





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase    : Tuple    	=  logging.get_logger(__name__)

lowercase    : Optional[int]    	=  {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "umt5"
     __A	  =       ["past_key_values"]

     def __init__(      self,       A=25_0112,       A=512,       A=64,       A=1024,       A=8,       A=None,       A=6,       A=32,       A=128,       A=0.1,       A=1E-6,       A=1.0,       A="gated-gelu",       A=True,       A=True,       A="T5Tokenizer",       A=True,       A=0,       A=1,       A=0,       **A,       ):





            """simple docstring"""

            super().__init__(
                is_encoder_decoder=A,       tokenizer_class=A,       tie_word_embeddings=A,       pad_token_id=A,       eos_token_id=A,       decoder_start_token_id=A,       **A,       )
            _UpperCamelCase              =						vocab_size
            _UpperCamelCase              =						d_model
            _UpperCamelCase              =						d_kv
            _UpperCamelCase              =						d_ff
            _UpperCamelCase              =						num_layers
            _UpperCamelCase              =						(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            _UpperCamelCase              =						num_heads
            _UpperCamelCase              =						relative_attention_num_buckets
            _UpperCamelCase              =						relative_attention_max_distance
            _UpperCamelCase              =						dropout_rate
            _UpperCamelCase              =						layer_norm_epsilon
            _UpperCamelCase              =						initializer_factor
            _UpperCamelCase              =						feed_forward_proj
            _UpperCamelCase              =						use_cache

            _UpperCamelCase              =						self.feed_forward_proj.split('''-'''      )
            _UpperCamelCase              =						act_info[-1]
            _UpperCamelCase              =						act_info[0] == '''gated'''

            if len(A      ) > 1 and act_info[0] != "gated" or len(A      ) > 2:
                   raise ValueError(
                       F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                       '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                       '''\'gated-gelu\' or \'relu\''''      )

            if feed_forward_proj == "gated-gelu":
                   _UpperCamelCase              =						'''gelu_new'''

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.d_model

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.num_heads



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.num_layers






class  __A(						__UpperCAmelCase ):

     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						{
                '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
                '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
            }
            if self.use_past:
                   _UpperCamelCase              =						'''past_encoder_sequence + sequence'''
                   _UpperCamelCase              =						{0: '''batch'''}
                   _UpperCamelCase              =						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            else:
                   _UpperCamelCase              =						{0: '''batch''', 1: '''decoder_sequence'''}
                   _UpperCamelCase              =						{0: '''batch''', 1: '''decoder_sequence'''}

            if self.use_past:
                   self.fill_with_past_key_values_(A,       direction='''inputs'''      )

            return common_inputs

     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 13



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 5E-4













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  __A(						unittest.TestCase ):
     __A	  =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __A	  =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TextaTextGenerationPipeline(model=A,       tokenizer=A      )
            return generator, ["Something to write", "Something else"]

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						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'''      )      )

            _UpperCamelCase              =						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      )}],
                ],       )

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''pt'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )

            _UpperCamelCase              =						3
            _UpperCamelCase              =						generator(
                '''Something there''',       num_return_sequences=A,       num_beams=A,       )
            _UpperCamelCase              =						[
                {'''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      )

            _UpperCamelCase              =						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      )},
                ],       )
            _UpperCamelCase              =						generator.model.config.eos_token_id
            _UpperCamelCase              =						'''<pad>'''
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''tf'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )






