
"""simple docstring"""




import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class 						a__		( unittest.TestCase		):
   __lowerCAmelCase				=							MODEL_FOR_MASKED_LM_MAPPING
   __lowerCAmelCase				=							TF_MODEL_FOR_MASKED_LM_MAPPING
   def      __magic_name__     (     self							):
          super().tearDown()
          # clean-up as much as possible GPU memory occupied by PyTorch
          gc.collect()
          if is_torch_available():
                 import torch

                 torch.cuda.empty_cache()
   @require_tf
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				pipeline(task="fill-mask"      ,       model="sshleifer/tiny-distilroberta-base"      ,       top_k=2      ,       framework="tf"							)
          lowercase       :							int									=				unmasker("My name is <mask>"							)
          self.assertEqual(
              nested_simplify(_a      ,       decimals=6							)      ,       [
                  {"sequence": "My name is grouped", "score": 2.1E-05, "token": 38_015, "token_str": " grouped"},
                  {"sequence": "My name is accuser", "score": 2.1E-05, "token": 25_506, "token_str": " accuser"},
              ]      ,       )

          lowercase       :							List[Any]									=				unmasker("The largest city in France is <mask>"							)
          self.assertEqual(
              nested_simplify(_a      ,       decimals=6							)      ,       [
                  {
                      "sequence": "The largest city in France is grouped",
                      "score": 2.1E-05,
                      "token": 38_015,
                      "token_str": " grouped",
                  },
                  {
                      "sequence": "The largest city in France is accuser",
                      "score": 2.1E-05,
                      "token": 25_506,
                      "token_str": " accuser",
                  },
              ]      ,       )

          lowercase       :							Any									=				unmasker("My name is <mask>"      ,       targets=[" Patrick", " Clara", " Teven"]      ,       top_k=3							)
          self.assertEqual(
              nested_simplify(_a      ,       decimals=6							)      ,       [
                  {"sequence": "My name is Clara", "score": 2E-05, "token": 13_606, "token_str": " Clara"},
                  {"sequence": "My name is Patrick", "score": 2E-05, "token": 3_499, "token_str": " Patrick"},
                  {"sequence": "My name is Te", "score": 1.9E-05, "token": 2_941, "token_str": " Te"},
              ]      ,       )
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							str									=				pipeline(task="fill-mask"      ,       model="sshleifer/tiny-distilroberta-base"      ,       top_k=2      ,       framework="pt"							)

          lowercase       :							Tuple									=				unmasker("My name is <mask>"							)
          self.assertEqual(
              nested_simplify(_a      ,       decimals=6							)      ,       [
                  {"sequence": "My name is Maul", "score": 2.2E-05, "token": 35_676, "token_str": " Maul"},
                  {"sequence": "My name isELS", "score": 2.2E-05, "token": 16_416, "token_str": "ELS"},
              ]      ,       )

          lowercase       :							str									=				unmasker("The largest city in France is <mask>"							)
          self.assertEqual(
              nested_simplify(_a      ,       decimals=6							)      ,       [
                  {
                      "sequence": "The largest city in France is Maul",
                      "score": 2.2E-05,
                      "token": 35_676,
                      "token_str": " Maul",
                  },
                  {"sequence": "The largest city in France isELS", "score": 2.2E-05, "token": 16_416, "token_str": "ELS"},
              ]      ,       )

          lowercase       :							Union[str, Any]									=				unmasker("My name is <mask>"      ,       targets=[" Patrick", " Clara", " Teven"]      ,       top_k=3							)
          self.assertEqual(
              nested_simplify(_a      ,       decimals=6							)      ,       [
                  {"sequence": "My name is Patrick", "score": 2.1E-05, "token": 3_499, "token_str": " Patrick"},
                  {"sequence": "My name is Te", "score": 2E-05, "token": 2_941, "token_str": " Te"},
                  {"sequence": "My name is Clara", "score": 2E-05, "token": 13_606, "token_str": " Clara"},
              ]      ,       )

          lowercase       :							Tuple									=				unmasker("My name is <mask> <mask>"      ,       top_k=2							)

          self.assertEqual(
              nested_simplify(_a      ,       decimals=6							)      ,       [
                  [
                      {
                          "score": 2.2E-05,
                          "token": 35_676,
                          "token_str": " Maul",
                          "sequence": "<s>My name is Maul<mask></s>",
                      },
                      {"score": 2.2E-05, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
                  ],
                  [
                      {
                          "score": 2.2E-05,
                          "token": 35_676,
                          "token_str": " Maul",
                          "sequence": "<s>My name is<mask> Maul</s>",
                      },
                      {"score": 2.2E-05, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
                  ],
              ]      ,       )
   @require_torch_gpu
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				pipeline("fill-mask"      ,       model="hf-internal-testing/tiny-random-distilbert"      ,       device=0      ,       framework="pt"							)

          # convert model to fp16
          pipe.model.half()

          lowercase       :							List[str]									=				pipe("Paris is the [MASK] of France."							)
          # We actually don't care about the result, we just want to make sure
          # it works, meaning the float16 tensor got casted back to float32
          # for postprocessing.
          self.assertIsInstance(_a      ,       _a							)
   @slow
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				pipeline(task="fill-mask"      ,       model="distilroberta-base"      ,       top_k=2      ,       framework="pt"							)
          self.run_large_test(_a							)
   @slow
   @require_tf
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				pipeline(task="fill-mask"      ,       model="distilroberta-base"      ,       top_k=2      ,       framework="tf"							)
          self.run_large_test(_a							)
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Optional[Any]									=				unmasker("My name is <mask>"							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  {"sequence": "My name is John", "score": 0.0_0_8, "token": 610, "token_str": " John"},
                  {"sequence": "My name is Chris", "score": 0.0_0_7, "token": 1_573, "token_str": " Chris"},
              ]      ,       )
          lowercase       :							str									=				unmasker("The largest city in France is <mask>"							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  {
                      "sequence": "The largest city in France is Paris",
                      "score": 0.2_5_1,
                      "token": 2_201,
                      "token_str": " Paris",
                  },
                  {
                      "sequence": "The largest city in France is Lyon",
                      "score": 0.2_1_4,
                      "token": 12_790,
                      "token_str": " Lyon",
                  },
              ]      ,       )

          lowercase       :							Any									=				unmasker("My name is <mask>"      ,       targets=[" Patrick", " Clara", " Teven"]      ,       top_k=3							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  {"sequence": "My name is Patrick", "score": 0.0_0_5, "token": 3_499, "token_str": " Patrick"},
                  {"sequence": "My name is Clara", "score": 0.0_0_0, "token": 13_606, "token_str": " Clara"},
                  {"sequence": "My name is Te", "score": 0.0_0_0, "token": 2_941, "token_str": " Te"},
              ]      ,       )
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							int									=				pipeline(task="fill-mask"      ,       model="sshleifer/tiny-distilroberta-base"      ,       framework="pt"							)
          lowercase       :							int									=				None
          lowercase       :							List[Any]									=				None
          self.run_pipeline_test(_a      ,       []							)
   @require_tf
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				pipeline(task="fill-mask"      ,       model="sshleifer/tiny-distilroberta-base"      ,       framework="tf"							)
          lowercase       :							int									=				None
          lowercase       :							str									=				None
          self.run_pipeline_test(_a      ,       []							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          if tokenizer is None or tokenizer.mask_token_id is None:
                 self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"							)

          lowercase       :							Optional[int]									=				FillMaskPipeline(model=_a      ,       tokenizer=_a							)
          lowercase       :							str									=				[
              f"""This is another {tokenizer.mask_token} test""",
          ]
          return fill_masker, examples
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				fill_masker.tokenizer
          lowercase       :							Optional[Any]									=				fill_masker.model

          lowercase       :							Tuple									=				fill_masker(
              f"""This is a {tokenizer.mask_token}"""      ,       )
          self.assertEqual(
              _a      ,       [
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
              ]      ,       )

          lowercase       :							List[str]									=				fill_masker([f"""This is a {tokenizer.mask_token}"""]							)
          self.assertEqual(
              _a      ,       [
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
              ]      ,       )

          lowercase       :							Optional[Any]									=				fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""]							)
          self.assertEqual(
              _a      ,       [
                  [
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  ],
                  [
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  ],
              ]      ,       )

          with self.assertRaises(_a							):
                 fill_masker([None]							)
          # No mask_token is not supported
          with self.assertRaises(_a							):
                 fill_masker("This is"							)

          self.run_test_top_k(_a      ,       _a							)
          self.run_test_targets(_a      ,       _a							)
          self.run_test_top_k_targets(_a      ,       _a							)
          self.fill_mask_with_duplicate_targets_and_top_k(_a      ,       _a							)
          self.fill_mask_with_multiple_masks(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Tuple									=				tokenizer.get_vocab()
          lowercase       :							Optional[int]									=				sorted(vocab.keys()							)[:2]
          # Pipeline argument
          lowercase       :							List[Any]									=				FillMaskPipeline(model=_a      ,       tokenizer=_a      ,       targets=_a							)
          lowercase       :							Union[str, Any]									=				fill_masker(f"""This is a {tokenizer.mask_token}"""							)
          self.assertEqual(
              _a      ,       [
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
              ]      ,       )
          lowercase       :							List[Any]									=				{vocab[el] for el in targets}
          self.assertEqual({el["token"] for el in outputs}      ,       _a							)
          lowercase       :							int									=				[tokenizer.decode([x]							) for x in target_ids]
          self.assertEqual({el["token_str"] for el in outputs}      ,       set(_a							)							)

          # Call argument
          lowercase       :							List[Any]									=				FillMaskPipeline(model=_a      ,       tokenizer=_a							)
          lowercase       :							Any									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       targets=_a							)
          self.assertEqual(
              _a      ,       [
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
              ]      ,       )
          lowercase       :							Optional[Any]									=				{vocab[el] for el in targets}
          self.assertEqual({el["token"] for el in outputs}      ,       _a							)
          lowercase       :							List[Any]									=				[tokenizer.decode([x]							) for x in target_ids]
          self.assertEqual({el["token_str"] for el in outputs}      ,       set(_a							)							)

          # Score equivalence
          lowercase       :							Optional[Any]									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       targets=_a							)
          lowercase       :							Optional[int]									=				[top_mask["token_str"] for top_mask in outputs]
          lowercase       :							Tuple									=				[top_mask["score"] for top_mask in outputs]

          # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
          if set(_a							) == set(_a							):
                 lowercase       :							Dict									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       targets=_a							)
                 lowercase       :							int									=				[top_mask["score"] for top_mask in unmasked_targets]
                 self.assertEqual(nested_simplify(_a							)      ,       nested_simplify(_a							)							)

          # Raises with invalid
          with self.assertRaises(_a							):
                 lowercase       :							Union[str, Any]									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       targets=[]							)
          # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
          if "" not in tokenizer.get_vocab():
                 with self.assertRaises(_a							):
                        lowercase       :							str									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       targets=[""]							)
                 with self.assertRaises(_a							):
                        lowercase       :							List[str]									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       targets=""							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Any									=				FillMaskPipeline(model=_a      ,       tokenizer=_a      ,       top_k=2							)
          lowercase       :							Optional[Any]									=				fill_masker(f"""This is a {tokenizer.mask_token}"""							)
          self.assertEqual(
              _a      ,       [
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
              ]      ,       )

          lowercase       :							str									=				FillMaskPipeline(model=_a      ,       tokenizer=_a							)
          lowercase       :							str									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       top_k=2							)
          self.assertEqual(
              _a      ,       [
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
              ]      ,       )
          self.assertEqual(nested_simplify(_a							)      ,       nested_simplify(_a							)							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[int]									=				tokenizer.get_vocab()
          lowercase       :							List[str]									=				FillMaskPipeline(model=_a      ,       tokenizer=_a							)

          # top_k=2, ntargets=3
          lowercase       :							Optional[int]									=				sorted(vocab.keys()							)[:3]
          lowercase       :							Dict									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       top_k=2      ,       targets=_a							)

          # If we use the most probably targets, and filter differently, we should still
          # have the same results
          lowercase       :							Tuple									=				[el["token_str"] for el in sorted(_a      ,       key=lambda _a							: x["score"]      ,       reverse=_a							)]
          # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
          if set(_a							).issubset(_a							):
                 lowercase       :							Optional[Any]									=				fill_masker(f"""This is a {tokenizer.mask_token}"""      ,       top_k=3      ,       targets=_a							)
                 # They should yield exactly the same result
                 self.assertEqual(nested_simplify(_a							)      ,       nested_simplify(_a							)							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Any									=				FillMaskPipeline(model=_a      ,       tokenizer=_a							)
          lowercase       :							Union[str, Any]									=				tokenizer.get_vocab()
          # String duplicates + id duplicates
          lowercase       :							Tuple									=				sorted(vocab.keys()							)[:3]
          lowercase       :							int									=				[targets[0], targets[1], targets[0], targets[2], targets[1]]
          lowercase       :							Union[str, Any]									=				fill_masker(f"""My name is {tokenizer.mask_token}"""      ,       targets=_a      ,       top_k=10							)

          # The target list contains duplicates, so we can't output more
          # than them
          self.assertEqual(len(_a							)      ,       3							)

   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				FillMaskPipeline(model=_a      ,       tokenizer=_a							)

          lowercase       :							Any									=				fill_masker(
              f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""      ,       top_k=2							)
          self.assertEqual(
              _a      ,       [
                  [
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  ],
                  [
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  ],
                  [
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                      {"sequence": ANY(_a							), "score": ANY(_a							), "token": ANY(_a							), "token_str": ANY(_a							)},
                  ],
              ]      ,       )




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[Any]:
       lowercase						,       lowercase       :							Tuple									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(__snake_case	,					__snake_case	,					bias=__snake_case    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

       lowercase       :							int									=				{key.replace("decoder"	,					"model"    ): val for key, val in state_dict.items()}

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					max_position_embeddings=args.max_target_positions	,					num_layers=args.decoder_layers	,					attention_heads=args.decoder_attention_heads	,					ffn_dim=args.decoder_ffn_embed_dim	,					d_model=args.decoder_embed_dim	,					layerdrop=args.decoder_layerdrop	,					dropout=args.dropout	,					attention_dropout=args.attention_dropout	,					activation_dropout=args.activation_dropout	,					activation_function="gelu"	,					scale_embedding=not args.no_scale_embedding	,					tie_word_embeddings=args.share_decoder_input_output_embed	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   _A     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


