import os
import sys
import unittest


lowercase_						=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase_						=       os.path.join(git_repo_path, """src""", """diffusers""")
class 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  Optional[Any]  ):
							lowercase__   					=			find_backend("    if not is_torch_available():"  )
							self.assertEqual(lowercase_,   "torch"  )

							# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
							# self.assertEqual(backend_with_underscore, "tensorflow_text")

							lowercase__   					=			find_backend("    if not (is_torch_available() and is_transformers_available()):"  )
							self.assertEqual(lowercase_,   "torch_and_transformers"  )

							# double_backend_with_underscore = find_backend(
							#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
							# )
							# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

							lowercase__   					=			find_backend(
							    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"  )
							self.assertEqual(lowercase_,   "torch_and_transformers_and_onnx"  )




				def 							A__ (  self   :  int  ):
							lowercase__   					=			read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("torch",   lowercase_  )
							self.assertIn("torch_and_transformers",   lowercase_  )
							self.assertIn("flax_and_transformers",   lowercase_  )
							self.assertIn("torch_and_transformers_and_onnx",   lowercase_  )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("UNet2DModel",   objects["torch"]  )
							self.assertIn("FlaxUNet2DConditionModel",   objects["flax"]  )
							self.assertIn("StableDiffusionPipeline",   objects["torch_and_transformers"]  )
							self.assertIn("FlaxStableDiffusionPipeline",   objects["flax_and_transformers"]  )
							self.assertIn("LMSDiscreteScheduler",   objects["torch_and_scipy"]  )
							self.assertIn("OnnxStableDiffusionPipeline",   objects["torch_and_transformers_and_onnx"]  )




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			create_dummy_object("CONSTANT",   "\'torch\'"  )
							self.assertEqual(lowercase_,   "\nCONSTANT = None\n"  )

							lowercase__   					=			create_dummy_object("function",   "\'torch\'"  )
							self.assertEqual(
							    lowercase_,   "\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n"  )

							lowercase__   					=			"\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n"
							lowercase__   					=			create_dummy_object("FakeClass",   "\'torch\'"  )
							self.assertEqual(lowercase_,   lowercase_  )




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
							lowercase__   					=			create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}  )
							self.assertEqual(dummy_files["torch"],   lowercase_  )









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							lowercase_						=       parser.parse_args()

							if args.model_type == "roberta":
														lowercase_						=       RobertaForMaskedLM.from_pretrained(args.model_name)
														lowercase_						=       """roberta"""
							elif args.model_type == "gpt2":
														lowercase_						=       GPTaLMHeadModel.from_pretrained(args.model_name)
														lowercase_						=       """transformer"""

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowercase_						=       state_dict[F'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowercase_						=       F'{prefix}.embeddings.{w}.weight'
																					lowercase_						=       state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowercase_						=       F'{prefix}.embeddings.LayerNorm.{w}'
																					lowercase_						=       state_dict[param_name]

    # Transformer Blocks #
							lowercase_						=       0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       state_dict["""lm_head.weight"""]

							print(F'N layers selected for distillation: {std_idx}')
							print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

							print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
							torch.save(compressed_sd, args.dump_checkpoint)




