





'''simple docstring'''

import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase : Optional[Any]											=			logging.get_logger(__name__)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = os.path.abspath(snake_case_							)
				logger.info(f'Converting TensorFlow checkpoint from {tf_path}'							)
				# Load weights from TF model
				lowercase         = tf.train.list_variables(snake_case_							)
				lowercase         = []
				lowercase         = []
				lowercase         = []
				for full_name, shape in init_vars:
								# logger.info(f"Loading TF weight {name} with shape {shape}")
								lowercase         = full_name.split("/"							)
								if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
												logger.info(f'Skipping non-model layer {full_name}'							)
												continue
								if "optimizer" in full_name:
												logger.info(f'Skipping optimization layer {full_name}'							)
												continue
								if name[0] == "model":
												# ignore initial 'model'
												lowercase         = name[1:]
								# figure out how many levels deep the name is
								lowercase         = 0
								for _name in name:
												if _name.startswith("layer_with_weights"							):
																depth += 1
												else:
																break
								layer_depth.append(snake_case_							)
								# read data
								lowercase         = tf.train.load_variable(snake_case_		,							snake_case_							)
								names.append("/".join(snake_case_							)							)
								arrays.append(snake_case_							)
				logger.info(f'Read a total of {len(snake_case_							):,} layers'							)

				# Sanity check
				if len(set(snake_case_							)							) != 1:
								raise ValueError(f'Found layer names with different depths (layer depth {list(set(snake_case_							)							)})'							)
				lowercase         = list(set(snake_case_							)							)[0]
				if layer_depth != 1:
								raise ValueError(
								    "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
								    " heads."							)

				# convert layers
				logger.info("Converting weights..."							)
				for full_name, array in zip(snake_case_		,							snake_case_							):
								lowercase         = full_name.split("/"							)
								lowercase         = model
								lowercase         = []
								for i, m_name in enumerate(snake_case_							):
												if m_name == ".ATTRIBUTES":
																# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																break
												if m_name.startswith("layer_with_weights"							):
																lowercase         = int(m_name.split("-"							)[-1]							)
																if layer_num <= 2:
																				# embedding layers
																				# layer_num 0: word_embeddings
																				# layer_num 1: position_embeddings
																				# layer_num 2: token_type_embeddings
																				continue
																elif layer_num == 3:
																				# embedding LayerNorm
																				trace.extend(["embeddings", "LayerNorm"]							)
																				lowercase         = getattr(snake_case_		,							"embeddings"							)
																				lowercase         = getattr(snake_case_		,							"LayerNorm"							)
																elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																				# encoder layers
																				trace.extend(["encoder", "layer", str(layer_num - 4							)]							)
																				lowercase         = getattr(snake_case_		,							"encoder"							)
																				lowercase         = getattr(snake_case_		,							"layer"							)
																				lowercase         = pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(["pooler", "dense"]							)
																				lowercase         = getattr(snake_case_		,							"pooler"							)
																				lowercase         = getattr(snake_case_		,							"dense"							)
												elif m_name == "embeddings":
																trace.append("embeddings"							)
																lowercase         = getattr(snake_case_		,							"embeddings"							)
																if layer_num == 0:
																				trace.append("word_embeddings"							)
																				lowercase         = getattr(snake_case_		,							"word_embeddings"							)
																elif layer_num == 1:
																				trace.append("position_embeddings"							)
																				lowercase         = getattr(snake_case_		,							"position_embeddings"							)
																elif layer_num == 2:
																				trace.append("token_type_embeddings"							)
																				lowercase         = getattr(snake_case_		,							"token_type_embeddings"							)
																else:
																				raise ValueError(f'Unknown embedding layer with name {full_name}'							)
																trace.append("weight"							)
																lowercase         = getattr(snake_case_		,							"weight"							)
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(["attention", "self"]							)
																lowercase         = getattr(snake_case_		,							"attention"							)
																lowercase         = getattr(snake_case_		,							"self"							)
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(["attention", "output", "LayerNorm"]							)
																lowercase         = getattr(snake_case_		,							"attention"							)
																lowercase         = getattr(snake_case_		,							"output"							)
																lowercase         = getattr(snake_case_		,							"LayerNorm"							)
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(["attention", "output", "dense"]							)
																lowercase         = getattr(snake_case_		,							"attention"							)
																lowercase         = getattr(snake_case_		,							"output"							)
																lowercase         = getattr(snake_case_		,							"dense"							)
												elif m_name == "_output_dense":
																# output dense
																trace.extend(["output", "dense"]							)
																lowercase         = getattr(snake_case_		,							"output"							)
																lowercase         = getattr(snake_case_		,							"dense"							)
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(["output", "LayerNorm"]							)
																lowercase         = getattr(snake_case_		,							"output"							)
																lowercase         = getattr(snake_case_		,							"LayerNorm"							)
												elif m_name == "_key_dense":
																# attention key
																trace.append("key"							)
																lowercase         = getattr(snake_case_		,							"key"							)
												elif m_name == "_query_dense":
																# attention query
																trace.append("query"							)
																lowercase         = getattr(snake_case_		,							"query"							)
												elif m_name == "_value_dense":
																# attention value
																trace.append("value"							)
																lowercase         = getattr(snake_case_		,							"value"							)
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(["intermediate", "dense"]							)
																lowercase         = getattr(snake_case_		,							"intermediate"							)
																lowercase         = getattr(snake_case_		,							"dense"							)
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append("output"							)
																lowercase         = getattr(snake_case_		,							"output"							)
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append("bias"							)
																lowercase         = getattr(snake_case_		,							"bias"							)
												elif m_name in ["kernel", "gamma"]:
																trace.append("weight"							)
																lowercase         = getattr(snake_case_		,							"weight"							)
												else:
																logger.warning(f'Ignored {m_name}'							)
        # for certain layers reshape is necessary
								lowercase         = """.""".join(snake_case_							)
								if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"		,							snake_case_							) or re.match(
								    R"(\S+)\.attention\.output\.dense\.weight"		,							snake_case_							):
												lowercase         = array.reshape(pointer.data.shape							)
								if "kernel" in full_name:
												lowercase         = array.transpose()
								if pointer.shape == array.shape:
												lowercase         = torch.from_numpy(snake_case_							)
								else:
												raise ValueError(
												    f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
												    f' {array.shape}'							)
								logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'							)
				return model







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				logger.info(f'Loading model based on config from {config_path}...'							)
				lowercase         = BertConfig.from_json_file(snake_case_							)
				lowercase         = BertModel(snake_case_							)

				# Load weights from checkpoint
				logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'							)
				load_tfa_weights_in_bert(snake_case_		,							snake_case_		,							snake_case_							)

				# Save pytorch-model
				logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'							)
				torch.save(model.state_dict()		,							snake_case_							)


if __name__ == "__main__":
			__lowerCamelCase : int											=			argparse.ArgumentParser()
			parser.add_argument(
			    "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path."
			)
			parser.add_argument(
			    "--bert_config_file",
			    type=str,
			    required=True,
			    help="The config json file corresponding to the BERT model. This specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path",
			    type=str,
			    required=True,
			    help="Path to the output PyTorch model (must include filename).",
			)
			__lowerCamelCase : Any											=			parser.parse_args()
			convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











'''simple docstring'''

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       UpperCAmelCase (						unittest.TestCase  ):
		UpperCAmelCase					:	Optional[int] 			=		ViTImageProcessor if is_vision_available() else None




		@property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = (3, 3_2, 1_2_8)
						lowercase         = tempfile.mkdtemp()

						# fmt: off
						lowercase         = ["[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
						lowercase         = dict(zip(A__							,			range(len(A__				)				)				)				)

						lowercase         = 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(A__				) + "\n"				)

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




		def 				UpperCAmelCase__   (self      :	Dict							,			**A__      :	List[Any]				)					-> Union[str, Any]:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			**A__      :	Tuple				)					-> Tuple:
						return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> int:
						shutil.rmtree(self.tmpdirname				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta				)

						lowercase         = Image.fromarray(np.moveaxis(A__							,			0							,			-1				)				)

						return image_input




		def 				UpperCAmelCase__   (self      :	str				)					-> List[Any]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)
						lowercase         = MgpstrProcessor.from_pretrained(self.tmpdirname							,			use_fast=A__				)

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

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




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)

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

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

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

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




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = self.prepare_image_inputs()

						lowercase         = image_processor(A__							,			return_tensors="np"				)
						lowercase         = processor(images=A__							,			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 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"

						lowercase         = processor(text=A__				)

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




		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

						lowercase         = processor(text=A__							,			images=A__				)

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

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




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

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

						self.assertListEqual(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = None
						lowercase         = self.prepare_image_inputs()

						lowercase         = processor(text=A__							,			images=A__				)

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





		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

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

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




