






"""simple docstring"""
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

							from transformers import ViTMSNForImageClassification, ViTMSNModel
							from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=13     ,     lowerCAmelCase_=30     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=37     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=None     ,     ):
		A_		:						Dict	    =   parent
		A_		:						Union[str, Any]	    =   batch_size
		A_		:						Tuple	    =   image_size
		A_		:						List[str]	    =   patch_size
		A_		:						Union[str, Any]	    =   num_channels
		A_		:						Tuple	    =   is_training
		A_		:						Dict	    =   use_labels
		A_		:						Optional[Any]	    =   hidden_size
		A_		:						Dict	    =   num_hidden_layers
		A_		:						Union[str, Any]	    =   num_attention_heads
		A_		:						Tuple	    =   intermediate_size
		A_		:						Union[str, Any]	    =   hidden_act
		A_		:						Any	    =   hidden_dropout_prob
		A_		:						Optional[Any]	    =   attention_probs_dropout_prob
		A_		:						Any	    =   type_sequence_label_size
		A_		:						Tuple	    =   initializer_range
		A_		:						Any	    =   scope

		# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
		A_		:						Dict	    =   (image_size // patch_size) ** 2
		A_		:						Optional[int]	    =   num_patches + 1





	def    lowerCamelCase(self							):
		A_		:						int	    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		A_		:						Optional[int]	    =   None
		if self.use_labels:
			A_		:						Union[str, Any]	    =   ids_tensor([self.batch_size]     ,     self.type_sequence_label_size							)

		A_		:						Optional[int]	    =   self.get_config()

		return config, pixel_values, labels





	def    lowerCamelCase(self							):
		return ViTMSNConfig(
		    image_size=self.image_size     ,     patch_size=self.patch_size     ,     num_channels=self.num_channels     ,     hidden_size=self.hidden_size     ,     num_hidden_layers=self.num_hidden_layers     ,     num_attention_heads=self.num_attention_heads     ,     intermediate_size=self.intermediate_size     ,     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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Optional[Any]	    =   ViTMSNModel(config=UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						List[Any]	    =   model(UpperCamelCase__							)
		self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						int	    =   self.type_sequence_label_size
		A_		:						List[Any]	    =   ViTMSNForImageClassification(UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						str	    =   model(UpperCamelCase__     ,     labels=UpperCamelCase__							)
		print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""							)
		print("""Labels: {labels}"""							)
		self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size)							)

		# test greyscale images
		A_		:						List[Any]	    =   1
		A_		:						int	    =   ViTMSNForImageClassification(UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()

		A_		:						Dict	    =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		A_		:						Optional[Any]	    =   model(UpperCamelCase__							)
		self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size)							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   self.prepare_config_and_inputs()
		A_		:						Optional[Any]	    =   config_and_inputs
		A_		:						List[Any]	    =   {'''pixel_values''': pixel_values}
		return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE  (				UpperCamelCase_       ,       UpperCamelCase_       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
	_A       :				List[str]    =				(
	    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_A       :				Optional[int]    =				False
	_A       :				Dict    =				False
	_A       :				int    =				False
	_A       :				Any    =				False





	def    lowerCamelCase(self							):
		A_		:						int	    =   ViTMSNModelTester(self							)
		A_		:						Optional[int]	    =   ConfigTester(self     ,     config_class=UpperCamelCase__     ,     has_text_modality=UpperCamelCase__     ,     hidden_size=37							)





	def    lowerCamelCase(self							):
		self.config_tester.run_common_tests()





	@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""							)
	def    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		A_		:						Any	    =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:						Any	    =   model_class(UpperCamelCase__							)
			self.assertIsInstance(model.get_input_embeddings()     ,     (nn.Module)							)
			A_		:						Any	    =   model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(UpperCamelCase__     ,     nn.Linear							)							)





	def    lowerCamelCase(self							):
		A_		:						str	    =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:						Optional[int]	    =   model_class(UpperCamelCase__							)
			A_		:						int	    =   inspect.signature(model.forward							)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			A_		:						List[Any]	    =   [*signature.parameters.keys()]

			A_		:						Optional[int]	    =   ['''pixel_values''']
			self.assertListEqual(arg_names[:1]     ,     UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__							)





	@slow
	def    lowerCamelCase(self							):





		for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:						Optional[Any]	    =   ViTMSNModel.from_pretrained(UpperCamelCase__							)
			self.assertIsNotNone(UpperCamelCase__							)






def 			__UpperCamelCase	(		):
	A_		:						Any	    =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image




@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	@cached_property
	def    lowerCamelCase(self							):
		return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""							) if is_vision_available() else None





	@slow
	def    lowerCamelCase(self							):
		torch.manual_seed(2							)
		A_		:						Optional[int]	    =   ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""							).to(UpperCamelCase__							)

		A_		:						int	    =   self.default_image_processor
		A_		:						Union[str, Any]	    =   prepare_img()
		A_		:						Tuple	    =   image_processor(images=UpperCamelCase__     ,     return_tensors="""pt"""							).to(UpperCamelCase__							)

		# forward pass
		with torch.no_grad():
			A_		:						Optional[int]	    =   model(**UpperCamelCase__							)

		# verify the logits
		A_		:						Optional[int]	    =   torch.Size((1, 1000)							)
		self.assertEqual(outputs.logits.shape     ,     UpperCamelCase__							)

		A_		:						List[Any]	    =   torch.tensor([-0.0803, -0.4454, -0.2375]							).to(UpperCamelCase__							)

		self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,     UpperCamelCase__     ,     atol=1e-4							)							)









"""simple docstring"""
import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_lowerCAmelCase         =							"http://www.mocksite.com/file1.txt"
_lowerCAmelCase         =							"\"text\": [\"foo\", \"foo\"]"
_lowerCAmelCase         =							"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				Optional[int]    =				200
	_A       :				Dict    =				{"""Content-Length""": """100"""}
	_A       :				List[Any]    =				{}





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return [bytes(lowerCAmelCase_     ,     """utf-8"""							)]






def 			__UpperCamelCase	(		*snake_case__   ,   **snake_case__					):
	return MockResponse()






@pytest.mark.parametrize("""urls_type"""   ,   [str, list, dict]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	import requests

	monkeypatch.setattr(snake_case__   ,   """request"""   ,   snake_case__					)

	A_		:						Optional[int]	    =   URL
	if issubclass(snake_case__   ,   snake_case__					):
		A_		:						str	    =   url
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Union[str, Any]	    =   [url]
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						List[Any]	    =   {"""train""": url}
	A_		:						Tuple	    =   """dummy"""
	A_		:						List[str]	    =   """downloads"""
	A_		:						List[str]	    =   tmp_path
	A_		:						str	    =   DownloadConfig(
	    cache_dir=os.path.join(snake_case__   ,   snake_case__					)   ,   use_etag=snake_case__   ,   )
	A_		:						Any	    =   DownloadManager(dataset_name=snake_case__   ,   download_config=snake_case__					)
	A_		:						Dict	    =   dl_manager.download(snake_case__					)
	A_		:						List[str]	    =   urls
	for downloaded_paths in [downloaded_paths]:
		if isinstance(snake_case__   ,   snake_case__					):
			A_		:						Optional[Any]	    =   [downloaded_paths]
			A_		:						Tuple	    =   [urls]
		elif isinstance(snake_case__   ,   snake_case__					):
			assert "train" in downloaded_paths.keys()
			A_		:						Union[str, Any]	    =   downloaded_paths.values()
			A_		:						Tuple	    =   urls.values()
		assert downloaded_paths
		for downloaded_path, input_url in zip(snake_case__   ,   snake_case__					):
			assert downloaded_path == dl_manager.downloaded_paths[input_url]
			A_		:						Union[str, Any]	    =   Path(snake_case__					)
			A_		:						str	    =   downloaded_path.parts
			assert parts[-1] == HASH
			assert parts[-2] == cache_subdir
			assert downloaded_path.exists()
			A_		:						List[str]	    =   downloaded_path.read_text()
			assert content == CONTENT
			A_		:						Union[str, Any]	    =   downloaded_path.with_suffix(""".json"""					)
			assert metadata_downloaded_path.exists()
			A_		:						List[str]	    =   json.loads(metadata_downloaded_path.read_text()					)
			assert metadata_content == {"url": URL, "etag": None}






@pytest.mark.parametrize("""paths_type"""   ,   [str, list, dict]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   str(snake_case__					)
	if issubclass(snake_case__   ,   snake_case__					):
		A_		:						List[str]	    =   filename
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Tuple	    =   [filename]
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Dict	    =   {"""train""": filename}
	A_		:						Union[str, Any]	    =   """dummy"""
	A_		:						Dict	    =   xz_file.parent
	A_		:						List[str]	    =   """extracted"""
	A_		:						int	    =   DownloadConfig(
	    cache_dir=snake_case__   ,   use_etag=snake_case__   ,   )
	A_		:						str	    =   DownloadManager(dataset_name=snake_case__   ,   download_config=snake_case__					)
	A_		:						int	    =   dl_manager.extract(snake_case__					)
	A_		:						int	    =   paths
	for extracted_paths in [extracted_paths]:
		if isinstance(snake_case__   ,   snake_case__					):
			A_		:						Dict	    =   [extracted_paths]
			A_		:						str	    =   [paths]
		elif isinstance(snake_case__   ,   snake_case__					):
			assert "train" in extracted_paths.keys()
			A_		:						Any	    =   extracted_paths.values()
			A_		:						str	    =   paths.values()
		assert extracted_paths
		for extracted_path, input_path in zip(snake_case__   ,   snake_case__					):
			assert extracted_path == dl_manager.extracted_paths[input_path]
			A_		:						str	    =   Path(snake_case__					)
			A_		:						List[str]	    =   extracted_path.parts
			assert parts[-1] == hash_url_to_filename(snake_case__   ,   etag=snake_case__					)
			assert parts[-2] == extracted_subdir
			assert extracted_path.exists()
			A_		:						Optional[Any]	    =   extracted_path.read_text()
			A_		:						Optional[int]	    =   text_file.read_text()
			assert extracted_file_content == expected_file_content






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	assert path.endswith(""".jsonl"""					)
	for num_items, line in enumerate(snake_case__   ,   start=1					):
		A_		:						List[str]	    =   json.loads(line.decode("""utf-8"""					)					)
		assert item.keys() == {"col_1", "col_2", "col_3"}
	assert num_items == 4






@pytest.mark.parametrize("""archive_jsonl"""   ,   ["""tar_jsonl_path""", """zip_jsonl_path"""]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   request.getfixturevalue(snake_case__					)
	A_		:						List[str]	    =   DownloadManager()
	for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
		_test_jsonl(snake_case__   ,   snake_case__					)
	assert num_jsonl == 2






@pytest.mark.parametrize("""archive_nested_jsonl"""   ,   ["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   request.getfixturevalue(snake_case__					)
	A_		:						Union[str, Any]	    =   DownloadManager()
	for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
		for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
			_test_jsonl(snake_case__   ,   snake_case__					)
	assert num_tar == 1
	assert num_jsonl == 2






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						int	    =   DownloadManager()
	for num_file, file in enumerate(dl_manager.iter_files(snake_case__					)   ,   start=1					):
		assert os.path.basename(snake_case__					) == ("test.txt" if num_file == 1 else "train.txt")
	assert num_file == 2

