





import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM

@require_torch
class 	A_       :
	def __init__(    self							,					_A							,					_A=9_9							,					_A=1_3							,					_A=1_6							,					_A=7							,					_A=True							,					_A=True							,					_A=True							,					_A=False							,					_A=True							,					_A=2							,					_A=3_2							,					_A=4							,					_A=4							,					_A=3_0							,					_A=0							,					_A=1							,					_A=2							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				decoder_seq_length
				# For common tests
				UpperCAmelCase   		=				self.decoder_seq_length
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_attention_mask
				UpperCAmelCase   		=				use_labels

				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				d_model
				UpperCAmelCase   		=				d_model
				UpperCAmelCase   		=				decoder_layers
				UpperCAmelCase   		=				decoder_layers
				UpperCAmelCase   		=				decoder_ffn_dim
				UpperCAmelCase   		=				decoder_attention_heads
				UpperCAmelCase   		=				decoder_attention_heads
				UpperCAmelCase   		=				eos_token_id
				UpperCAmelCase   		=				bos_token_id
				UpperCAmelCase   		=				pad_token_id
				UpperCAmelCase   		=				decoder_start_token_id
				UpperCAmelCase   		=				use_cache
				UpperCAmelCase   		=				max_position_embeddings

				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				decoder_seq_length
				UpperCAmelCase   		=				2
				UpperCAmelCase   		=				1
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.decoder_seq_length]							,					self.vocab_size		)

				UpperCAmelCase   		=				None
				if self.use_attention_mask:
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.decoder_seq_length]							,					vocab_size=2		)

				UpperCAmelCase   		=				None
				if self.use_labels:
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.decoder_seq_length]							,					self.vocab_size		)

				UpperCAmelCase   		=				TrOCRConfig(
				    vocab_size=self.vocab_size							,					d_model=self.d_model							,					decoder_layers=self.decoder_layers							,					decoder_ffn_dim=self.decoder_ffn_dim							,					decoder_attention_heads=self.decoder_attention_heads							,					eos_token_id=self.eos_token_id							,					bos_token_id=self.bos_token_id							,					use_cache=self.use_cache							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.decoder_start_token_id							,					max_position_embeddings=self.max_position_embeddings							,					)

				return (config, input_ids, attention_mask, lm_labels)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				True
				UpperCAmelCase   		=				TrOCRDecoder(config=_A		).to(_A		).eval()
				UpperCAmelCase   		=				input_ids[:2]

				input_ids[input_ids == 0] += 1
				# first forward pass
				UpperCAmelCase   		=				model(_A							,					use_cache=_A		)
				UpperCAmelCase   		=				model(_A		)
				UpperCAmelCase   		=				model(_A							,					use_cache=_A		)

				self.parent.assertTrue(len(_A		) == len(_A		)		)
				self.parent.assertTrue(len(_A		) == len(_A		) + 1		)

				UpperCAmelCase   		=				outputs['''past_key_values''']

				# create hypothetical next token and extent to next_input_ids
				UpperCAmelCase   		=				ids_tensor((2, 1)							,					config.vocab_size - 1		) + 1

				# append to next input_ids and
				UpperCAmelCase   		=				torch.cat([input_ids, next_tokens]							,					dim=-1		)

				UpperCAmelCase   		=				model(_A		)['''last_hidden_state''']
				UpperCAmelCase   		=				model(_A							,					past_key_values=_A		)['''last_hidden_state''']

				# select random slice
				UpperCAmelCase   		=				ids_tensor((1,)							,					output_from_past.shape[-1]		).item()
				UpperCAmelCase   		=				output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
				UpperCAmelCase   		=				output_from_past[:, 0, random_slice_idx].detach()

				# test that outputs are equal for slice
				assert torch.allclose(_A							,					_A							,					atol=1E-3		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.prepare_config_and_inputs()
				UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase   		=				config_and_inputs

				UpperCAmelCase   		=				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict

@require_torch
class 	A_       (a_     ,							a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
	UpperCAmelCase__					=			(TrOCRForCausalLM,) if is_torch_available() else ()
	UpperCAmelCase__					=			{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
	UpperCAmelCase__					=			True
	UpperCAmelCase__					=			False
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TrOCRStandaloneDecoderModelTester(self							,					is_training=_A		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				return







	@unittest.skip('''The model doesn\'t support left padding'''		)  # and it's not used enough to be worth fixing :)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass










import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





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







			'''simple docstring'''

			if "model" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''model.'''			,			'''''' )
			if "norm1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm1'''			,			'''attention.output.LayerNorm''' )
			if "norm2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm2'''			,			'''output.LayerNorm''' )
			if "norm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm'''			,			'''LayerNorm''' )
			if "transformer" in orig_key:
						UpperCAmelCase   		=				orig_key.split('''.''' )[0].split('''_''' )[-1]
						UpperCAmelCase   		=				orig_key.replace(F"""transformer_{layer_num}"""			,			F"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha.attn'''			,			'''attention.self''' )
			if "mha" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha'''			,			'''attention''' )
			if "W_q" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_q'''			,			'''self.query''' )
			if "W_k" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_k'''			,			'''self.key''' )
			if "W_v" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_v'''			,			'''self.value''' )
			if "ff1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff1'''			,			'''intermediate.dense''' )
			if "ff2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff2'''			,			'''output.dense''' )
			if "ff" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff'''			,			'''output.dense''' )
			if "mlm_class" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm.mlm_class'''			,			'''cls.predictions.decoder''' )
			if "mlm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm'''			,			'''cls.predictions.transform''' )
			if "cls" not in orig_key:
						UpperCAmelCase   		=				'''yoso.''' + orig_key

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			for key in orig_state_dict.copy().keys():
						UpperCAmelCase   		=				orig_state_dict.pop(UpperCamelCase__ )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									UpperCAmelCase   		=				val

			UpperCAmelCase   		=				orig_state_dict['''cls.predictions.decoder.bias''']
			UpperCAmelCase   		=				torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )['''model_state_dict''']
			UpperCAmelCase   		=				YosoConfig.from_json_file(UpperCamelCase__ )
			UpperCAmelCase   		=				YosoForMaskedLM(UpperCamelCase__ )

			UpperCAmelCase   		=				convert_checkpoint_helper(config.max_position_embeddings			,			UpperCamelCase__ )

			print(model.load_state_dict(UpperCamelCase__ ) )
			model.eval()
			model.save_pretrained(UpperCamelCase__ )

			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				__A				:    Any       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for YOSO model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



