






import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 				UpperCAmelCase_  (		__lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  CLIPTokenizer
     __lowerCamelCase      				=  CLIPTokenizerFast
     __lowerCamelCase      				=  True
     __lowerCamelCase      				=  {}
     __lowerCamelCase      				=  False




     def   __UpperCAmelCase  (				self		):
          super().setUp()

          # fmt: off
          UpperCAmelCase__      :  Dict				      =			["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
          # fmt: on
          UpperCAmelCase__      :  Union[str, Any]				      =			dict(zip(_lowerCAmelCase	,						range(len(_lowerCAmelCase		)		)		)		)
          UpperCAmelCase__      :  Optional[int]				      =			["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
          UpperCAmelCase__      :  Any				      =			{"""unk_token""": """<unk>"""}

          UpperCAmelCase__      :  int				      =			os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["""vocab_file"""]		)
          UpperCAmelCase__      :  str				      =			os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["""merges_file"""]		)
          with open(self.vocab_file	,						"""w"""	,						encoding="""utf-8"""		) as fp:
               fp.write(json.dumps(_lowerCAmelCase		) + """\n"""		)
          with open(self.merges_file	,						"""w"""	,						encoding="""utf-8"""		) as fp:
               fp.write("""\n""".join(_lowerCAmelCase		)		)




     def   __UpperCAmelCase  (				self	,						**_lowerCAmelCase		):
          kwargs.update(self.special_tokens_map		)
          return CLIPTokenizer.from_pretrained(self.tmpdirname	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						**_lowerCAmelCase		):
          kwargs.update(self.special_tokens_map		)
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			"""lower newer"""
          UpperCAmelCase__      :  str				      =			"""lower newer"""
          return input_text, output_text




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			CLIPTokenizer(self.vocab_file	,						self.merges_file	,						**self.special_tokens_map		)
          UpperCAmelCase__      :  int				      =			"""lower newer"""
          UpperCAmelCase__      :  List[str]				      =			["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
          UpperCAmelCase__      :  str				      =			tokenizer.tokenize(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)

          UpperCAmelCase__      :  List[str]				      =			tokens + [tokenizer.unk_token]
          UpperCAmelCase__      :  Dict				      =			[10, 2, 16, 9, 3, 2, 16, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase		)	,						_lowerCAmelCase		)




     @require_ftfy
     def   __UpperCAmelCase  (				self		):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    UpperCAmelCase__      :  Tuple				      =			self.tokenizer_class.from_pretrained(_lowerCAmelCase	,						**_lowerCAmelCase		)
                    UpperCAmelCase__      :  List[Any]				      =			self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase	,						**_lowerCAmelCase		)

                    UpperCAmelCase__      :  Union[str, Any]				      =			"""A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
                    UpperCAmelCase__      :  Dict				      =			tokenizer_s.tokenize(_lowerCAmelCase		)
                    UpperCAmelCase__      :  List[str]				      =			tokenizer_r.tokenize(_lowerCAmelCase		)

                    self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)

                    # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                    # with Tilde) encoded in 2 different ways
                    UpperCAmelCase__      :  Optional[int]				      =			"""xa\u0303y""" + """ """ + """x\xe3y"""
                    UpperCAmelCase__      :  int				      =			tokenizer_s.tokenize(_lowerCAmelCase		)
                    UpperCAmelCase__      :  int				      =			tokenizer_r.tokenize(_lowerCAmelCase		)

                    self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)

                    # Test that the tokenization is identical on unicode of space type
                    UpperCAmelCase__      :  Tuple				      =			[
                        """\u0009""",  # (horizontal tab, '\t')
                        """\u000B""",  # (vertical tab)
                        """\u000C""",  # (form feed)
                        """\u0020""",  # (space, ' ')
                        """\u200E""",  # (left-to-right mark):w
                        """\u200F""",  # (right-to-left mark)
                    ]
                    for unicode_seq in spaces_unicodes:
                         UpperCAmelCase__      :  Optional[Any]				      =			tokenizer_s.tokenize(_lowerCAmelCase		)
                         UpperCAmelCase__      :  List[Any]				      =			tokenizer_r.tokenize(_lowerCAmelCase		)

                         self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)

                    # Test that the tokenization is identical on unicode of line break type
                    UpperCAmelCase__      :  List[Any]				      =			[
                        """\u000A""",  # (line feed, '\n')
                        """\r\n""",  # (carriage return and line feed, '\r\n')
                        """\u000D""",  # (carriage return, '\r')
                        """\r""",  # (carriage return, '\r')
                        """\u000D""",  # (carriage return, '\r')
                        """\u2028""",  # (line separator)
                        """\u2029""",  # (paragraph separator)
                        # "\u0085", # (next line)
                    ]

                    # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                    # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                    # space (and thus into an empty list).

                    for unicode_seq in line_break_unicodes:
                         UpperCAmelCase__      :  Optional[int]				      =			tokenizer_s.tokenize(_lowerCAmelCase		)
                         UpperCAmelCase__      :  int				      =			tokenizer_r.tokenize(_lowerCAmelCase		)

                         self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    UpperCAmelCase__      :  Union[str, Any]				      =			"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                    UpperCAmelCase__      :  Dict				      =			f"{text_of_1_token} {text_of_1_token}"

                    UpperCAmelCase__      :  Dict				      =			self.rust_tokenizer_class.from_pretrained(
                        _lowerCAmelCase	,						use_fast=_lowerCAmelCase	,						)
                    UpperCAmelCase__      :  Optional[int]				      =			tokenizer_r(_lowerCAmelCase	,						return_offsets_mapping=_lowerCAmelCase	,						add_special_tokens=_lowerCAmelCase		)
                    self.assertEqual(encoding.offset_mapping[0]	,						(0, len(_lowerCAmelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]	,						(len(_lowerCAmelCase		) + 1, len(_lowerCAmelCase		) + 1 + len(_lowerCAmelCase		))	,						)

                    UpperCAmelCase__      :  int				      =			f" {text}"

                    UpperCAmelCase__      :  Optional[int]				      =			self.rust_tokenizer_class.from_pretrained(
                        _lowerCAmelCase	,						use_fast=_lowerCAmelCase	,						)
                    UpperCAmelCase__      :  Optional[Any]				      =			tokenizer_r(_lowerCAmelCase	,						return_offsets_mapping=_lowerCAmelCase	,						add_special_tokens=_lowerCAmelCase		)
                    self.assertEqual(encoding.offset_mapping[0]	,						(1, 1 + len(_lowerCAmelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]	,						(1 + len(_lowerCAmelCase		) + 1, 1 + len(_lowerCAmelCase		) + 1 + len(_lowerCAmelCase		))	,						)




     def   __UpperCAmelCase  (				self		):
          # Test related to the breaking change introduced in transformers v4.17.0
          # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
          with self.assertRaises(_lowerCAmelCase		) as context:
               self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""		)

          self.assertTrue(
              context.exception.args[0].startswith(
                  """The `backend_tokenizer` provided does not match the expected format."""		)		)




     @require_ftfy
     def   __UpperCAmelCase  (				self		):
          super().test_tokenization_python_rust_equals()




     def   __UpperCAmelCase  (				self		):
          # CLIP always lower cases letters
          pass













"""simple docstring"""





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase					      =   logging.get_logger(__name__)
logging.set_verbosity_info()



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if "xprophetnet" in prophetnet_checkpoint_path:
														__lowerCamelCase   :   Optional[Any]       =XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =XLMProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =ProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''self_attn''': '''ngram_self_attn''',
							    '''cross_attn''': '''encoder_attn''',
							    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
							    '''feed_forward_layer_norm''': '''final_layer_norm''',
							    '''feed_forward''': '''''',
							    '''intermediate''': '''fc1''',
							    '''output''': '''fc2''',
							    '''key_proj''': '''k_proj''',
							    '''query_proj''': '''q_proj''',
							    '''value_proj''': '''v_proj''',
							    '''word_embeddings''': '''embed_tokens''',
							    '''embeddings_layer_norm''': '''emb_layer_norm''',
							    '''relative_pos_embeddings''': '''relative_linear''',
							    '''ngram_embeddings''': '''ngram_input_embed''',
							    '''position_embeddings''': '''embed_positions''',
							}

							for key in loading_info["missing_keys"]:
														__lowerCamelCase   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

														__lowerCamelCase   :   Optional[Any]       =False
														for attribute in attributes:
																					if attribute in mapping:
																												__lowerCamelCase   :   Optional[Any]       =mapping[attribute]
																												if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
																																			__lowerCamelCase   :   Any       =attribute
																					elif hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   Any       =attribute

																					if attribute == "weight":
																												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																												__lowerCamelCase   :   str       =old_model.weight
																												logger.info(F'{attribute} is initialized.' )
																												__lowerCamelCase   :   Any       =True
																												break
																					elif attribute == "bias":
																												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																												__lowerCamelCase   :   Union[str, Any]       =old_model.bias
																												logger.info(F'{attribute} is initialized' )
																												__lowerCamelCase   :   str       =True
																												break
																					elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE     ,    '''in_proj_weight''' ):
																												__lowerCamelCase   :   int       =old_model.in_proj_weight.shape[0] // 3
																												__lowerCamelCase   :   Union[str, Any]       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
																												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																												if attribute == "query_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
																																			__lowerCamelCase   :   str       =nn.Parameter(old_model.in_proj_bias[:embed_dim] )

																												elif attribute == "key_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
																																			__lowerCamelCase   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   Dict       =True
																												break
																					elif attribute == "position_embeddings":
																												assert (
																												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																												), "Hidden size has to match"
																												assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

																					if attribute.isdigit():
																												__lowerCamelCase   :   List[str]       =model[int(SCREAMING_SNAKE_CASE )]
																												__lowerCamelCase   :   Optional[Any]       =old_model[int(SCREAMING_SNAKE_CASE )]
																					else:
																												__lowerCamelCase   :   int       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

																												if old_attribute == "":
																																			__lowerCamelCase   :   Dict       =old_model
																												else:
																																			if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}' )
																																			__lowerCamelCase   :   Tuple       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

														if not is_key_init:
																					raise ValueError(F'{key} was not correctly initialized!' )

							print(F'Saving model to {pytorch_dump_folder_path}' )
							prophet.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							_UpperCamelCase					      =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							_UpperCamelCase					      =   parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




