






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					):


						'''simple docstring'''




						A     :   Tuple						=					ViTImageProcessor if is_vision_available() else None






						@property
						def       _lowerCAmelCase       (	self				) ->				str:
								return self.image_processor_tester.prepare_image_processor_dict()






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       (3, 32, 128)
								snake_case_				:							Union[str, Any]						 =       tempfile.mkdtemp()

								# fmt: off
								snake_case_				:							Dict						 =       ["[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
								snake_case_				:							Optional[int]						 =       dict(zip(_SCREAMING_SNAKE_CASE		,				range(len(_SCREAMING_SNAKE_CASE				)				)				)				)

								snake_case_				:							Any						 =       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(_SCREAMING_SNAKE_CASE				) + "\n"				)

								snake_case_				:							Dict						 =       {
								    "do_normalize": False,
								    "do_resize": True,
								    "image_processor_type": "ViTImageProcessor",
								    "resample": 3,
								    "size": {"height": 32, "width": 128},
								}
								snake_case_				:							Optional[int]						 =       os.path.join(self.tmpdirname		,				_SCREAMING_SNAKE_CASE				)
								with open(self.image_processor_file		,				"w"		,				encoding="utf-8"				) as fp:
										json.dump(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Tuple:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								shutil.rmtree(self.tmpdirname				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       np.random.randint(255		,				size=(3, 30, 400)		,				dtype=np.uinta				)

								snake_case_				:							Union[str, Any]						 =       Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE		,				0		,				-1				)				)

								return image_input






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Dict						 =       self.get_tokenizer()
								snake_case_				:							Dict						 =       self.get_image_processor()

								snake_case_				:							str						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)
								processor.save_pretrained(self.tmpdirname				)
								snake_case_				:							Optional[Any]						 =       MgpstrProcessor.from_pretrained(self.tmpdirname		,				use_fast=_SCREAMING_SNAKE_CASE				)

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

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






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Tuple						 =       self.get_tokenizer()
								snake_case_				:							int						 =       self.get_image_processor()

								snake_case_				:							str						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)
								processor.save_pretrained(self.tmpdirname				)

								snake_case_				:							Union[str, Any]						 =       self.get_tokenizer(bos_token="(BOS)"		,				eos_token="(EOS)"				)
								snake_case_				:							List[Any]						 =       self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE		,				padding_value=1.0				)

								snake_case_				:							Dict						 =       MgpstrProcessor.from_pretrained(
								    self.tmpdirname		,				bos_token="(BOS)"		,				eos_token="(EOS)"		,				do_normalize=_SCREAMING_SNAKE_CASE		,				padding_value=1.0				)

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

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






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Optional[Any]						 =       self.get_image_processor()
								snake_case_				:							Any						 =       self.get_tokenizer()

								snake_case_				:							Tuple						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       self.prepare_image_inputs()

								snake_case_				:							int						 =       image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				)
								snake_case_				:							Dict						 =       processor(images=_SCREAMING_SNAKE_CASE		,				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       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Optional[int]						 =       self.get_image_processor()
								snake_case_				:							Dict						 =       self.get_tokenizer()

								snake_case_				:							int						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       "test"

								snake_case_				:							str						 =       processor(text=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       tokenizer(_SCREAMING_SNAKE_CASE				)
								for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]		,				encoded_processor[key]				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							Dict						 =       self.get_image_processor()
								snake_case_				:							Optional[Any]						 =       self.get_tokenizer()

								snake_case_				:							Tuple						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Tuple						 =       "test"
								snake_case_				:							str						 =       self.prepare_image_inputs()

								snake_case_				:							Dict						 =       processor(text=_SCREAMING_SNAKE_CASE		,				images=_SCREAMING_SNAKE_CASE				)

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

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






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

								snake_case_				:							Dict						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								snake_case_				:							Dict						 =       processor.char_decode(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Dict						 =       [seq.replace(" "		,				""				) for seq in decoded_tok]

								self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Union[str, Any]						 =       self.get_image_processor()
								snake_case_				:							Any						 =       self.get_tokenizer()

								snake_case_				:							List[str]						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       None
								snake_case_				:							List[Any]						 =       self.prepare_image_inputs()

								snake_case_				:							str						 =       processor(text=_SCREAMING_SNAKE_CASE		,				images=_SCREAMING_SNAKE_CASE				)

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






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[Any]						 =       self.get_image_processor()
								snake_case_				:							List[str]						 =       self.get_tokenizer()

								snake_case_				:							Tuple						 =       MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       torch.randn(1		,				27		,				38				)
								snake_case_				:							int						 =       torch.randn(1		,				27		,				5_0257				)
								snake_case_				:							Tuple						 =       torch.randn(1		,				27		,				3_0522				)

								snake_case_				:							Tuple						 =       processor.batch_decode([char_input, bpe_input, wp_input]				)

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








import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase  :							Tuple			  =    '''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowercase  :							Union[str, Any]			  =    [
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def      lowerCAmelCase__	(    _a       :			Union[str, Any]  ,      _a       :			Dict  ,      _a       :			List[str]    ):
		snake_case_				:							Optional[int]						 =       SavedModel()
		snake_case_				:							Any						 =       []

		with open(os.path.join(_a  ,      "utils"  ,      "tf_ops"  ,      "onnx.json"    )    ) as f:
				snake_case_				:							int						 =       json.load(_a    )["opsets"]

		for i in range(1  ,      opset + 1    ):
				onnx_ops.extend(onnx_opsets[str(_a    )]    )

		with open(_a  ,      "rb"    ) as f:
				saved_model.ParseFromString(f.read()    )

		snake_case_				:							str						 =       set()

		# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
		for meta_graph in saved_model.meta_graphs:
				# Add operations in the graph definition
				model_op_names.update(node.op for node in meta_graph.graph_def.node    )

				# Go through the functions in the graph definition
				for func in meta_graph.graph_def.library.function:
						# Add operations in each function
						model_op_names.update(node.op for node in func.node_def    )

    # Convert to list, sorted if you want
		snake_case_				:							int						 =       sorted(_a    )
		snake_case_				:							Optional[int]						 =       []

		for op in model_op_names:
				if op not in onnx_ops and op not in INTERNAL_OPS:
						incompatible_ops.append(_a    )

		if strict and len(_a    ) > 0:
				raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops    )
		elif len(_a    ) > 0:
				print(F'''Found the following incompatible ops for the opset {opset}:'''    )
				print(*_a  ,      sep="\n"    )
		else:
				print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''    )


if __name__ == "__main__":
							lowercase  :							List[Any]			  =    argparse.ArgumentParser()
							parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
							parser.add_argument(
							    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
							)
							parser.add_argument(
							    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
							)
							parser.add_argument(
							    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
							)
							lowercase  :							int			  =    parser.parse_args()

							if args.framework == "onnx":
														onnx_compliancy(args.saved_model_path, args.strict, args.opset)
