import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class      __lowercase					:

      def __init__(	self				:      int							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[Any]=99							,							__lowerCamelCase				:      int=13							,							__lowerCamelCase				:      Optional[Any]=7							,							__lowerCamelCase				:      Optional[int]=9							,							__lowerCamelCase				:      List[Any]=True							,							__lowerCamelCase				:      Union[str, Any]=True							,							__lowerCamelCase				:      int=False							,							__lowerCamelCase				:      Optional[int]=32							,							__lowerCamelCase				:      Union[str, Any]=5							,							__lowerCamelCase				:      Tuple=4							,							__lowerCamelCase				:      int=37							,							__lowerCamelCase				:      Any=8							,							__lowerCamelCase				:      List[str]=0.1							,							__lowerCamelCase				:      Tuple=0.002							,							__lowerCamelCase				:      Any=1							,							__lowerCamelCase				:      List[Any]=0							,							__lowerCamelCase				:      List[str]=0							,							__lowerCamelCase				:      int=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		List[str]:


         '''simple docstring'''

         lowercase							=		parent
         lowercase							=		batch_size
         lowercase							=		encoder_seq_length
         lowercase							=		decoder_seq_length
         # For common tests
         lowercase							=		self.decoder_seq_length
         lowercase							=		is_training
         lowercase							=		use_attention_mask
         lowercase							=		use_labels
         lowercase							=		vocab_size
         lowercase							=		hidden_size
         lowercase							=		num_hidden_layers
         lowercase							=		num_attention_heads
         lowercase							=		d_ff
         lowercase							=		relative_attention_num_buckets
         lowercase							=		dropout_rate
         lowercase							=		initializer_factor
         lowercase							=		eos_token_id
         lowercase							=		pad_token_id
         lowercase							=		decoder_start_token_id
         lowercase							=		None
         lowercase							=		decoder_layers

      def        __a		(	self				:      int   )							->		List[Any]:


         '''simple docstring'''

         return TaConfig.from_pretrained('''google/umt5-base'''   )

      def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      Tuple=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      Optional[int]=None							,							)							->		str:


         '''simple docstring'''

         if attention_mask is None:
            lowercase							=		input_ids.ne(config.pad_token_id   )
         if decoder_attention_mask is None:
            lowercase							=		decoder_input_ids.ne(config.pad_token_id   )
         if head_mask is None:
            lowercase							=		torch.ones(config.num_hidden_layers							,							config.num_attention_heads							,							device=lowerCAmelCase__   )
         if decoder_head_mask is None:
            lowercase							=		torch.ones(config.num_decoder_layers							,							config.num_attention_heads							,							device=lowerCAmelCase__   )
         if cross_attn_head_mask is None:
            lowercase							=		torch.ones(
                config.num_decoder_layers							,							config.num_attention_heads							,							device=lowerCAmelCase__   )
         return {
             "input_ids": input_ids,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "decoder_attention_mask": decoder_attention_mask,
             "head_mask": head_mask,
             "decoder_head_mask": decoder_head_mask,
             "cross_attn_head_mask": cross_attn_head_mask,
         }

      def        __a		(	self				:      Dict   )							->		Any:


         '''simple docstring'''

         lowercase							=		ids_tensor([self.batch_size, self.encoder_seq_length]							,							self.vocab_size   )
         lowercase							=		ids_tensor([self.batch_size, self.decoder_seq_length]							,							self.vocab_size   )

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for NllbMoe the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         lowercase							=		input_ids.clamp(self.pad_token_id + 1   )
         lowercase							=		decoder_input_ids.clamp(self.pad_token_id + 1   )

         lowercase							=		self.get_config()
         lowercase							=		config.num_attention_heads
         lowercase							=		self.prepare_inputs_dict(lowerCAmelCase__							,							lowerCAmelCase__							,							lowerCAmelCase__   )
         return config, input_dict

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


         '''simple docstring'''

         lowercase		,lowercase							=		self.prepare_config_and_inputs()
         return config, inputs_dict

      def        __a		(	self				:      Dict   )							->		List[str]:


         '''simple docstring'''

         return TaConfig(
             vocab_size=1_66							,							d_model=self.hidden_size							,							d_ff=self.d_ff							,							d_kv=self.hidden_size // self.num_attention_heads							,							num_layers=self.num_hidden_layers							,							num_decoder_layers=self.decoder_layers							,							num_heads=self.num_attention_heads							,							relative_attention_num_buckets=self.relative_attention_num_buckets							,							dropout_rate=self.dropout_rate							,							initializer_factor=self.initializer_factor							,							eos_token_id=self.eos_token_id							,							bos_token_id=self.pad_token_id							,							pad_token_id=self.pad_token_id							,							decoder_start_token_id=self.decoder_start_token_id							,							)

      def        __a		(	self				:      Optional[Any]   )							->		List[str]:


         '''simple docstring'''

         return TaConfig(
             vocab_size=self.vocab_size							,							d_model=self.hidden_size							,							d_ff=self.d_ff							,							d_kv=self.hidden_size // self.num_attention_heads							,							num_layers=self.num_hidden_layers							,							num_decoder_layers=self.decoder_layers							,							num_heads=self.num_attention_heads							,							relative_attention_num_buckets=self.relative_attention_num_buckets							,							dropout_rate=self.dropout_rate							,							initializer_factor=self.initializer_factor							,							eos_token_id=self.eos_token_id							,							bos_token_id=self.pad_token_id							,							pad_token_id=self.pad_token_id							,							decoder_start_token_id=self.decoder_start_token_id							,							)

      def        __a		(	self				:      int							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      int							,							)							->		List[str]:


         '''simple docstring'''

         lowercase							=		UMTaModel(config=lowerCAmelCase__   )
         model.to(lowerCAmelCase__   )
         model.eval()
         lowercase							=		model(
             input_ids=lowerCAmelCase__							,							decoder_input_ids=lowerCAmelCase__							,							attention_mask=lowerCAmelCase__							,							decoder_attention_mask=lowerCAmelCase__							,							)
         lowercase							=		model(input_ids=lowerCAmelCase__							,							decoder_input_ids=lowerCAmelCase__   )
         lowercase							=		result.last_hidden_state
         lowercase							=		result.past_key_values
         lowercase							=		result.encoder_last_hidden_state

         self.parent.assertEqual(encoder_output.size()							,							(self.batch_size, self.encoder_seq_length, self.hidden_size)   )
         self.parent.assertEqual(decoder_output.size()							,							(self.batch_size, self.decoder_seq_length, self.hidden_size)   )
         # There should be `num_layers` key value embeddings stored in decoder_past
         self.parent.assertEqual(len(lowerCAmelCase__   )							,							config.num_layers   )
         # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
         self.parent.assertEqual(len(decoder_past[0]   )							,							4   )

      def        __a		(	self				:      str							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Any							,							)							->		Optional[Any]:


         '''simple docstring'''

         lowercase							=		UMTaModel(config=lowerCAmelCase__   ).get_decoder().to(lowerCAmelCase__   ).eval()
         # first forward pass
         lowercase							=		model(lowerCAmelCase__							,							use_cache=lowerCAmelCase__   )
         lowercase							=		model(lowerCAmelCase__   )
         lowercase							=		model(lowerCAmelCase__							,							use_cache=lowerCAmelCase__   )

         self.parent.assertTrue(len(lowerCAmelCase__   ) == len(lowerCAmelCase__   )   )
         self.parent.assertTrue(len(lowerCAmelCase__   ) == len(lowerCAmelCase__   ) + 1   )

         lowercase		,lowercase							=		outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         lowercase							=		ids_tensor((self.batch_size, 1)							,							config.vocab_size   )

         # append to next input_ids and
         lowercase							=		torch.cat([input_ids, next_tokens]							,							dim=-1   )

         lowercase							=		model(lowerCAmelCase__   )['''last_hidden_state''']
         lowercase							=		model(lowerCAmelCase__							,							past_key_values=lowerCAmelCase__   )['''last_hidden_state''']

         # select random slice
         lowercase							=		ids_tensor((1,)							,							output_from_past.shape[-1]   ).item()
         lowercase							=		output_from_no_past[:, -1, random_slice_idx].detach()
         lowercase							=		output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(lowerCAmelCase__							,							lowerCAmelCase__							,							atol=1E-3   )   )



      def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      List[str]							,							)							->		Optional[int]:


         '''simple docstring'''

         lowercase							=		UMTaModel(config=lowerCAmelCase__   ).to(lowerCAmelCase__   ).half().eval()
         lowercase							=		model(**lowerCAmelCase__   )['''last_hidden_state''']
         self.parent.assertFalse(torch.isnan(lowerCAmelCase__   ).any().item()   )






@require_torch
class      __lowercase					(    _snake_case       ,     _snake_case       ,     _snake_case       ,     unittest.TestCase    ):
      lowercase					 =     (
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      lowercase					 =     (UMTaForConditionalGeneration,) if is_torch_available() else ()
      lowercase					 =     (
          {
              'conversational': UMTaForConditionalGeneration,
              'feature-extraction': UMTaModel,
              'summarization': UMTaForConditionalGeneration,
              'text2text-generation': UMTaForConditionalGeneration,
              'translation': UMTaForConditionalGeneration,
              'question-answering': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      lowercase					 =     True
      lowercase					 =     False
      lowercase					 =     False
      lowercase					 =     True
      lowercase					 =     True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      lowercase					 =     [0.8, 0.9]

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


         '''simple docstring'''

         lowercase							=		UMTaModelTester(self   )

      @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
      def        __a		(	self				:      Tuple   )							->		List[str]:


         '''simple docstring'''

         lowercase							=		self.model_tester.prepare_config_and_inputs()
         lowercase							=		UMTaModel(config_and_inputs[0]   ).to(lowerCAmelCase__   )
         with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                lowerCAmelCase__							,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							,							f'{tmpdirname}/t5_test.onnx'							,							export_params=lowerCAmelCase__							,							opset_version=9							,							input_names=['''input_ids''', '''decoder_input_ids''']							,							)

      @unittest.skipIf(torch_device == '''cpu'''							,							'''Cant do half precision'''   )
      def        __a		(	self				:      Optional[Any]   )							->		int:


         '''simple docstring'''

         lowercase							=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase__   )

      def        __a		(	self				:      int   )							->		Optional[Any]:


         '''simple docstring'''

         lowercase							=		['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
         lowercase							=		self.model_tester.prepare_config_and_inputs()
         lowercase							=		config_and_inputs[0]
         lowercase							=		UMTaForConditionalGeneration(lowerCAmelCase__   ).eval()
         model.to(lowerCAmelCase__   )

         lowercase							=		{
             '''head_mask''': torch.zeros(config.num_layers							,							config.num_heads							,							device=lowerCAmelCase__   ),
             '''decoder_head_mask''': torch.zeros(config.num_decoder_layers							,							config.num_heads							,							device=lowerCAmelCase__   ),
             '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers							,							config.num_heads							,							device=lowerCAmelCase__   ),
         }

         for attn_name, (name, mask) in zip(lowerCAmelCase__							,							head_masking.items()   ):
            lowercase							=		{name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
               lowercase							=		torch.ones(
                   config.num_decoder_layers							,							config.num_heads							,							device=lowerCAmelCase__   )

            lowercase							=		model.generate(
                config_and_inputs[1]['''input_ids''']							,							num_beams=1							,							max_length=3							,							output_attentions=lowerCAmelCase__							,							return_dict_in_generate=lowerCAmelCase__							,							**lowerCAmelCase__							,							)
            # We check the state of decoder_attentions and cross_attentions just from the last step
            lowercase							=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
            self.assertEqual(sum([w.sum().item() for w in attn_weights]   )							,							0.0   )



      @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''   )
      def        __a		(	self				:      Optional[Any]   )							->		Optional[int]:


         '''simple docstring'''

         pass






@require_torch
@require_sentencepiece
@require_tokenizers
class      __lowercase					(    unittest.TestCase    ):

      @slow
      @unittest.skip(
          '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''   )
      def        __a		(	self				:      Tuple   )							->		str:


         '''simple docstring'''

         lowercase							=		UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''							,							return_dict=lowerCAmelCase__   ).to(lowerCAmelCase__   )
         lowercase							=		AutoTokenizer.from_pretrained('''google/umt5-small'''							,							use_fast=lowerCAmelCase__							,							legacy=lowerCAmelCase__   )
         lowercase							=		[
             '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
             '''No se como puedo <extra_id_0>.''',
             '''This is the reason why we <extra_id_0> them.''',
             '''The <extra_id_0> walks in <extra_id_1>, seats''',
             '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
         ]
         lowercase							=		tokenizer(lowerCAmelCase__							,							return_tensors='''pt'''							,							padding=lowerCAmelCase__   ).input_ids
         # fmt: off
         lowercase							=		torch.tensor(
             [
                 [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                 [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
             ]   )
         # fmt: on
         torch.testing.assert_allclose(lowerCAmelCase__							,							lowerCAmelCase__   )

         lowercase							=		model.generate(input_ids.to(lowerCAmelCase__   )   )
         lowercase							=		[
             '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
             '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
         ]
         lowercase							=		tokenizer.batch_decode(lowerCAmelCase__   )
         self.assertEqual(lowerCAmelCase__							,							lowerCAmelCase__   )






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )
			if "model" in sd.keys():
						lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )['''model''']

			# pop unnecessary weights
			lowercase							=		[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(UpperCAmelCase       )

			lowercase							=		{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowercase							=		sd.pop(UpperCAmelCase       )

			lowercase							=		list(sd.keys()       )
			for key in keys:
						if ".qkv_proj." in key:
									lowercase							=		sd[key]
									# We split QKV in separate Q,K,V

									lowercase							=		key.replace('''.qkv_proj.''',		'''.q_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.k_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.v_proj.'''       )

									lowercase							=		value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowercase		,lowercase		,lowercase							=		torch.split(UpperCAmelCase,		depth // 3,		dim=0       )

									lowercase							=		q
									lowercase							=		k
									lowercase							=		v
									del sd[key]

			return sd






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None       )->       int:



			"""simple docstring"""







			lowercase							=		load_checkpoint(UpperCAmelCase       )

			if config is not None:
						lowercase							=		OPTConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		OPTConfig()

			lowercase							=		OPTModel(UpperCAmelCase       ).half().eval()
			model.load_state_dict(UpperCAmelCase       )

			# Check results
			Path(UpperCAmelCase       ).mkdir(exist_ok=UpperCAmelCase       )
			model.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--fairseq_path",
						    type=str,
						    help=(
						        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
						        " https://huggingface.co/models?other=opt_metasq"
						    ),
						)
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
						A_      =      parser.parse_args()
						convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





