
							lowerCAmelCase__       =						sr_ratios
							lowerCAmelCase__       =						depths
							lowerCAmelCase__       =						hidden_sizes
							lowerCAmelCase__       =						downsampling_rates
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						hidden_act
							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						num_labels
							lowerCAmelCase__       =						scope

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							lowerCAmelCase__       =						None
							if self.use_labels:
													lowerCAmelCase__       =						ids_tensor([self.batch_size, self.image_size, self.image_size]		,    self.num_labels       )

							lowerCAmelCase__       =						self.get_config()
							return config, pixel_values, labels

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







							'''simple docstring'''





							return SegformerConfig(
							    image_size=self.image_size		,    num_channels=self.num_channels		,    num_encoder_blocks=self.num_encoder_blocks		,    depths=self.depths		,    hidden_sizes=self.hidden_sizes		,    num_attention_heads=self.num_attention_heads		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    initializer_range=self.initializer_range		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModel(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							lowerCAmelCase__       =						lowerCAmelCase__       =						self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape		,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							self.parent.assertGreater(result.loss		,    0.0       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						1
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						torch.randint(0		,    1		,    (self.batch_size, self.image_size, self.image_size)       ).to(__UpperCAmelCase       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertGreater(result.loss		,    0.0       )


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







							'''simple docstring'''





							lowerCAmelCase__       =						self.prepare_config_and_inputs()
							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						config_and_inputs
							lowerCAmelCase__       =						{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a_         =(
	    {
	        """feature-extraction""": SegformerModel,
	        """image-classification""": SegformerForImageClassification,
	        """image-segmentation""": SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	a_         =True
	a_         =False
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModelTester(self       )
							lowerCAmelCase__       =						SegformerConfigTester(self		,    config_class=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							self.config_tester.run_common_tests()

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase       )

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase       )

	@unittest.skip("SegFormer does not use inputs_embeds"       )
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							pass

	@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"       )
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							pass

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCAmelCase__       =						["pixel_values"]
													self.assertListEqual(arg_names[:1]		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						False
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													lowerCAmelCase__       =						sum(self.model_tester.depths       )
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

													# verify the last attentions (last block, last layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 32) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]       )		,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]		,    )
													lowerCAmelCase__       =						len(__UpperCAmelCase       )

													# Check attention is always last and order is fine
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													self.assertEqual(out_len + 1		,    len(__UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )
													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							def check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()

													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.hidden_states

													lowerCAmelCase__       =						self.model_tester.num_encoder_blocks
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]       )		,    [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]		,    )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCAmelCase__       =						True

													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

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







							'''simple docstring'''





							if not self.model_tester.is_training:
													return

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													if model_class in get_values(__UpperCAmelCase       ):
																			continue

													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.train()
													lowerCAmelCase__       =						self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase		,    return_labels=__UpperCAmelCase       )
													lowerCAmelCase__       =						model(**__UpperCAmelCase       ).loss
													loss.backward()

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







							'''simple docstring'''





							pass


	@slow
	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__       =						SegformerModel.from_pretrained(__UpperCAmelCase       )
													self.assertIsNotNone(__UpperCAmelCase       )





def        _a  (     ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image





@require_torch
class      lowercase__     (			unittest.TestCase	):

	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
							        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
							        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )

	@slow
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained(
							    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
							        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
							        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-1       )       )


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







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						outputs.logits.detach().cpu()

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase		,    target_sizes=[(500, 300)]       )
							lowerCAmelCase__       =						torch.Size((500, 300)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase       )
							lowerCAmelCase__       =						torch.Size((128, 128)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )






