




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a = 16	,  _a = 88	,  _a = None	,  _a = 1	,  _a = 0.0	,  _a = 32	,  _a = None	,  _a = False	,  _a = None	,  _a = None	,  _a = "geglu"	,  _a = None	,  )		->							str:
           super().__init__()
           _A							:  Union[str, Any]    =		nn.ModuleList(
               [
                   TransformeraDModel(
                       num_attention_heads=_a	,  attention_head_dim=_a	,  in_channels=_a	,  num_layers=_a	,  dropout=_a	,  norm_num_groups=_a	,  cross_attention_dim=_a	,  attention_bias=_a	,  sample_size=_a	,  num_vector_embeds=_a	,  activation_fn=_a	,  num_embeds_ada_norm=_a	,  )
                   for _ in range(2       )
               ]       )

           # Variables that can be set by a pipeline:

           # The ratio of transformer1 to transformer2's output states to be combined during inference
           _A							:  Dict    =		0.5

           # The shape of `encoder_hidden_states` is expected to be
           # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
           _A							:  Union[str, Any]    =		[77, 257]

           # Which transformer to use to encode which condition.
           # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
           _A							:  Tuple    =		[1, 0]



     def       a__			(						self	,  _a	,  _a	,  _a=None	,  _a=None	,  _a=None	,  _a = True	,  )		->							List[Any]:
           _A							:  List[Any]    =		hidden_states

           _A							:  List[str]    =		[]
           _A							:  Optional[Any]    =		0
           # attention_mask is not used yet
           for i in range(2       ):
                 # for each of the two transformers, pass the corresponding condition tokens
                 _A							:  Tuple    =		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
                 _A							:  List[str]    =		self.transformer_index_for_condition[i]
                 _A							:  int    =		self.transformers[transformer_index](
                     _a	,  encoder_hidden_states=_a	,  timestep=_a	,  cross_attention_kwargs=_a	,  return_dict=_a	,  )[0]
                 encoded_states.append(encoded_state - input_states       )
                 tokens_start += self.condition_lengths[i]

           _A							:  List[Any]    =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
           _A							:  Any    =		output_states + input_states

           if not return_dict:
                 return (output_states,)

           return TransformeraDModelOutput(sample=_a       )







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 		lowercase							(     unittest.TestCase	):
     _a											=		MODEL_FOR_MASKED_LM_MAPPING
     _a											=		TF_MODEL_FOR_MASKED_LM_MAPPING

     def       a__			(						self       )		->							Tuple:
           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       a__			(						self       )		->							Any:
           _A							:  Optional[Any]    =		pipeline(task="""fill-mask"""	,  model="""sshleifer/tiny-distilroberta-base"""	,  top_k=2	,  framework="""tf"""       )
           _A							:  Optional[int]    =		unmasker("""My name is <mask>"""       )
           self.assertEqual(
               nested_simplify(_a	,  decimals=6       )	,  [
                   {"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_8015, """token_str""": """ grouped"""},
                   {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_5506, """token_str""": """ accuser"""},
               ]	,  )

           _A							:  Tuple    =		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""": 3_8015,
                       """token_str""": """ grouped""",
                   },
                   {
                       """sequence""": """The largest city in France is accuser""",
                       """score""": 2.1e-05,
                       """token""": 2_5506,
                       """token_str""": """ accuser""",
                   },
               ]	,  )

           _A							:  List[str]    =		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""": 1_3606, """token_str""": """ Clara"""},
                   {"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 3499, """token_str""": """ Patrick"""},
                   {"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 2941, """token_str""": """ Te"""},
               ]	,  )

     @require_torch
     def       a__			(						self       )		->							str:
           _A							:  Any    =		pipeline(task="""fill-mask"""	,  model="""sshleifer/tiny-distilroberta-base"""	,  top_k=2	,  framework="""pt"""       )

           _A							:  List[Any]    =		unmasker("""My name is <mask>"""       )
           self.assertEqual(
               nested_simplify(_a	,  decimals=6       )	,  [
                   {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_5676, """token_str""": """ Maul"""},
                   {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
               ]	,  )

           _A							:  Optional[Any]    =		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""": 3_5676,
                       """token_str""": """ Maul""",
                   },
                   {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
               ]	,  )

           _A							:  Optional[int]    =		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""": 3499, """token_str""": """ Patrick"""},
                   {"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 2941, """token_str""": """ Te"""},
                   {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3606, """token_str""": """ Clara"""},
               ]	,  )

           _A							:  str    =		unmasker("""My name is <mask> <mask>"""	,  top_k=2       )

           self.assertEqual(
               nested_simplify(_a	,  decimals=6       )	,  [
                   [
                       {
                           """score""": 2.2e-05,
                           """token""": 3_5676,
                           """token_str""": """ Maul""",
                           """sequence""": """<s>My name is Maul<mask></s>""",
                       },
                       {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
                   ],
                   [
                       {
                           """score""": 2.2e-05,
                           """token""": 3_5676,
                           """token_str""": """ Maul""",
                           """sequence""": """<s>My name is<mask> Maul</s>""",
                       },
                       {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
                   ],
               ]	,  )

     @require_torch_gpu
     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  int    =		pipeline("""fill-mask"""	,  model="""hf-internal-testing/tiny-random-distilbert"""	,  device=0	,  framework="""pt"""       )

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

           _A							:  Optional[Any]    =		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       a__			(						self       )		->							Optional[int]:
           _A							:  Optional[Any]    =		pipeline(task="""fill-mask"""	,  model="""distilroberta-base"""	,  top_k=2	,  framework="""pt"""       )
           self.run_large_test(_a       )

     @slow
     @require_tf
     def       a__			(						self       )		->							Tuple:
           _A							:  str    =		pipeline(task="""fill-mask"""	,  model="""distilroberta-base"""	,  top_k=2	,  framework="""tf"""       )
           self.run_large_test(_a       )

     def       a__			(						self	,  _a       )		->							Tuple:
           _A							:  Optional[int]    =		unmasker("""My name is <mask>"""       )
           self.assertEqual(
               nested_simplify(_a       )	,  [
                   {"""sequence""": """My name is John""", """score""": 0.008, """token""": 610, """token_str""": """ John"""},
                   {"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 1573, """token_str""": """ Chris"""},
               ]	,  )
           _A							:  int    =		unmasker("""The largest city in France is <mask>"""       )
           self.assertEqual(
               nested_simplify(_a       )	,  [
                   {
                       """sequence""": """The largest city in France is Paris""",
                       """score""": 0.251,
                       """token""": 2201,
                       """token_str""": """ Paris""",
                   },
                   {
                       """sequence""": """The largest city in France is Lyon""",
                       """score""": 0.214,
                       """token""": 1_2790,
                       """token_str""": """ Lyon""",
                   },
               ]	,  )

           _A							:  Optional[Any]    =		unmasker("""My name is <mask>"""	,  targets=[""" Patrick""", """ Clara""", """ Teven"""]	,  top_k=3       )
           self.assertEqual(
               nested_simplify(_a       )	,  [
                   {"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3499, """token_str""": """ Patrick"""},
                   {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 1_3606, """token_str""": """ Clara"""},
                   {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2941, """token_str""": """ Te"""},
               ]	,  )

     @require_torch
     def       a__			(						self       )		->							Tuple:
           _A							:  List[str]    =		pipeline(task="""fill-mask"""	,  model="""sshleifer/tiny-distilroberta-base"""	,  framework="""pt"""       )
           _A							:  str    =		None
           _A							:  Union[str, Any]    =		None
           self.run_pipeline_test(_a	,  []       )

     @require_tf
     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Tuple    =		pipeline(task="""fill-mask"""	,  model="""sshleifer/tiny-distilroberta-base"""	,  framework="""tf"""       )
           _A							:  Any    =		None
           _A							:  Dict    =		None
           self.run_pipeline_test(_a	,  []       )

     def       a__			(						self	,  _a	,  _a	,  _a       )		->							Any:
           if tokenizer is None or tokenizer.mask_token_id is None:
                 self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)"""       )

           _A							:  Optional[Any]    =		FillMaskPipeline(model=_a	,  tokenizer=_a       )
           _A							:  Tuple    =		[
               F'''This is another {tokenizer.mask_token} test''',
           ]
           return fill_masker, examples

     def       a__			(						self	,  _a	,  _a       )		->							Dict:
           _A							:  Dict    =		fill_masker.tokenizer
           _A							:  List[str]    =		fill_masker.model

           _A							:  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       )},
               ]	,  )

           _A							:  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       )},
                   {"""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       )},
               ]	,  )

           _A							:  List[str]    =		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       a__			(						self	,  _a	,  _a       )		->							List[str]:
           _A							:  int    =		tokenizer.get_vocab()
           _A							:  str    =		sorted(vocab.keys()       )[:2]
           # Pipeline argument
           _A							:  Tuple    =		FillMaskPipeline(model=_a	,  tokenizer=_a	,  targets=_a       )
           _A							:  Optional[int]    =		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       )},
               ]	,  )
           _A							:  List[str]    =		{vocab[el] for el in targets}
           self.assertEqual({el["""token"""] for el in outputs}	,  _a       )
           _A							:  Union[str, Any]    =		[tokenizer.decode([x]       ) for x in target_ids]
           self.assertEqual({el["""token_str"""] for el in outputs}	,  set(_a       )       )

           # Call argument
           _A							:  str    =		FillMaskPipeline(model=_a	,  tokenizer=_a       )
           _A							:  Optional[int]    =		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       )},
               ]	,  )
           _A							:  int    =		{vocab[el] for el in targets}
           self.assertEqual({el["""token"""] for el in outputs}	,  _a       )
           _A							:  Any    =		[tokenizer.decode([x]       ) for x in target_ids]
           self.assertEqual({el["""token_str"""] for el in outputs}	,  set(_a       )       )

           # Score equivalence
           _A							:  int    =		fill_masker(F'''This is a {tokenizer.mask_token}'''	,  targets=_a       )
           _A							:  Optional[int]    =		[top_mask["""token_str"""] for top_mask in outputs]
           _A							:  Union[str, Any]    =		[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       ):
                 _A							:  Tuple    =		fill_masker(F'''This is a {tokenizer.mask_token}'''	,  targets=_a       )
                 _A							:  Union[str, Any]    =		[top_mask["""score"""] for top_mask in unmasked_targets]
                 self.assertEqual(nested_simplify(_a       )	,  nested_simplify(_a       )       )

           # Raises with invalid
           with self.assertRaises(_a       ):
                 _A							:  str    =		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       ):
                       _A							:  Optional[Any]    =		fill_masker(F'''This is a {tokenizer.mask_token}'''	,  targets=[""""""]       )
                 with self.assertRaises(_a       ):
                       _A							:  int    =		fill_masker(F'''This is a {tokenizer.mask_token}'''	,  targets=""""""       )

     def       a__			(						self	,  _a	,  _a       )		->							Optional[Any]:
           _A							:  str    =		FillMaskPipeline(model=_a	,  tokenizer=_a	,  top_k=2       )
           _A							:  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       )},
               ]	,  )

           _A							:  Union[str, Any]    =		FillMaskPipeline(model=_a	,  tokenizer=_a       )
           _A							:  Union[str, Any]    =		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       a__			(						self	,  _a	,  _a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		tokenizer.get_vocab()
           _A							:  int    =		FillMaskPipeline(model=_a	,  tokenizer=_a       )

           # top_k=2, ntargets=3
           _A							:  List[str]    =		sorted(vocab.keys()       )[:3]
           _A							:  Tuple    =		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
           _A							:  Any    =		[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       ):
                 _A							:  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       a__			(						self	,  _a	,  _a       )		->							str:
           _A							:  Optional[int]    =		FillMaskPipeline(model=_a	,  tokenizer=_a       )
           _A							:  List[Any]    =		tokenizer.get_vocab()
           # String duplicates + id duplicates
           _A							:  Optional[Any]    =		sorted(vocab.keys()       )[:3]
           _A							:  Optional[Any]    =		[targets[0], targets[1], targets[0], targets[2], targets[1]]
           _A							:  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       a__			(						self	,  _a	,  _a       )		->							Tuple:
           _A							:  Any    =		FillMaskPipeline(model=_a	,  tokenizer=_a       )

           _A							:  Optional[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       )},
                   ],
               ]	,  )

