






from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFCvtForImageClassification, TFCvtModel
						from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor


class        __UpperCamelCase    (     _lowerCAmelCase   ):







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







		__lowercase             =  self.config_class(**self.inputs_dict			)
		self.parent.assertTrue(hasattr(_lowerCAmelCase       ,				"""embed_dim"""			)			)
		self.parent.assertTrue(hasattr(_lowerCAmelCase       ,				"""num_heads"""			)			)


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							List[str]=13       ,				_lowerCAmelCase      :							Optional[Any]=64       ,				_lowerCAmelCase      :							Optional[Any]=3       ,				_lowerCAmelCase      :							Optional[int]=[16, 48, 96]       ,				_lowerCAmelCase      :							int=[1, 3, 6]       ,				_lowerCAmelCase      :							int=[1, 2, 10]       ,				_lowerCAmelCase      :							Optional[Any]=[7, 3, 3]       ,				_lowerCAmelCase      :							Union[str, Any]=[4, 2, 2]       ,				_lowerCAmelCase      :							Optional[int]=[2, 1, 1]       ,				_lowerCAmelCase      :							List[Any]=[2, 2, 2]       ,				_lowerCAmelCase      :							Optional[Any]=[False, False, True]       ,				_lowerCAmelCase      :							Optional[Any]=[0.0, 0.0, 0.0]       ,				_lowerCAmelCase      :							Dict=0.02       ,				_lowerCAmelCase      :							Union[str, Any]=1e-12       ,				_lowerCAmelCase      :							Tuple=True       ,				_lowerCAmelCase      :							Any=True       ,				_lowerCAmelCase      :							Tuple=2       ,				)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  image_size
		__lowercase             =  patch_sizes
		__lowercase             =  patch_stride
		__lowercase             =  patch_padding
		__lowercase             =  is_training
		__lowercase             =  use_labels
		__lowercase             =  num_labels
		__lowercase             =  num_channels
		__lowercase             =  embed_dim
		__lowercase             =  num_heads
		__lowercase             =  stride_kv
		__lowercase             =  depth
		__lowercase             =  cls_token
		__lowercase             =  attention_drop_rate
		__lowercase             =  initializer_range
		__lowercase             =  layer_norm_eps







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







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

		__lowercase             =  None
		if self.use_labels:
			# create a random int32 tensor of given shape
			__lowercase             =  ids_tensor([self.batch_size]       ,				self.num_labels			)

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







	def  _a   (  self      :							Tuple			)		-> str:
		"""simple docstring"""







		return CvtConfig(
		    image_size=self.image_size       ,				num_labels=self.num_labels       ,				num_channels=self.num_channels       ,				embed_dim=self.embed_dim       ,				num_heads=self.num_heads       ,				patch_sizes=self.patch_sizes       ,				patch_padding=self.patch_padding       ,				patch_stride=self.patch_stride       ,				stride_kv=self.stride_kv       ,				depth=self.depth       ,				cls_token=self.cls_token       ,				attention_drop_rate=self.attention_drop_rate       ,				initializer_range=self.initializer_range       ,				)







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







		__lowercase             =  TFCvtModel(config=_lowerCAmelCase			)
		__lowercase             =  model(_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		__lowercase             =  (self.image_size, self.image_size)
		__lowercase       ,						__lowercase             =  image_size[0], image_size[1]
		for i in range(len(self.depth			)			):
			__lowercase             =  floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1			)
			__lowercase             =  floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1			)
		self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.embed_dim[-1], height, width)			)







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.num_labels
		__lowercase             =  TFCvtForImageClassification(_lowerCAmelCase			)
		__lowercase             =  model(_lowerCAmelCase       ,				labels=_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)			)







	def  _a   (  self      :							Tuple			)		-> Tuple:
		"""simple docstring"""







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


@require_tf
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Optional[Any]   =       (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
	__snake_case	:Optional[Any]   =       (
	    {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
	    if is_tf_available()
	    else {}
	)
	__snake_case	:str   =       False
	__snake_case	:int   =       False
	__snake_case	:List[Any]   =       False
	__snake_case	:str   =       False
	__snake_case	:str   =       False







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







		__lowercase             =  TFCvtModelTester(self			)
		__lowercase             =  TFCvtConfigTester(self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase       ,				hidden_size=37			)







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







		self.config_tester.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()







	@unittest.skip(reason="""Cvt does not output attentions"""			)
	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		pass







	@unittest.skip(reason="""Cvt does not use inputs_embeds"""			)
	def  _a   (  self      :							Union[str, Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	@unittest.skip(reason="""Cvt does not support input and output embeddings"""			)
	def  _a   (  self      :							Optional[Any]			)		-> Tuple:
		"""simple docstring"""







		pass







	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""			)			) == 0       ,				reason="""TF does not support backprop for grouped convolutions on CPU."""       ,				)
	def  _a   (  self      :							Any			)		-> Tuple:
		"""simple docstring"""







		super().test_dataset_conversion()







	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""			)			) == 0       ,				reason="""TF does not support backprop for grouped convolutions on CPU."""       ,				)
	@slow
	def  _a   (  self      :							Tuple			)		-> List[Any]:
		"""simple docstring"""







		super().test_keras_fit()







	@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"""			)
	def  _a   (  self      :							Any			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  tf.keras.mixed_precision.Policy("""mixed_float16"""			)
		tf.keras.mixed_precision.set_global_policy(_lowerCAmelCase			)
		super().test_keras_fit()
		tf.keras.mixed_precision.set_global_policy("""float32"""			)







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







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

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

			__lowercase             =  ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]       ,				_lowerCAmelCase			)







	def  _a   (  self      :							int			)		-> int:
		"""simple docstring"""







		def check_hidden_states_output(_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Any			):
			__lowercase             =  model_class(_lowerCAmelCase			)

			__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)			)
			__lowercase             =  outputs.hidden_states

			__lowercase             =  len(self.model_tester.depth			)
			self.assertEqual(len(_lowerCAmelCase			)       ,				_lowerCAmelCase			)

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

		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  True
			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)

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

			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_lowerCAmelCase			)







	def  _a   (  self      :							int			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							List[Any]			)		-> Dict:
		"""simple docstring"""







		for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__lowercase             =  TFCvtModel.from_pretrained(_lowerCAmelCase			)
			self.assertIsNotNone(_lowerCAmelCase			)





def 				snake_case  (     ):




	'''simple docstring'''







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




@require_tf
@require_vision
class        __UpperCamelCase    (     unittest.TestCase   ):







	@cached_property
	def  _a   (  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)







	@slow
	def  _a   (  self      :							int			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""tf"""			)

		# forward pass
		__lowercase             =  model(**_lowerCAmelCase			)

		# verify the logits
		__lowercase             =  tf.TensorShape((1, 1000)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)

		__lowercase             =  tf.constant([0.9_285, 0.9_015, -0.3_150]			)
		self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,				_lowerCAmelCase       ,				atol=1e-4			)			)









import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__UpperCamelCase      :      Optional[Any]     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()
	__lowercase             =  []

	def parse_line(lowerCamelCase					):
		for line in fp:
			if isinstance(lowerCamelCase				,  lowerCamelCase					):
				__lowercase             =  line.decode("""UTF-8"""					)
			if "warnings summary (final)" in line:
				continue
			# This means we are outside the body of a warning
			elif not line.startswith(""" """					):
				# process a single warning and move it to `selected_warnings`.
				if len(lowerCamelCase					) > 0:
					__lowercase             =  """\n""".join(lowerCamelCase					)
					# Only keep the warnings specified in `targets`
					if any(F': {x}: ' in warning for x in targets					):
						selected_warnings.add(lowerCamelCase					)
					buffer.clear()
				continue
			else:
				__lowercase             =  line.strip()
				buffer.append(lowerCamelCase					)

	if from_gh:
		for filename in os.listdir(lowerCamelCase					):
			__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)
			if not os.path.isdir(lowerCamelCase					):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(lowerCamelCase					) as fp:
					parse_line(lowerCamelCase					)
	else:
		try:
			with zipfile.ZipFile(lowerCamelCase					) as z:
				for filename in z.namelist():
					if not os.path.isdir(lowerCamelCase					):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(lowerCamelCase					) as fp:
							parse_line(lowerCamelCase					)
		except Exception:
			logger.warning(
			    F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'					)

	return selected_warnings





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()

	__lowercase             =  [os.path.join(lowerCamelCase				,  lowerCamelCase					) for p in os.listdir(lowerCamelCase					) if (p.endswith(""".zip"""					) or from_gh)]
	for p in paths:
		selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase				,  lowerCamelCase					)					)

	return selected_warnings


if __name__ == "__main__":





						def 				snake_case  (     lowerCamelCase					):




							'''simple docstring'''







							return values.split(""","""					)

						__UpperCamelCase      :      Union[str, Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
						parser.add_argument(
						    """--output_dir""",
						    type=str,
						    required=True,
						    help="""Where to store the downloaded artifacts and other result files.""",
						)
						parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
						# optional parameters
						parser.add_argument(
						    """--targets""",
						    default="""DeprecationWarning,UserWarning,FutureWarning""",
						    type=list_str,
						    help="""Comma-separated list of target warning(s) which we want to extract.""",
						)
						parser.add_argument(
						    """--from_gh""",
						    action="""store_true""",
						    help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
						)

						__UpperCamelCase      :      List[str]     						=						parser.parse_args()

						__UpperCamelCase      :      Union[str, Any]     						=						args.from_gh
						if from_gh:
												# The artifacts have to be downloaded using `actions/download-artifact@v3`
												pass
						else:
												os.makedirs(args.output_dir, exist_ok=True)

												# get download links
												__UpperCamelCase      :      Any     						=						get_artifacts_links(args.workflow_run_id, token=args.token)
												with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
																		json.dump(artifacts, fp, ensure_ascii=False, indent=4)

												# download artifacts
												for idx, (name, url) in enumerate(artifacts.items()):
																		print(name)
																		print(url)
																		print("""=""" * 80)
																		download_artifact(name, url, args.output_dir, args.token)
																		# Be gentle to GitHub
																		time.sleep(1)

    # extract warnings from artifacts
						__UpperCamelCase      :      Union[str, Any]     						=						extract_warnings(args.output_dir, args.targets)
						__UpperCamelCase      :      Any     						=						sorted(selected_warnings)
						with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
												json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

