

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     DataaVecVisionForImageClassification,
     DataaVecVisionForSemanticSegmentation,
     DataaVecVisionModel,
 )
 from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor






class 		__magic_name__ :






     def __init__(							self						,		_lowercase						,		_lowercase=100						,		_lowercase=13						,		_lowercase=30						,		_lowercase=2						,		_lowercase=3						,		_lowercase=True						,		_lowercase=True						,		_lowercase=32						,		_lowercase=4						,		_lowercase=4						,		_lowercase=37						,		_lowercase="gelu"						,		_lowercase=0.1						,		_lowercase=0.1						,		_lowercase=10						,		_lowercase=0.02						,		_lowercase=3						,		_lowercase=None						,		_lowercase=[0, 1, 2, 3]						,		)->			str:
         UpperCamelCase_          =		parent
         UpperCamelCase_          =		100
         UpperCamelCase_          =		batch_size
         UpperCamelCase_          =		image_size
         UpperCamelCase_          =		patch_size
         UpperCamelCase_          =		num_channels
         UpperCamelCase_          =		is_training
         UpperCamelCase_          =		use_labels
         UpperCamelCase_          =		hidden_size
         UpperCamelCase_          =		num_hidden_layers
         UpperCamelCase_          =		num_attention_heads
         UpperCamelCase_          =		intermediate_size
         UpperCamelCase_          =		hidden_act
         UpperCamelCase_          =		hidden_dropout_prob
         UpperCamelCase_          =		attention_probs_dropout_prob
         UpperCamelCase_          =		type_sequence_label_size
         UpperCamelCase_          =		initializer_range
         UpperCamelCase_          =		scope
         UpperCamelCase_          =		out_indices
         UpperCamelCase_          =		num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCamelCase_          =		(image_size // patch_size) ** 2
         UpperCamelCase_          =		num_patches + 1






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

         UpperCamelCase_          =		None
         UpperCamelCase_          =		None
         if self.use_labels:
             UpperCamelCase_          =		ids_tensor([self.batch_size]						,		self.type_sequence_label_size     )
             UpperCamelCase_          =		ids_tensor([self.batch_size, self.image_size, self.image_size]						,		self.num_labels     )

         UpperCamelCase_          =		self.get_config()

         return config, pixel_values, labels, pixel_labels






     def      UpperCAmelCase_							(							self     )->			str:
         return DataaVecVisionConfig(
             vocab_size=self.vocab_size						,		image_size=self.image_size						,		patch_size=self.patch_size						,		num_channels=self.num_channels						,		hidden_size=self.hidden_size						,		num_hidden_layers=self.num_hidden_layers						,		num_attention_heads=self.num_attention_heads						,		intermediate_size=self.intermediate_size						,		hidden_act=self.hidden_act						,		hidden_dropout_prob=self.hidden_dropout_prob						,		attention_probs_dropout_prob=self.attention_probs_dropout_prob						,		is_decoder=_lowercase						,		initializer_range=self.initializer_range						,		out_indices=self.out_indices						,		)






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			Any:
         UpperCamelCase_          =		DataaVecVisionModel(config=_lowercase     )
         model.to(_lowercase     )
         model.eval()
         UpperCamelCase_          =		model(_lowercase     )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCamelCase_          =		(self.image_size // self.patch_size) ** 2
         self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, num_patches + 1, self.hidden_size)     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		self.type_sequence_label_size
         UpperCamelCase_          =		DataaVecVisionForImageClassification(_lowercase     )
         model.to(_lowercase     )
         model.eval()
         UpperCamelCase_          =		model(_lowercase						,		labels=_lowercase     )
         self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.type_sequence_label_size)     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			str:
         UpperCamelCase_          =		self.num_labels
         UpperCamelCase_          =		DataaVecVisionForSemanticSegmentation(_lowercase     )
         model.to(_lowercase     )
         model.eval()
         UpperCamelCase_          =		model(_lowercase     )
         self.parent.assertEqual(
             result.logits.shape						,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )
         UpperCamelCase_          =		model(_lowercase						,		labels=_lowercase     )
         self.parent.assertEqual(
             result.logits.shape						,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )






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






@require_torch
class 		__magic_name__ (				snake_case       ,	snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :Optional[Any]									=	(
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     UpperCamelCase_     :Any									=	(
         {
             """feature-extraction""": DataaVecVisionModel,
             """image-classification""": DataaVecVisionForImageClassification,
             """image-segmentation""": DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     UpperCamelCase_     :int									=	False
     UpperCamelCase_     :List[str]									=	False
     UpperCamelCase_     :Optional[Any]									=	False






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		DataaVecVisionModelTester(self     )
         UpperCamelCase_          =		ConfigTester(
             self						,		config_class=_lowercase						,		has_text_modality=_lowercase						,		hidden_size=37     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         self.config_tester.run_common_tests()






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         # Data2VecVision does not use inputs_embeds
         pass






     @require_torch_multi_gpu
     @unittest.skip(
         reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"     )
     def      UpperCAmelCase_							(							self     )->			Dict:
         pass






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(_lowercase     )
             self.assertIsInstance(model.get_input_embeddings()						,		(nn.Module)     )
             UpperCamelCase_          =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_lowercase						,		nn.Linear     )     )






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase_          =		["pixel_values"]
             self.assertListEqual(arg_names[:1]						,		_lowercase     )






     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Dict:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         if not self.model_tester.is_training:
             return

         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_          =		True

         for model_class in self.all_model_classes:
             if model_class in [*get_values(_lowercase     )]:
                 continue

             UpperCamelCase_          =		model_class(_lowercase     )
             model.to(_lowercase     )
             model.train()
             UpperCamelCase_          =		self._prepare_for_class(_lowercase						,		_lowercase						,		return_labels=_lowercase     )
             UpperCamelCase_          =		model(**_lowercase     ).loss
             loss.backward()






     def      UpperCAmelCase_							(							self     )->			Dict:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return

         UpperCamelCase_          =		False
         UpperCamelCase_          =		True

         for model_class in self.all_model_classes:
             if model_class in [*get_values(_lowercase     )] or not model_class.supports_gradient_checkpointing:
                 continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                 UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		inputs_dict["pixel_values"].shape
                 UpperCamelCase_          =		torch.zeros(
                     [self.model_tester.batch_size, height, width]						,		device=_lowercase     ).long()
             UpperCamelCase_          =		model_class(_lowercase     )
             model.gradient_checkpointing_enable()
             model.to(_lowercase     )
             model.train()
             UpperCamelCase_          =		self._prepare_for_class(_lowercase						,		_lowercase						,		return_labels=_lowercase     )
             UpperCamelCase_          =		model(**_lowercase     ).loss
             loss.backward()






     def      UpperCAmelCase_							(							self     )->			Dict:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         UpperCamelCase_          =		_config_zero_init(_lowercase     )
         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(config=_lowercase     )
             for name, param in model.named_parameters():
                 # we skip lambda parameters as these require special initial values
                 # determined by config.layer_scale_init_value
                 if "lambda" in name:
                     continue
                 if param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()						,		[0.0, 1.0]						,		msg=F"Parameter {name} of model {model_class} seems not properly initialized"						,		)






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase=2e-4						,		_lowercase="outputs"						,		_lowercase=None     )->			List[Any]:
         # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
         super().check_pt_tf_outputs(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowercase     )






     @slow
     def      UpperCAmelCase_							(							self     )->			Any:
         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_          =		DataaVecVisionModel.from_pretrained(_lowercase     )
             self.assertIsNotNone(_lowercase     )




def        lowerCAmelCase(	)->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image






@require_torch
@require_vision
class 		__magic_name__ (				unittest.TestCase							):






     @cached_property
     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         return (
             BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"     ) if is_vision_available() else None
         )






     @slow
     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"     ).to(
             _lowercase     )

         UpperCamelCase_          =		self.default_image_processor
         UpperCamelCase_          =		prepare_img()
         UpperCamelCase_          =		image_processor(images=_lowercase						,		return_tensors="pt"     ).to(_lowercase     )

         # forward pass
         with torch.no_grad():
             UpperCamelCase_          =		model(**_lowercase     )
         UpperCamelCase_          =		outputs.logits

         # verify the logits
         UpperCamelCase_          =		torch.Size((1, 1_000)     )
         self.assertEqual(logits.shape						,		_lowercase     )

         UpperCamelCase_          =		torch.tensor([0.3_277, -0.1_395, 0.0_911]     ).to(_lowercase     )

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

         UpperCamelCase_          =		[model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
         self.assertEqual(logits[0].topk(2     ).indices.cpu().tolist()						,		_lowercase     )



import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoConfig(
        hidden_size=config_json['n_embd']       ,      num_layers=config_json['n_layer']       ,      num_heads=config_json['n_head']       ,      attention_types=config_json['attention_types']       ,      max_position_embeddings=config_json['n_positions']       ,      resid_dropout=config_json['res_dropout']       ,      embed_dropout=config_json['embed_dropout']       ,      attention_dropout=config_json['attn_dropout']       ,      )
    print(f"""Building PyTorch model from configuration: {config}"""				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoForCausalLM(_SCREAMING_SNAKE_CASE				)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
    model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
       UpperCamelCase__     :					Tuple								=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained mesh-tf model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


