





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_						(					_lowerCamelCase   ):




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			self.config_class(**self.inputs_dict					)
										self.parent.assertTrue(hasattr(lowerCAmelCase_			,  'embed_dim'					)					)
										self.parent.assertTrue(hasattr(lowerCAmelCase_			,  'num_heads'					)					)





class      UpperCamelCase_						:




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=13			,  lowerCAmelCase_=64			,  lowerCAmelCase_=3			,  lowerCAmelCase_=[16, 48, 96]			,  lowerCAmelCase_=[1, 3, 6]			,  lowerCAmelCase_=[1, 2, 10]			,  lowerCAmelCase_=[7, 3, 3]			,  lowerCAmelCase_=[4, 2, 2]			,  lowerCAmelCase_=[2, 1, 1]			,  lowerCAmelCase_=[2, 2, 2]			,  lowerCAmelCase_=[False, False, True]			,  lowerCAmelCase_=[0.0, 0.0, 0.0]			,  lowerCAmelCase_=0.02			,  lowerCAmelCase_=1E-12			,  lowerCAmelCase_=True			,  lowerCAmelCase_=True			,  lowerCAmelCase_=2			,  )						->							Tuple:
										_snake_case													=			parent
										_snake_case													=			batch_size
										_snake_case													=			image_size
										_snake_case													=			patch_sizes
										_snake_case													=			patch_stride
										_snake_case													=			patch_padding
										_snake_case													=			is_training
										_snake_case													=			use_labels
										_snake_case													=			num_labels
										_snake_case													=			num_channels
										_snake_case													=			embed_dim
										_snake_case													=			num_heads
										_snake_case													=			stride_kv
										_snake_case													=			depth
										_snake_case													=			cls_token
										_snake_case													=			attention_drop_rate
										_snake_case													=			initializer_range
										_snake_case													=			layer_norm_eps




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										_snake_case													=			self.get_config()
										return config, pixel_values, labels




					def 		lowerCAmelCase     (						self					)						->							Dict:
										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 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			TFCvtModel(config=lowerCAmelCase_					)
										_snake_case													=			model(lowerCAmelCase_			,  training=lowerCAmelCase_					)
										_snake_case													=			(self.image_size, self.image_size)
										_snake_case							,       _snake_case													=			image_size[0], image_size[1]
										for i in range(len(self.depth					)					):
															_snake_case													=			floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1					)
															_snake_case													=			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 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										_snake_case													=			self.num_labels
										_snake_case													=			TFCvtForImageClassification(lowerCAmelCase_					)
										_snake_case													=			model(lowerCAmelCase_			,  labels=lowerCAmelCase_			,  training=lowerCAmelCase_					)
										self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)					)






					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			self.prepare_config_and_inputs()
										_snake_case							,       _snake_case							,       _snake_case													=			config_and_inputs
										_snake_case													=			{'pixel_values': pixel_values}
										return config, inputs_dict





@require_tf
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
					lowerCAmelCase_							=		(
					    {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		False




					def 		lowerCAmelCase     (						self					)						->							int:
										_snake_case													=			TFCvtModelTester(self					)
										_snake_case													=			TFCvtConfigTester(self			,  config_class=lowerCAmelCase_			,  has_text_modality=lowerCAmelCase_			,  hidden_size=37					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										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 		lowerCAmelCase     (						self					)						->							List[Any]:
										pass




					@unittest.skip(reason='Cvt does not use inputs_embeds'					)
					def 		lowerCAmelCase     (						self					)						->							str:
										pass




					@unittest.skip(reason='Cvt does not support input and output embeddings'					)
					def 		lowerCAmelCase     (						self					)						->							Optional[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 		lowerCAmelCase     (						self					)						->							Union[str, 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 		lowerCAmelCase     (						self					)						->							Dict:
										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 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			tf.keras.mixed_precision.Policy('mixed_float16'					)
										tf.keras.mixed_precision.set_global_policy(lowerCAmelCase_					)
										super().test_keras_fit()
										tf.keras.mixed_precision.set_global_policy('float32'					)




					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										_snake_case							,       _snake_case													=			self.model_tester.prepare_config_and_inputs_for_common()

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

															_snake_case													=			['pixel_values']
															self.assertListEqual(arg_names[:1]			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										def check_hidden_states_output(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			model_class(lowerCAmelCase_					)

															_snake_case													=			model(**self._prepare_for_class(lowerCAmelCase_			,  lowerCAmelCase_					)					)
															_snake_case													=			outputs.hidden_states

															_snake_case													=			len(self.model_tester.depth					)
															self.assertEqual(len(lowerCAmelCase_					)			,  lowerCAmelCase_					)

															# 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,
															    ]			,  )

										_snake_case							,       _snake_case													=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															_snake_case													=			True
															check_hidden_states_output(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)

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

															check_hidden_states_output(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_					)






					@slow
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_snake_case													=			TFCvtModel.from_pretrained(lowerCAmelCase_					)
															self.assertIsNotNone(lowerCAmelCase_					)





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




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




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




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Tuple:
										return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)






					@slow
					def 		lowerCAmelCase     (						self					)						->							int:
										_snake_case													=			TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)

										_snake_case													=			self.default_image_processor
										_snake_case													=			prepare_img()
										_snake_case													=			image_processor(images=lowerCAmelCase_			,  return_tensors='tf'					)

										# forward pass
										_snake_case													=			model(**lowerCAmelCase_					)

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

										_snake_case													=			tf.constant([0.92_85, 0.90_15, -0.31_50]					)
										self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,  lowerCAmelCase_			,  atol=1E-4					)					)










import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase_ 							=       logging.get_logger(__name__)
logging.set_verbosity_info()





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str  )     ->	Union[str, Any]:



					'''simple docstring'''




					if "xprophetnet" in prophetnet_checkpoint_path:
										_snake_case													=			XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )
					else:
										_snake_case													=			ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )

					_snake_case													=			['key_proj', 'value_proj', 'query_proj']

					_snake_case													=			{
					    'self_attn': 'ngram_self_attn',
					    'cross_attn': 'encoder_attn',
					    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
					    'feed_forward_layer_norm': 'final_layer_norm',
					    'feed_forward': '',
					    'intermediate': 'fc1',
					    'output': 'fc2',
					    'key_proj': 'k_proj',
					    'query_proj': 'q_proj',
					    'value_proj': 'v_proj',
					    'word_embeddings': 'embed_tokens',
					    'embeddings_layer_norm': 'emb_layer_norm',
					    'relative_pos_embeddings': 'relative_linear',
					    'ngram_embeddings': 'ngram_input_embed',
					    'position_embeddings': 'embed_positions',
					}

					for key in loading_info["missing_keys"]:
										_snake_case													=			key.split('.'  )

										if attributes[0] == "lm_head":
															_snake_case													=			prophet
															_snake_case													=			prophet_old
										else:
															_snake_case													=			prophet.prophetnet
															_snake_case													=			prophet_old.model

										_snake_case													=			False
										for attribute in attributes:
															if attribute in mapping:
																				_snake_case													=			mapping[attribute]
																				if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ) and len(UpperCamelCase__  ) > 0:
																									_snake_case													=			attribute
															elif hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																				_snake_case													=			attribute

															if attribute == "weight":
																				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																				_snake_case													=			old_model.weight
																				logger.info(F'''{attribute} is initialized.'''  )
																				_snake_case													=			True
																				break
															elif attribute == "bias":
																				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																				_snake_case													=			old_model.bias
																				logger.info(F'''{attribute} is initialized'''  )
																				_snake_case													=			True
																				break
															elif attribute in special_keys and hasattr(UpperCamelCase__    ,      'in_proj_weight'  ):
																				_snake_case													=			old_model.in_proj_weight.shape[0] // 3
																				_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )
																				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																				if attribute == "query_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[:embed_dim]  )

																				elif attribute == "key_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
																				elif attribute == "value_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
																				_snake_case													=			True
																				break
															elif attribute == "position_embeddings":
																				assert (
																				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																				), "Hidden size has to match"
																				assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																				_snake_case													=			nn.Parameter(old_model.embed_positions.weight[:512, :]  )
																				_snake_case													=			True
																				break

															if attribute.isdigit():
																				_snake_case													=			model[int(UpperCamelCase__  )]
																				_snake_case													=			old_model[int(UpperCamelCase__  )]
															else:
																				_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

																				if old_attribute == "":
																									_snake_case													=			old_model
																				else:
																									if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																														raise ValueError(F'''{old_model} does not have {old_attribute}'''  )
																									_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

										if not is_key_init:
															raise ValueError(F'''{key} was not correctly initialized!'''  )

					print(F'''Saving model to {pytorch_dump_folder_path}'''  )
					prophet.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					UpperCAmelCase_ 							=       parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



