






import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_a      	=  logging.getLogger(__name__)

if __name__ == "__main__":
		_a      	=  argparse.ArgumentParser(
		    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
		)
		parser.add_argument(
		    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
		)
		parser.add_argument(
		    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
		)
		parser.add_argument('''--vocab_size''', default=30522, type=int)
		_a      	=  parser.parse_args()

		logger.info(F'''Loading data from {args.data_file}''')
		with open(args.data_file, '''rb''') as fp:
				_a      	=  pickle.load(fp)

		logger.info('''Counting occurrences for MLM.''')
		_a      	=  Counter()
		for tk_ids in data:
				counter.update(tk_ids)
		_a      	=  [0] * args.vocab_size
		for k, v in counter.items():
				_a      	=  v

		logger.info(F'''Dump to {args.token_counts_dump}''')
		with open(args.token_counts_dump, '''wb''') as handle:
				pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)








from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
		from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=3       ,    UpperCAmelCase=32       ,    UpperCAmelCase=3       ,    UpperCAmelCase=10       ,    UpperCAmelCase=[10, 20, 30, 40]       ,    UpperCAmelCase=[1, 1, 2, 1]       ,    UpperCAmelCase=True       ,    UpperCAmelCase=True       ,    UpperCAmelCase="relu"       ,    UpperCAmelCase=3       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			image_size
							_UpperCAmelCase									=			num_channels
							_UpperCAmelCase									=			embeddings_size
							_UpperCAmelCase									=			hidden_sizes
							_UpperCAmelCase									=			depths
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			num_labels
							_UpperCAmelCase									=			scope
							_UpperCAmelCase									=			len(UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

							_UpperCAmelCase									=			self.get_config()

							return config, pixel_values, labels

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return ResNetConfig(
							    num_channels=self.num_channels       ,    embeddings_size=self.embeddings_size       ,    hidden_sizes=self.hidden_sizes       ,    depths=self.depths       ,    hidden_act=self.hidden_act       ,    num_labels=self.num_labels       ,    image_size=self.image_size       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetModel(config=UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase    )
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape       ,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.num_labels
							_UpperCAmelCase									=			TFResNetForImageClassification(UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    labels=UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.prepare_config_and_inputs()
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			config_and_inputs
							_UpperCAmelCase									=			{'pixel_values': pixel_values}
							return config, inputs_dict





@require_tf
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	UpperCamelCase__								=      (
	    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetModelTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase       ,    has_text_modality=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.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()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return

	@unittest.skip(reason='ResNet does not use inputs_embeds'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='ResNet does not support input and output embeddings'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

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

													_UpperCAmelCase									=			['pixel_values']
													self.assertListEqual(arg_names[:1]       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							def check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )

													_UpperCAmelCase									=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													_UpperCAmelCase									=			self.model_tester.num_stages
													self.assertEqual(len(UpperCAmelCase    )       ,    expected_num_stages + 1    )

													# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]    )       ,    [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,    )

							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			['basic', 'bottleneck']
							for model_class in self.all_model_classes:
													for layer_type in layers_type:
																			_UpperCAmelCase									=			layer_type
																			_UpperCAmelCase									=			True
																			check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

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

																			check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCAmelCase									=			TFResNetModel.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )



def __A    (      )->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
						return image





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





	"""simple docstring"""






	@cached_property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (
							    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
							    if is_vision_available()
							    else None
							)

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )

							_UpperCAmelCase									=			self.default_image_processor
							_UpperCAmelCase									=			prepare_img()
							_UpperCAmelCase									=			image_processor(images=UpperCAmelCase       ,    return_tensors='tf'    )

							# forward pass
							_UpperCAmelCase									=			model(**UpperCAmelCase    )

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

							_UpperCAmelCase									=			tf.constant([-11.10_69, -9.78_77, -8.37_77]    )

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