



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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.config_class(**self.inputs_dict						)
						self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,'embed_dim'						)						)
						self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,'num_heads'						)						)



class 	__snake_case   :
		def __init__(							self    ,a_    ,a_=13    ,a_=64    ,a_=3    ,a_=[16, 48, 96]    ,a_=[1, 3, 6]    ,a_=[1, 2, 10]    ,a_=[7, 3, 3]    ,a_=[4, 2, 2]    ,a_=[2, 1, 1]    ,a_=[2, 2, 2]    ,a_=[False, False, True]    ,a_=[0.0, 0.0, 0.0]    ,a_=0.02    ,a_=1e-1_2    ,a_=True    ,a_=True    ,a_=2    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  parent
						lowerCAmelCase__		  =  batch_size
						lowerCAmelCase__		  =  image_size
						lowerCAmelCase__		  =  patch_sizes
						lowerCAmelCase__		  =  patch_stride
						lowerCAmelCase__		  =  patch_padding
						lowerCAmelCase__		  =  is_training
						lowerCAmelCase__		  =  use_labels
						lowerCAmelCase__		  =  num_labels
						lowerCAmelCase__		  =  num_channels
						lowerCAmelCase__		  =  embed_dim
						lowerCAmelCase__		  =  num_heads
						lowerCAmelCase__		  =  stride_kv
						lowerCAmelCase__		  =  depth
						lowerCAmelCase__		  =  cls_token
						lowerCAmelCase__		  =  attention_drop_rate
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  layer_norm_eps
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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

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

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







						"""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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFCvtModel(config=SCREAMING_SNAKE_CASE__						)
						lowerCAmelCase__		  =  model(SCREAMING_SNAKE_CASE__    ,training=SCREAMING_SNAKE_CASE__						)
						lowerCAmelCase__		  =  (self.image_size, self.image_size)
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  image_size[0], image_size[1]
						for i in range(len(self.depth						)						):
										lowerCAmelCase__		  =  floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
										lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.num_labels
						lowerCAmelCase__		  =  TFCvtForImageClassification(SCREAMING_SNAKE_CASE__						)
						lowerCAmelCase__		  =  model(SCREAMING_SNAKE_CASE__    ,labels=SCREAMING_SNAKE_CASE__    ,training=SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







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



@require_tf
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
		SCREAMING_SNAKE_CASE__								=	(
		    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
		    if is_tf_available()
		    else {}
		)
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFCvtModelTester(self						)
						lowerCAmelCase__		  =  TFCvtConfigTester(self    ,config_class=SCREAMING_SNAKE_CASE__    ,has_text_modality=SCREAMING_SNAKE_CASE__    ,hidden_size=37						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		@unittest.skip(reason='Cvt does not use inputs_embeds'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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







						"""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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  tf.keras.mixed_precision.Policy('mixed_float16'						)
						tf.keras.mixed_precision.set_global_policy(SCREAMING_SNAKE_CASE__						)
						super().test_keras_fit()
						tf.keras.mixed_precision.set_global_policy('float32'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCAmelCase__		  =  ['pixel_values']
										self.assertListEqual(arg_names[:1]    ,SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""



						def check_hidden_states_output(a_    ,a_    ,a_						):
										lowerCAmelCase__		  =  model_class(SCREAMING_SNAKE_CASE__						)

										lowerCAmelCase__		  =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)						)
										lowerCAmelCase__		  =  outputs.hidden_states

										lowerCAmelCase__		  =  len(self.model_tester.depth						)
										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)    ,SCREAMING_SNAKE_CASE__						)

										# 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,
										    ]    ,)

						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(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)

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

										check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""



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







def     UpperCAmelCase_		(					)	->     str:



				"""simple docstring"""





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


@require_tf
@require_vision
class 	__snake_case   ( unittest.TestCase ):
		@cached_property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

						lowerCAmelCase__		  =  self.default_image_processor
						lowerCAmelCase__		  =  prepare_img()
						lowerCAmelCase__		  =  image_processor(images=SCREAMING_SNAKE_CASE__    ,return_tensors='tf'						)

						# forward pass
						lowerCAmelCase__		  =  model(**SCREAMING_SNAKE_CASE__						)

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

						lowerCAmelCase__		  =  tf.constant([0.9285, 0.9015, -0.3150]						)
						self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,SCREAMING_SNAKE_CASE__    ,atol=1e-4						)						)


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=1024			)       ->      Union[str, Any]:
     UpperCamelCase    ,		UpperCamelCase						    =					[], []

     UpperCamelCase						    =					list(zip(_lowercase						,     _lowercase			)			)
     UpperCamelCase    ,		UpperCamelCase						    =					sorted_examples[0]

     def is_too_big(_lowercase			):
          return tok(_lowercase						,     return_tensors='pt'			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          UpperCamelCase						    =					new_src + ' ' + src
          UpperCamelCase						    =					new_tgt + ' ' + tgt
          if is_too_big(_lowercase			) or is_too_big(_lowercase			):  # cant fit, finalize example
               finished_src.append(_lowercase			)
               finished_tgt.append(_lowercase			)
               UpperCamelCase    ,		UpperCamelCase						    =					src, tgt
          else:  # can fit, keep adding
               UpperCamelCase    ,		UpperCamelCase						    =					cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(_lowercase			)
          finished_tgt.append(_lowercase			)
     return finished_src, finished_tgt

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     UpperCamelCase						    =					Path(_lowercase			)
     save_path.mkdir(exist_ok=_lowercase			)
     for split in ["train"]:
          UpperCamelCase    ,		UpperCamelCase						    =					data_dir / F'{split}.source', data_dir / F'{split}.target'
          UpperCamelCase						    =					[x.rstrip() for x in Path(_lowercase			).open().readlines()]
          UpperCamelCase						    =					[x.rstrip() for x in Path(_lowercase			).open().readlines()]
          UpperCamelCase    ,		UpperCamelCase						    =					pack_examples(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)
          print(F'packed {split} split from {len(_lowercase			)} examples -> {len(_lowercase			)}.'			)
          Path(save_path / F'{split}.source'			).open('w'			).write('\n'.join(_lowercase			)			)
          Path(save_path / F'{split}.target'			).open('w'			).write('\n'.join(_lowercase			)			)
     for split in ["val", "test"]:
          UpperCamelCase    ,		UpperCamelCase						    =					data_dir / F'{split}.source', data_dir / F'{split}.target'
          shutil.copyfile(_lowercase						,     save_path / F'{split}.source'			)
          shutil.copyfile(_lowercase						,     save_path / F'{split}.target'			)

def  __lowerCamelCase					( )       ->      Union[str, Any]:
     UpperCamelCase						    =					argparse.ArgumentParser()
     parser.add_argument('--tok_name'						,     type=_lowercase						,     help='like facebook/bart-large-cnn,t5-base, etc.'			)
     parser.add_argument('--max_seq_len'						,     type=_lowercase						,     default=128			)
     parser.add_argument('--data_dir'						,     type=_lowercase			)
     parser.add_argument('--save_path'						,     type=_lowercase			)
     UpperCamelCase						    =					parser.parse_args()
     UpperCamelCase						    =					AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(_lowercase						,     Path(args.data_dir			)						,     args.max_seq_len						,     args.save_path			)


if __name__ == "__main__":
 packer_cli()


