



'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__ :			int												=				get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')






@require_sentencepiece
@require_tokenizers
class 		_UpperCamelCase						(							lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Dict    					=	PegasusTokenizer
			_A   :	Tuple    					=	PegasusTokenizerFast
			_A   :	str    					=	True
			_A   :	Union[str, Any]    					=	True

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					super().setUp()

					# We have a SentencePiece fixture for testing
					__SCREAMING_SNAKE_CASE :	int					 =	PegasusTokenizer(lowerCAmelCase__		)
					tokenizer.save_pretrained(self.tmpdirname		)

			@cached_property
			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					return PegasusTokenizer.from_pretrained("""google/pegasus-large"""		)

			def UpperCamelCase__      (							self  :				str							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					return PegasusTokenizer.from_pretrained(self.tmpdirname							,  **lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					return ("This is a test", "This is a test")

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	"""</s>"""
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__		)							,  lowerCAmelCase__		)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__		)							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	list(self.get_tokenizer().get_vocab().keys()		)

					self.assertEqual(vocab_keys[0]							,  """<pad>"""		)
					self.assertEqual(vocab_keys[1]							,  """</s>"""		)
					self.assertEqual(vocab_keys[-1]							,  """v"""		)
					self.assertEqual(len(lowerCAmelCase__		)							,  1_1_0_3		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,  1_1_0_3		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.tokenizer_class.from_pretrained(self.tmpdirname		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	(
					    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
					    """ </s> <pad> <pad> <pad>"""
					)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	rust_tokenizer([raw_input_str]							,  return_tensors=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__		).input_ids[0]
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	py_tokenizer([raw_input_str]							,  return_tensors=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__		).input_ids[0]
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self._large_tokenizer
					# <mask_1> masks whole sentence while <mask_2> masks single word
					__SCREAMING_SNAKE_CASE :	Tuple					 =	"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
					__SCREAMING_SNAKE_CASE :	int					 =	[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer([raw_input_str]							,  return_tensors=lowerCAmelCase__		).input_ids[0]
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self._large_tokenizer
					# The tracebacks for the following asserts are **better** without messages or self.assertEqual
					assert tokenizer.vocab_size == 9_6_1_0_3
					assert tokenizer.pad_token_id == 0
					assert tokenizer.eos_token_id == 1
					assert tokenizer.offset == 1_0_3
					assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
					assert tokenizer.unk_token == "<unk>"
					assert tokenizer.model_max_length == 1_0_2_4
					__SCREAMING_SNAKE_CASE :	Tuple					 =	"""To ensure a smooth flow of bank resolutions."""
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	tokenizer([raw_input_str]							,  return_tensors=lowerCAmelCase__		).input_ids[0]
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)
					assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

			@require_torch
			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	["""This is going to be way too long.""" * 1_5_0, """short example"""]
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	["""not super long but more than 5 tokens""", """tiny"""]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self._large_tokenizer(lowerCAmelCase__							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  return_tensors="""pt"""		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self._large_tokenizer(
					    text_target=lowerCAmelCase__							,  max_length=5							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  return_tensors="""pt"""		)

					assert batch.input_ids.shape == (2, 1_0_2_4)
					assert batch.attention_mask.shape == (2, 1_0_2_4)
					assert targets["input_ids"].shape == (2, 5)
					assert len(lowerCAmelCase__		) == 2  # input_ids, attention_mask.



			@slow
			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	{"""input_ids""": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=lowerCAmelCase__							,  model_name="""google/bigbird-pegasus-large-arxiv"""							,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""							,  )






@require_sentencepiece
@require_tokenizers
class 		_UpperCamelCase						(							lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	PegasusTokenizer
			_A   :	Dict    					=	PegasusTokenizerFast
			_A   :	Dict    					=	True
			_A   :	Any    					=	True

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					super().setUp()

					# We have a SentencePiece fixture for testing
					__SCREAMING_SNAKE_CASE :	Tuple					 =	PegasusTokenizer(lowerCAmelCase__							,  offset=0							,  mask_token_sent=lowerCAmelCase__							,  mask_token="""[MASK]"""		)
					tokenizer.save_pretrained(self.tmpdirname		)

			@cached_property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""		)

			def UpperCamelCase__      (							self  :				List[Any]							,  **lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					return PegasusTokenizer.from_pretrained(self.tmpdirname							,  **lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					return ("This is a test", "This is a test")

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.tokenizer_class.from_pretrained(self.tmpdirname		)
					__SCREAMING_SNAKE_CASE :	str					 =	(
					    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
					    """ <pad> <pad> <pad>"""
					)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	rust_tokenizer([raw_input_str]							,  return_tensors=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__		).input_ids[0]
					__SCREAMING_SNAKE_CASE :	int					 =	py_tokenizer([raw_input_str]							,  return_tensors=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__		).input_ids[0]
					self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__		)

			@require_torch
			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	["""This is going to be way too long.""" * 1_0_0_0, """short example"""]
					__SCREAMING_SNAKE_CASE :	int					 =	["""not super long but more than 5 tokens""", """tiny"""]
					__SCREAMING_SNAKE_CASE :	int					 =	self._large_tokenizer(lowerCAmelCase__							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  return_tensors="""pt"""		)
					__SCREAMING_SNAKE_CASE :	str					 =	self._large_tokenizer(
					    text_target=lowerCAmelCase__							,  max_length=5							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  return_tensors="""pt"""		)

					assert batch.input_ids.shape == (2, 4_0_9_6)
					assert batch.attention_mask.shape == (2, 4_0_9_6)
					assert targets["input_ids"].shape == (2, 5)
					assert len(lowerCAmelCase__		) == 2  # input_ids, attention_mask.



			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	(
					    """This is an example string that is used to test the original TF implementation against the HF"""
					    """ implementation"""
					)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self._large_tokenizer(lowerCAmelCase__		).input_ids

					self.assertListEqual(
					    lowerCAmelCase__							,  [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]							,  )




'''simple docstring'''




import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if hor == 1_28:
				__SCREAMING_SNAKE_CASE :	Any					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(32, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	str					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

		elif hor == 32:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	str					 =	(32, 64, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	Tuple					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"      )
		__SCREAMING_SNAKE_CASE :	Any					 =	model.state_dict()
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{
		    """down_block_types""": down_block_types,
		    """block_out_channels""": block_out_channels,
		    """up_block_types""": up_block_types,
		    """layers_per_block""": 1,
		    """use_timestep_embedding""": True,
		    """out_block_type""": """OutConv1DBlock""",
		    """norm_num_groups""": 8,
		    """downsample_each_block""": False,
		    """in_channels""": 14,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """sample_size""": 6_55_36,
		    """mid_block_type""": """MidResTemporalBlock1D""",
		    """act_fn""": """mish""",
		}
		__SCREAMING_SNAKE_CASE :	int					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dict(zip(model.state_dict().keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	state_dict.pop(_lowerCamelCase      )
		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   F"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"      )
		with open(F"hub/hopper-medium-v2/unet/hor{hor}/config.json" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	{
		    """in_channels""": 14,
		    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
		    """up_block_types""": (),
		    """out_block_type""": """ValueFunction""",
		    """mid_block_type""": """ValueFunctionMidBlock1D""",
		    """block_out_channels""": (32, 64, 1_28, 2_56),
		    """layers_per_block""": 1,
		    """downsample_each_block""": True,
		    """sample_size""": 6_55_36,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """use_timestep_embedding""": True,
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """norm_num_groups""": 8,
		    """act_fn""": """mish""",
		}

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )

		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	str					 =	state_dict.pop(_lowerCamelCase      )

		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""      )
		with open("""hub/hopper-medium-v2/value_function/config.json""" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


if __name__ == "__main__":
						unet(32)
						# unet(128)
						value_function()