





'''simple docstring'''

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	(       UpperCamelCase__				):
 def      UpperCAmelCase_							(				self      :Any							)->							int:
     A__				=			self.config_class(**self.inputs_dict							)
     self.parent.assertTrue(hasattr(lowercase_						,       "embed_dim"							)							)
     self.parent.assertTrue(hasattr(lowercase_						,       "num_heads"							)							)






class    UpperCAmelCase	:
 def __init__(				self      :Any						,       lowercase_      :int						,       lowercase_      :Union[str, Any]=13						,       lowercase_      :str=64						,       lowercase_      :List[str]=3						,       lowercase_      :Any=[16, 48, 96]						,       lowercase_      :int=[1, 3, 6]						,       lowercase_      :str=[1, 2, 10]						,       lowercase_      :Optional[Any]=[7, 3, 3]						,       lowercase_      :Optional[Any]=[4, 2, 2]						,       lowercase_      :Dict=[2, 1, 1]						,       lowercase_      :List[str]=[2, 2, 2]						,       lowercase_      :List[Any]=[False, False, True]						,       lowercase_      :List[Any]=[0.0, 0.0, 0.0]						,       lowercase_      :Optional[int]=0.0_2						,       lowercase_      :Optional[Any]=1E-12						,       lowercase_      :Union[str, Any]=True						,       lowercase_      :List[Any]=True						,       lowercase_      :List[str]=2						,       )->							int:
     A__				=			parent
     A__				=			batch_size
     A__				=			image_size
     A__				=			patch_sizes
     A__				=			patch_stride
     A__				=			patch_padding
     A__				=			is_training
     A__				=			use_labels
     A__				=			num_labels
     A__				=			num_channels
     A__				=			embed_dim
     A__				=			num_heads
     A__				=			stride_kv
     A__				=			depth
     A__				=			cls_token
     A__				=			attention_drop_rate
     A__				=			initializer_range
     A__				=			layer_norm_eps
 def      UpperCAmelCase_							(				self      :Any							)->							Tuple:
     A__				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

     A__				=			self.get_config()
     return config, pixel_values, labels
 def      UpperCAmelCase_							(				self      :int							)->							str:
     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      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :Dict						,       lowercase_      :List[str]							)->							Dict:
     A__				=			TFCvtModel(config=lowercase_							)
     A__				=			model(lowercase_						,       training=lowercase_							)
     A__				=			(self.image_size, self.image_size)
     A__,	A__				=			image_size[0], image_size[1]
     for i in range(len(self.depth							)							):
         A__				=			floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
         A__				=			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      UpperCAmelCase_							(				self      :int						,       lowercase_      :str						,       lowercase_      :Dict						,       lowercase_      :Optional[Any]							)->							Dict:
     A__				=			self.num_labels
     A__				=			TFCvtForImageClassification(lowercase_							)
     A__				=			model(lowercase_						,       labels=lowercase_						,       training=lowercase_							)
     self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_labels)							)



 def      UpperCAmelCase_							(				self      :str							)->							Optional[int]:
     A__				=			self.prepare_config_and_inputs()
     A__,	A__,	A__				=			config_and_inputs
     A__				=			{"pixel_values": pixel_values}
     return config, inputs_dict






@require_tf
class    UpperCAmelCase	(       UpperCamelCase__			,       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
 __lowercase   		=				(
     {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
     if is_tf_available()
     else {}
 )
 __lowercase   		=				False
 __lowercase   		=				False
 __lowercase   		=				False
 __lowercase   		=				False
 __lowercase   		=				False
 def      UpperCAmelCase_							(				self      :str							)->							List[Any]:
     A__				=			TFCvtModelTester(self							)
     A__				=			TFCvtConfigTester(self						,       config_class=lowercase_						,       has_text_modality=lowercase_						,       hidden_size=37							)
 def      UpperCAmelCase_							(				self      :int							)->							int:
     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      UpperCAmelCase_							(				self      :Tuple							)->							List[Any]:
     pass
 @unittest.skip(reason="Cvt does not use inputs_embeds"							)
 def      UpperCAmelCase_							(				self      :int							)->							Union[str, Any]:
     pass
 @unittest.skip(reason="Cvt does not support input and output embeddings"							)
 def      UpperCAmelCase_							(				self      :Any							)->							Union[str, Any]:
     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      UpperCAmelCase_							(				self      :Tuple							)->							List[Any]:
     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      UpperCAmelCase_							(				self      :str							)->							int:
     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      UpperCAmelCase_							(				self      :Union[str, Any]							)->							List[str]:
     A__				=			tf.keras.mixed_precision.Policy("mixed_float16"							)
     tf.keras.mixed_precision.set_global_policy(lowercase_							)
     super().test_keras_fit()
     tf.keras.mixed_precision.set_global_policy("float32"							)
 def      UpperCAmelCase_							(				self      :Tuple							)->							Optional[int]:
     A__,	A__				=			self.model_tester.prepare_config_and_inputs_for_common()

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

         A__				=			["pixel_values"]
         self.assertListEqual(arg_names[:1]						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :str							)->							str:
     def check_hidden_states_output(lowercase_      :str						,       lowercase_      :List[str]						,       lowercase_      :Optional[Any]							):
         A__				=			model_class(lowercase_							)

         A__				=			model(**self._prepare_for_class(lowercase_						,       lowercase_							)							)
         A__				=			outputs.hidden_states

         A__				=			len(self.model_tester.depth							)
         self.assertEqual(len(lowercase_							)						,       lowercase_							)

         # 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,
             ]						,       )

     A__,	A__				=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__				=			True
         check_hidden_states_output(lowercase_						,       lowercase_						,       lowercase_							)

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

         check_hidden_states_output(lowercase_						,       lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     A__				=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def      UpperCAmelCase_							(				self      :int							)->							int:
     A__				=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_							)



 @slow
 def      UpperCAmelCase_							(				self      :Tuple							)->							List[str]:
     for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__				=			TFCvtModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)





def 						UpperCamelCase				(							):
    A__				=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image






@require_tf
@require_vision
class    UpperCAmelCase	(       unittest.TestCase				):
 @cached_property
 def      UpperCAmelCase_							(				self      :List[str]							)->							Dict:
     return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)



 @slow
 def      UpperCAmelCase_							(				self      :Dict							)->							Union[str, Any]:
     A__				=			TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

     A__				=			self.default_image_processor
     A__				=			prepare_img()
     A__				=			image_processor(images=lowercase_						,       return_tensors="tf"							)

     # forward pass
     A__				=			model(**lowercase_							)

     # verify the logits
     A__				=			tf.TensorShape((1, 10_00)							)
     self.assertEqual(outputs.logits.shape						,       lowercase_							)

     A__				=			tf.constant([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]							)
     self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()						,       lowercase_						,       atol=1E-4							)							)










'''simple docstring'''

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCAmelCase							:	Any			=os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCAmelCase							:	Optional[int]			="    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Tuple							)->							Union[str, Any]:
     A__				=			tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir						,       "models/bert/"							)							)
     A__				=			self.transformer_dir
     shutil.copy(
         os.path.join(lowercase_						,       "src/transformers/models/bert/modeling_bert.py"							)						,       os.path.join(self.transformer_dir						,       "models/bert/modeling_bert.py"							)						,       )
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Tuple:
     A__				=			"src/transformers"
     shutil.rmtree(self.transformer_dir							)
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[str]						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :Tuple=None							)->							Optional[Any]:
     A__				=			comment + F"\nclass {class_name}(nn.Module):\n" + class_code
     if overwrite_result is not None:
         A__				=			comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
     A__				=			black.Mode(target_versions={black.TargetVersion.PYaa}						,       line_length=1_19							)
     A__				=			black.format_str(lowercase_						,       mode=lowercase_							)
     A__				=			os.path.join(self.transformer_dir						,       "new_code.py"							)
     with open(lowercase_						,       "w"						,       newline="\n"							) as f:
         f.write(lowercase_							)
     if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(lowercase_							)							) == 0							)
     else:
         check_copies.is_copy_consistent(f.name						,       overwrite=lowercase_							)
         with open(lowercase_						,       "r"							) as f:
             self.assertTrue(f.read()						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :str							)->							Optional[Any]:
     A__				=			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
     self.assertEqual(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Optional[int]:
     # Base copy consistency
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,       "BertLMPredictionHead"						,       REFERENCE_CODE + "\n"						,       )

     # With no empty line at the end
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,       "BertLMPredictionHead"						,       lowercase_						,       )

     # Copy consistency with rename
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,       "TestModelLMPredictionHead"						,       re.sub("Bert"						,       "TestModel"						,       lowercase_							)						,       )

     # Copy consistency with a really long name
     A__				=			"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
     self.check_copy_consistency(
         F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"						,       F"{long_class_name}LMPredictionHead"						,       re.sub("Bert"						,       lowercase_						,       lowercase_							)						,       )

     # Copy consistency with overwrite
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,       "TestModelLMPredictionHead"						,       lowercase_						,       overwrite_result=re.sub("Bert"						,       "TestModel"						,       lowercase_							)						,       )



 def      UpperCAmelCase_							(				self      :Dict							)->							Any:
     A__				=			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

     A__				=			(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
         " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
         " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
         " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
         " Luong, Quoc V. Le, Christopher D. Manning."
     )
     A__				=			(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     A__				=			(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
         " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
         " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
         " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
         " Christopher D. Manning 发布。\n"
     )

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

     self.assertFalse(lowercase_							)
     self.assertEqual(lowercase_						,       lowercase_							)

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

     # Check whether the number of models is equal to README.md after conversion.
     self.assertTrue(lowercase_							)

     A__				=			(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
     )
     A__				=			(
         "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
         " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     A__				=			(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

     # Check if the model link is synchronized.
     self.assertEqual(lowercase_						,       lowercase_							)



