


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 	__lowercase					(	unittest.TestCase	):
      UpperCamelCase										=							ViTImageProcessor if is_vision_available() else None





      @property
      def 							_lowercase					(		self    :       str							)     -> int:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def 							_lowercase					(		self    :       Dict							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  (3, 3_2, 1_2_8)
         UpperCAmelCase      					=  tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase      					=  ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
         # fmt: on
         UpperCAmelCase      					=  dict(zip(__lowerCamelCase       ,							range(len(__lowerCamelCase							)							)							)							)

         UpperCAmelCase      					=  os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""vocab_file"""]							)
         with open(self.vocab_file       ,							"""w"""       ,							encoding="""utf-8"""							) as fp:
            fp.write(json.dumps(__lowerCamelCase							) + """\n"""							)

         UpperCAmelCase      					=  {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase      					=  os.path.join(self.tmpdirname       ,							__lowerCamelCase							)
         with open(self.image_processor_file       ,							"""w"""       ,							encoding="""utf-8"""							) as fp:
            json.dump(__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str       ,							**__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







         return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,							**__lowerCamelCase							)





      def 							_lowercase					(		self    :       Union[str, Any]       ,							**__lowerCamelCase    :       Optional[Any]							)     -> Dict:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained(self.tmpdirname       ,							**__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[int]:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname							)





      def 							_lowercase					(		self    :       str							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  np.random.randint(2_5_5       ,							size=(3, 3_0, 4_0_0)       ,							dtype=np.uinta							)

         UpperCAmelCase      					=  Image.fromarray(np.moveaxis(__lowerCamelCase       ,							0       ,							-1							)							)

         return image_input





      def 							_lowercase					(		self    :       List[Any]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_tokenizer()
         UpperCAmelCase      					=  self.get_image_processor()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)
         UpperCAmelCase      					=  MgpstrProcessor.from_pretrained(self.tmpdirname       ,							use_fast=__lowerCamelCase							)

         self.assertEqual(processor.char_tokenizer.get_vocab()       ,							tokenizer.get_vocab()							)
         self.assertIsInstance(processor.char_tokenizer       ,							__lowerCamelCase							)

         self.assertEqual(processor.image_processor.to_json_string()       ,							image_processor.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Tuple							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_tokenizer()
         UpperCAmelCase      					=  self.get_image_processor()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

         UpperCAmelCase      					=  self.get_tokenizer(bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""							)
         UpperCAmelCase      					=  self.get_image_processor(do_normalize=__lowerCamelCase       ,							padding_value=1.0							)

         UpperCAmelCase      					=  MgpstrProcessor.from_pretrained(
             self.tmpdirname       ,							bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""       ,							do_normalize=__lowerCamelCase       ,							padding_value=1.0							)

         self.assertEqual(processor.char_tokenizer.get_vocab()       ,							tokenizer_add_kwargs.get_vocab()							)
         self.assertIsInstance(processor.char_tokenizer       ,							__lowerCamelCase							)

         self.assertEqual(processor.image_processor.to_json_string()       ,							image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Tuple							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  self.prepare_image_inputs()

         UpperCAmelCase      					=  image_processor(__lowerCamelCase       ,							return_tensors="""np"""							)
         UpperCAmelCase      					=  processor(images=__lowerCamelCase       ,							return_tensors="""np"""							)

         for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum()       ,							input_processor[key].sum()       ,							delta=1e-2							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  """test"""

         UpperCAmelCase      					=  processor(text=__lowerCamelCase							)

         UpperCAmelCase      					=  tokenizer(__lowerCamelCase							)
         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]       ,							encoded_processor[key]							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  """test"""
         UpperCAmelCase      					=  self.prepare_image_inputs()

         UpperCAmelCase      					=  processor(text=__lowerCamelCase       ,							images=__lowerCamelCase							)

         self.assertListEqual(list(inputs.keys()							)       ,							["""pixel_values""", """labels"""]							)

         # test if it raises when no input is passed
         with pytest.raises(__lowerCamelCase							):
            processor()





      def 							_lowercase					(		self    :       str							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase      					=  processor.char_decode(__lowerCamelCase							)
         UpperCAmelCase      					=  tokenizer.batch_decode(__lowerCamelCase							)
         UpperCAmelCase      					=  [seq.replace(""" """       ,							""""""							) for seq in decoded_tok]

         self.assertListEqual(__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  None
         UpperCAmelCase      					=  self.prepare_image_inputs()

         UpperCAmelCase      					=  processor(text=__lowerCamelCase       ,							images=__lowerCamelCase							)

         self.assertListEqual(list(inputs.keys()							)       ,							processor.model_input_names							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  MgpstrProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  torch.randn(1       ,							2_7       ,							3_8							)
         UpperCAmelCase      					=  torch.randn(1       ,							2_7       ,							5_0_2_5_7							)
         UpperCAmelCase      					=  torch.randn(1       ,							2_7       ,							3_0_5_2_2							)

         UpperCAmelCase      					=  processor.batch_decode([char_input, bpe_input, wp_input]							)

         self.assertListEqual(list(results.keys()							)       ,							["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]							)







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 	__lowercase					:





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[int]=3       ,							__lowerCamelCase    :       Union[str, Any]=3_2       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       Dict=1_0       ,							__lowerCamelCase    :       Optional[int]=[1_0, 2_0, 3_0, 4_0]       ,							__lowerCamelCase    :       Optional[int]=[1, 1, 2, 1]       ,							__lowerCamelCase    :       int=True       ,							__lowerCamelCase    :       str=True       ,							__lowerCamelCase    :       str="relu"       ,							__lowerCamelCase    :       str=3       ,							__lowerCamelCase    :       int=None       ,							)     -> Dict:






         """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(__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """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 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """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 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Tuple							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModel(config=__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         # 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 // 3_2, self.image_size // 3_2)       ,							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       int							)     -> Tuple:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Optional[int]							)     -> Tuple:






         """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 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      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 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """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 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         return





      @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         pass





      @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Tuple:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            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]       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       List[Any]							)     -> List[str]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       str       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)

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

            UpperCAmelCase      					=  self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase							)       ,							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(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

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

               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> int:






         """simple docstring"""







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





      @slow
      def 							_lowercase					(		self    :       Tuple							)     -> List[str]:






         """simple docstring"""







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

def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





      @cached_property
      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







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





      @slow
      def 							_lowercase					(		self    :       Dict							)     -> str:






         """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=__lowerCamelCase       ,							return_tensors="""tf"""							)

         # forward pass
         UpperCAmelCase      					=  model(**__lowerCamelCase							)

         # verify the logits
         UpperCAmelCase      					=  tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  tf.constant([-11.1_069, -9.7_877, -8.3_777]							)

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



