




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch







@is_pipeline_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						generator("""Something there"""				)
           self.assertEqual(_a		,     [{"""generated_text""": ANY(_a				)}]				)
           # These are encoder decoder, they don't just append to incoming string
           self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""				)				)

           _A							:     List[Any]			=						generator(["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           _A							:     Optional[int]			=						generator(
               ["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     batch_size=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           with self.assertRaises(_a				):
                 generator(4				)

     @require_torch
     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""pt"""				)
           # do_sample=False necessary for reproducibility
           _A							:     Dict			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[int]			=						[
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """"""},
           ]
           self.assertEqual(_a		,     _a				)

           _A							:     Dict			=						generator("""This is a test"""		,     do_sample=_a		,     num_return_sequences=2		,     return_tensors=_a				)
           self.assertEqual(
               _a		,     [
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						generator(
               ["""This is a test""", """This is a second test"""]		,     do_sample=_a		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=_a		,     )
           self.assertEqual(
               _a		,     [
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
               ]		,     )



     @require_tf
     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)








import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     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    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      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    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



