"""simple docstring"""




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__="attention"     ):





		"""simple docstring"""




		lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
		lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
		lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
		lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
		return k, o, q, v







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		if split_mlp_wi:
				lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
				lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
				lowerCAmelCase__        =   (wi_a, wi_a)
		else:
				lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

		lowerCAmelCase__        =   params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
		return wi, wo







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]







def 							_UpperCAmelCase		(       lowerCamelCase__			,  *, lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   traverse_util.flatten_dict(variables["""target"""]     )
		lowerCAmelCase__        =   {"""/""".join(lowerCamelCase__     ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		lowerCAmelCase__        =   """encoder/layers_0/mlp/wi_0/kernel""" in old
		print("""Split MLP:"""			,  lowerCamelCase__     )

		lowerCAmelCase__        =   collections.OrderedDict()

		# Shared embeddings.
		lowerCAmelCase__        =   old["""token_embedder/embedding"""]

		# Encoder.
		for i in range(lowerCamelCase__     ):
				# Block i, layer 0 (Self Attention).
				lowerCAmelCase__        =   tax_layer_norm_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """encoder"""			,  """pre_attention_layer_norm"""     )
				lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   tax_attention_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """encoder"""			,  """attention"""     )
				lowerCAmelCase__        =   layer_norm
				lowerCAmelCase__        =   k.T
				lowerCAmelCase__        =   o.T
				lowerCAmelCase__        =   q.T
				lowerCAmelCase__        =   v.T

				# Block i, layer 1 (MLP).
				lowerCAmelCase__        =   tax_layer_norm_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """encoder"""			,  """pre_mlp_layer_norm"""     )
				lowerCAmelCase__   ,					lowerCAmelCase__        =   tax_mlp_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """encoder"""			,  lowerCamelCase__     )
				lowerCAmelCase__        =   layer_norm
				if split_mlp_wi:
						lowerCAmelCase__        =   wi[0].T
						lowerCAmelCase__        =   wi[1].T
				else:
						lowerCAmelCase__        =   wi.T
				lowerCAmelCase__        =   wo.T

		lowerCAmelCase__        =   old[
		    """encoder/relpos_bias/rel_embedding"""
		].T
		lowerCAmelCase__        =   old["""encoder/encoder_norm/scale"""]

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCamelCase__     ):
						# Block i, layer 0 (Self Attention).
						lowerCAmelCase__        =   tax_layer_norm_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """decoder"""			,  """pre_self_attention_layer_norm"""     )
						lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   tax_attention_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """decoder"""			,  """self_attention"""     )
						lowerCAmelCase__        =   layer_norm
						lowerCAmelCase__        =   k.T
						lowerCAmelCase__        =   o.T
						lowerCAmelCase__        =   q.T
						lowerCAmelCase__        =   v.T

						# Block i, layer 1 (Cross Attention).
						lowerCAmelCase__        =   tax_layer_norm_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """decoder"""			,  """pre_cross_attention_layer_norm"""     )
						lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   tax_attention_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """decoder"""			,  """encoder_decoder_attention"""     )
						lowerCAmelCase__        =   layer_norm
						lowerCAmelCase__        =   k.T
						lowerCAmelCase__        =   o.T
						lowerCAmelCase__        =   q.T
						lowerCAmelCase__        =   v.T

						# Block i, layer 2 (MLP).
						lowerCAmelCase__        =   tax_layer_norm_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """decoder"""			,  """pre_mlp_layer_norm"""     )
						lowerCAmelCase__   ,					lowerCAmelCase__        =   tax_mlp_lookup(lowerCamelCase__			,  lowerCamelCase__			,  """decoder"""			,  lowerCamelCase__     )
						lowerCAmelCase__        =   layer_norm
						if split_mlp_wi:
								lowerCAmelCase__        =   wi[0].T
								lowerCAmelCase__        =   wi[1].T
						else:
								lowerCAmelCase__        =   wi.T
						lowerCAmelCase__        =   wo.T

				lowerCAmelCase__        =   old["""decoder/decoder_norm/scale"""]
				lowerCAmelCase__        =   old[
				    """decoder/relpos_bias/rel_embedding"""
				].T

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						lowerCAmelCase__        =   old["""decoder/logits_dense/kernel"""].T

		return new







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				lowerCAmelCase__        =   state_dict["""shared.weight"""]

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						lowerCAmelCase__        =   state_dict["""shared.weight"""]

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print("""Using shared word embeddings as lm_head."""     )
						lowerCAmelCase__        =   state_dict["""shared.weight"""]

		return state_dict







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   checkpoints.load_tax_checkpoint(lowerCamelCase__     )
		lowerCAmelCase__        =   convert_tax_to_pytorch(lowerCamelCase__			,  num_layers=config.num_layers			,  is_encoder_only=lowerCamelCase__     )
		lowerCAmelCase__        =   make_state_dict(lowerCamelCase__			,  lowerCamelCase__     )
		model.load_state_dict(lowerCamelCase__			,  strict=lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__ = False     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TaConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				lowerCAmelCase__        =   TaEncoderModel(lowerCamelCase__     )
		else:
				lowerCAmelCase__        =   TaForConditionalGeneration(lowerCamelCase__     )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

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

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowerCamelCase__     )
		print("""Done"""     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[Any]													=						argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
			)
			__lowerCAmelCase    :							Any													=						parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCAmelCase    :							Union[str, Any]													=						get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
			__lowerCAmelCase    :							Optional[int]													=						json.load(f)

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							return FSMTTokenizer.from_pretrained(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   FSMTForConditionalGeneration.from_pretrained(snake_case__     ).to(snake_case__     )
							if torch_device == "cuda":
									model.half()
							return model




					@parameterized.expand(
					    [
					        ["""en-ru""", 26.0],
					        ["""ru-en""", 22.0],
					        ["""en-de""", 22.0],
					        ["""de-en""", 29.0],
					    ]     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Any  ,						snake_case__						:     int     ):
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowerCAmelCase__        =   F"""facebook/wmt19-{pair}"""
							lowerCAmelCase__        =   self.get_tokenizer(snake_case__     )
							lowerCAmelCase__        =   self.get_model(snake_case__     )

							lowerCAmelCase__        =   bleu_data[pair]["""src"""]
							lowerCAmelCase__        =   bleu_data[pair]["""tgt"""]

							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__  ,						padding="""longest"""     ).to(snake_case__     )
							lowerCAmelCase__        =   model.generate(
							    input_ids=batch.input_ids  ,						num_beams=8  ,						)
							lowerCAmelCase__        =   tokenizer.batch_decode(
							    snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )
							lowerCAmelCase__        =   calculate_bleu(snake_case__  ,						snake_case__     )
							print(snake_case__     )
							self.assertGreaterEqual(scores["""bleu"""]  ,						snake_case__     )




