






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

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = self.config_class(**self.inputs_dict				)
		self.parent.assertTrue(hasattr(lowerCAmelCase_  ,  """tf_padding"""				)				)
		self.parent.assertTrue(hasattr(lowerCAmelCase_  ,  """depth_multiplier"""				)				)






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=13  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=32  ,  lowerCAmelCase_=0.25  ,  lowerCAmelCase_=8  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=1024  ,  lowerCAmelCase_=32  ,  lowerCAmelCase_="relu6"  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=0.02  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=10  ,  lowerCAmelCase_=None  ,  ):
		A_		:    Optional[int]						     = parent
		A_		:    List[Any]						     = batch_size
		A_		:    Tuple						     = num_channels
		A_		:    Optional[Any]						     = image_size
		A_		:    str						     = depth_multiplier
		A_		:    Optional[Any]						     = min_depth
		A_		:    Tuple						     = tf_padding
		A_		:    Tuple						     = int(last_hidden_size * depth_multiplier				)
		A_		:    Union[str, Any]						     = output_stride
		A_		:    List[Any]						     = hidden_act
		A_		:    Optional[Any]						     = classifier_dropout_prob
		A_		:    Optional[int]						     = use_labels
		A_		:    List[Any]						     = is_training
		A_		:    Tuple						     = num_labels
		A_		:    Optional[Any]						     = initializer_range
		A_		:    Union[str, Any]						     = scope





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

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

		A_		:    List[str]						     = self.get_config()

		return config, pixel_values, labels, pixel_labels





	def  lowerCamelCase(self				):
		return MobileNetVaConfig(
		    num_channels=self.num_channels  ,  image_size=self.image_size  ,  depth_multiplier=self.depth_multiplier  ,  min_depth=self.min_depth  ,  tf_padding=self.tf_padding  ,  hidden_act=self.hidden_act  ,  classifier_dropout_prob=self.classifier_dropout_prob  ,  initializer_range=self.initializer_range  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = MobileNetVaModel(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Any						     = model(lowerCAmelCase_				)
		self.parent.assertEqual(
		    result.last_hidden_state.shape  ,  (
		        self.batch_size,
		        self.last_hidden_size,
		        self.image_size // self.output_stride,
		        self.image_size // self.output_stride,
		    )  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Dict						     = self.num_labels
		A_		:    Union[str, Any]						     = MobileNetVaForImageClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Tuple						     = model(lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)				)





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.prepare_config_and_inputs()
		A_     ,  A_     ,  A_     ,  A_		:    Union[str, Any]						     = config_and_inputs
		A_		:    List[Any]						     = {"""pixel_values""": pixel_values}
		return config, inputs_dict






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





	"""simple docstring"""




	_A							:					Dict 			=					(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
	_A							:					str 			=					(
	    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_A							:					int 			=					False
	_A							:					List[Any] 			=					False
	_A							:					str 			=					False
	_A							:					str 			=					False





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = MobileNetVaModelTester(self				)
		A_		:    str						     = MobileNetVaConfigTester(self  ,  config_class=lowerCAmelCase_  ,  has_text_modality=lowerCAmelCase_				)





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





	@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""				)
	def  lowerCamelCase(self				):
		pass





	@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""				)
	def  lowerCamelCase(self				):
		pass





	@unittest.skip(reason="""MobileNetV1 does not output attentions"""				)
	def  lowerCamelCase(self				):
		pass





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

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

			A_		:    Any						     = ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    str						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		def check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
			A_		:    Any						     = model_class(lowerCAmelCase_				)
			model.to(lowerCAmelCase_				)
			model.eval()

			with torch.no_grad():
				A_		:    Any						     = model(**self._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_				)				)

			A_		:    Union[str, Any]						     = outputs.hidden_states

			A_		:    List[str]						     = 26
			self.assertEqual(len(lowerCAmelCase_				)  ,  lowerCAmelCase_				)

		A_     ,  A_		:    int						     = self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:    Dict						     = True
			check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			A_		:    List[Any]						     = True

			check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)





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





	@slow
	def  lowerCamelCase(self				):
		for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:    Optional[int]						     = MobileNetVaModel.from_pretrained(lowerCAmelCase_				)
			self.assertIsNotNone(lowerCAmelCase_				)






def       __UpperCamelCase						(		):
	A_		:    Union[str, 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 (
		    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""				) if is_vision_available() else None
		)





	@slow
	def  lowerCamelCase(self				):
		A_		:    Tuple						     = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""				).to(lowerCAmelCase_				)

		A_		:    List[str]						     = self.default_image_processor
		A_		:    Optional[Any]						     = prepare_img()
		A_		:    Dict						     = image_processor(images=lowerCAmelCase_  ,  return_tensors="""pt"""				).to(lowerCAmelCase_				)

		# forward pass
		with torch.no_grad():
			A_		:    str						     = model(**lowerCAmelCase_				)

		# verify the logits
		A_		:    Dict						     = torch.Size((1, 1001)				)
		self.assertEqual(outputs.logits.shape  ,  lowerCAmelCase_				)

		A_		:    Tuple						     = torch.tensor([-4.1739, -1.1233, 3.1205]				).to(lowerCAmelCase_				)

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









"""simple docstring"""
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					str 			=					LEDTokenizer
	_A							:					List[Any] 			=					LEDTokenizerFast
	_A							:					Dict 			=					True





	def  lowerCamelCase(self				):
		super().setUp()
		A_		:    Dict						     = [
		    """l""",
		    """o""",
		    """w""",
		    """e""",
		    """r""",
		    """s""",
		    """t""",
		    """i""",
		    """d""",
		    """n""",
		    """\u0120""",
		    """\u0120l""",
		    """\u0120n""",
		    """\u0120lo""",
		    """\u0120low""",
		    """er""",
		    """\u0120lowest""",
		    """\u0120newer""",
		    """\u0120wider""",
		    """<unk>""",
		]
		A_		:    Any						     = dict(zip(lowerCAmelCase_  ,  range(len(lowerCAmelCase_				)				)				)				)
		A_		:    List[Any]						     = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
		A_		:    List[str]						     = {"""unk_token""": """<unk>"""}

		A_		:    List[Any]						     = os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""vocab_file"""]				)
		A_		:    Tuple						     = os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""merges_file"""]				)
		with open(self.vocab_file  ,  """w"""  ,  encoding="""utf-8"""				) as fp:
			fp.write(json.dumps(lowerCAmelCase_				) + """\n"""				)
		with open(self.merges_file  ,  """w"""  ,  encoding="""utf-8"""				) as fp:
			fp.write("""\n""".join(lowerCAmelCase_				)				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		return "lower newer", "lower newer"





	@cached_property
	def  lowerCamelCase(self				):
		return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""				)





	@cached_property
	def  lowerCamelCase(self				):
		return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		A_		:    Any						     = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = tokenizer(lowerCAmelCase_  ,  max_length=len(lowerCAmelCase_				)  ,  padding=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIsInstance(lowerCAmelCase_  ,  lowerCAmelCase_				)

			self.assertEqual((2, 9)  ,  batch.input_ids.shape				)
			self.assertEqual((2, 9)  ,  batch.attention_mask.shape				)
			A_		:    int						     = batch.input_ids.tolist()[0]
			self.assertListEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Tuple						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIn("""input_ids"""  ,  lowerCAmelCase_				)
			self.assertIn("""attention_mask"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""labels"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""decoder_attention_mask"""  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Tuple						     = [
		    """Summary of the text.""",
		    """Another summary.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = tokenizer(text_target=lowerCAmelCase_  ,  max_length=32  ,  padding="""max_length"""  ,  return_tensors="""pt"""				)
			self.assertEqual(32  ,  targets["""input_ids"""].shape[1]				)





	@require_torch
	def  lowerCamelCase(self				):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[Any]						     = tokenizer(
			    ["""I am a small frog""" * 1024, """I am a small frog"""]  ,  padding=lowerCAmelCase_  ,  truncation=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIsInstance(lowerCAmelCase_  ,  lowerCAmelCase_				)
			self.assertEqual(batch.input_ids.shape  ,  (2, 5122)				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Dict						     = ["""A long paragraph for summarization."""]
		A_		:    Any						     = [
		    """Summary of the text.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    int						     = tokenizer(lowerCAmelCase_  ,  return_tensors="""pt"""				)
			A_		:    Optional[int]						     = tokenizer(text_target=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			A_		:    str						     = inputs["""input_ids"""]
			A_		:    Tuple						     = targets["""input_ids"""]
			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)





	@require_torch
	def  lowerCamelCase(self				):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    str						     = ["""Summary of the text.""", """Another summary."""]
			A_		:    Dict						     = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

			A_		:    Optional[Any]						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_				)
			A_		:    Any						     = [[0] * len(lowerCAmelCase_				) for x in encoded_output["""input_ids"""]]
			A_		:    Tuple						     = tokenizer.pad(lowerCAmelCase_				)
			self.assertSequenceEqual(outputs["""global_attention_mask"""]  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
				A_		:    Any						     = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = self.tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = """A, <mask> AllenNLP sentence."""
				A_		:    List[Any]						     = tokenizer_r.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				A_		:    Any						     = tokenizer_p.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				self.assertEqual(sum(tokens_r["""token_type_ids"""]				)  ,  sum(tokens_p["""token_type_ids"""]				)				)
				self.assertEqual(
				    sum(tokens_r["""attention_mask"""]				) / len(tokens_r["""attention_mask"""]				)  ,  sum(tokens_p["""attention_mask"""]				) / len(tokens_p["""attention_mask"""]				)  ,  )

				A_		:    List[Any]						     = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
				A_		:    Union[str, Any]						     = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]				)
				self.assertSequenceEqual(tokens_p["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)
				self.assertSequenceEqual(tokens_r["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)

				self.assertSequenceEqual(
				    lowerCAmelCase_  ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)
				self.assertSequenceEqual(
				    lowerCAmelCase_  ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)

