import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
 import torch

 from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
     SpeechaTextaDecoder,
     SpeechaTextaForCausalLM,
 )






@require_torch
class    __A				:






    def __init__(	self			:    int    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    List[Any]=99    ,				UpperCAmelCase_			:    str=13    ,				UpperCAmelCase_			:    List[str]=16    ,				UpperCAmelCase_			:    Optional[int]=7    ,				UpperCAmelCase_			:    Optional[Any]=True    ,				UpperCAmelCase_			:    Dict=True    ,				UpperCAmelCase_			:    List[str]=True    ,				UpperCAmelCase_			:    int=False    ,				UpperCAmelCase_			:    Optional[Any]=True    ,				UpperCAmelCase_			:    Optional[Any]=2    ,				UpperCAmelCase_			:    Dict=32    ,				UpperCAmelCase_			:    List[str]=4    ,				UpperCAmelCase_			:    int=4    ,				UpperCAmelCase_			:    Optional[Any]=30    ,				UpperCAmelCase_			:    List[Any]=0    ,				UpperCAmelCase_			:    str=1    ,				UpperCAmelCase_			:    Dict=2    ,				UpperCAmelCase_			:    Any=None    ,				):
        lowerCAmelCase				:			List[Any]    			=      parent
        lowerCAmelCase				:			List[str]    			=      batch_size
        lowerCAmelCase				:			Optional[Any]    			=      decoder_seq_length
        # For common tests
        lowerCAmelCase				:			List[Any]    			=      self.decoder_seq_length
        lowerCAmelCase				:			str    			=      is_training
        lowerCAmelCase				:			List[str]    			=      use_attention_mask
        lowerCAmelCase				:			List[str]    			=      use_labels

        lowerCAmelCase				:			Dict    			=      vocab_size
        lowerCAmelCase				:			str    			=      d_model
        lowerCAmelCase				:			List[str]    			=      d_model
        lowerCAmelCase				:			str    			=      decoder_layers
        lowerCAmelCase				:			str    			=      decoder_layers
        lowerCAmelCase				:			Union[str, Any]    			=      decoder_ffn_dim
        lowerCAmelCase				:			str    			=      decoder_attention_heads
        lowerCAmelCase				:			Union[str, Any]    			=      decoder_attention_heads
        lowerCAmelCase				:			List[str]    			=      eos_token_id
        lowerCAmelCase				:			Optional[int]    			=      bos_token_id
        lowerCAmelCase				:			int    			=      pad_token_id
        lowerCAmelCase				:			int    			=      decoder_start_token_id
        lowerCAmelCase				:			Optional[Any]    			=      use_cache
        lowerCAmelCase				:			List[str]    			=      max_position_embeddings

        lowerCAmelCase				:			Optional[Any]    			=      None
        lowerCAmelCase				:			Any    			=      decoder_seq_length
        lowerCAmelCase				:			Union[str, Any]    			=      2
        lowerCAmelCase				:			Tuple    			=      1






    def 	lowercase__ (	self			:    Tuple    ):
        lowerCAmelCase				:			List[str]    			=      ids_tensor([self.batch_size, self.decoder_seq_length]    ,				self.vocab_size    )

        lowerCAmelCase				:			Optional[int]    			=      None
        if self.use_attention_mask:
            lowerCAmelCase				:			List[str]    			=      ids_tensor([self.batch_size, self.decoder_seq_length]    ,				vocab_size=2    )

        lowerCAmelCase				:			List[str]    			=      None
        if self.use_labels:
            lowerCAmelCase				:			List[str]    			=      ids_tensor([self.batch_size, self.decoder_seq_length]    ,				self.vocab_size    )

        lowerCAmelCase				:			Union[str, Any]    			=      SpeechaTextaConfig(
            vocab_size=self.vocab_size    ,				d_model=self.d_model    ,				decoder_layers=self.decoder_layers    ,				decoder_ffn_dim=self.decoder_ffn_dim    ,				decoder_attention_heads=self.decoder_attention_heads    ,				eos_token_id=self.eos_token_id    ,				bos_token_id=self.bos_token_id    ,				use_cache=self.use_cache    ,				pad_token_id=self.pad_token_id    ,				decoder_start_token_id=self.decoder_start_token_id    ,				max_position_embeddings=self.max_position_embeddings    ,				)

        return (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        )






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[str]    ,				):
        lowerCAmelCase				:			str    			=      True
        lowerCAmelCase				:			Optional[Any]    			=      SpeechaTextaDecoder(config=UpperCAmelCase_    ).to(UpperCAmelCase_    ).eval()
        lowerCAmelCase				:			Union[str, Any]    			=      input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        lowerCAmelCase				:			List[Any]    			=      model(UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    )
        lowerCAmelCase				:			List[Any]    			=      model(UpperCAmelCase_    )
        lowerCAmelCase				:			str    			=      model(UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    )

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

        lowerCAmelCase				:			Tuple    			=      outputs['past_key_values']

        # create hypothetical next token and extent to next_input_ids
        lowerCAmelCase				:			Union[str, Any]    			=      ids_tensor((2, 1)    ,				config.vocab_size - 1    ) + 1

        # append to next input_ids and
        lowerCAmelCase				:			List[str]    			=      torch.cat([input_ids, next_tokens]    ,				dim=-1    )

        print(UpperCAmelCase_    )

        lowerCAmelCase				:			Union[str, Any]    			=      model(UpperCAmelCase_    )['last_hidden_state']
        lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				past_key_values=UpperCAmelCase_    )['last_hidden_state']

        # select random slice
        lowerCAmelCase				:			Optional[Any]    			=      ids_tensor((1,)    ,				output_from_past.shape[-1]    ).item()
        lowerCAmelCase				:			List[str]    			=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        lowerCAmelCase				:			int    			=      output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(UpperCAmelCase_    ,				UpperCAmelCase_    ,				atol=1E-3    )






    def 	lowercase__ (	self			:    Optional[int]    ):
        lowerCAmelCase				:			int    			=      self.prepare_config_and_inputs()
        (
            lowerCAmelCase  
        )				:			Any    			=      config_and_inputs

        lowerCAmelCase				:			Union[str, Any]    			=      {
            'input_ids': input_ids,
            'attention_mask': attention_mask,
        }
        return config, inputs_dict






@require_torch
class    __A				(							lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
    lowerCAmelCase_							:		List[Any]						=							(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
    lowerCAmelCase_							:		int						=							(SpeechaTextaForCausalLM,) if is_torch_available() else ()
    lowerCAmelCase_							:		List[Any]						=							{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
    lowerCAmelCase_							:		int						=							True
    lowerCAmelCase_							:		Dict						=							False






    def 	lowercase__ (	self			:    Optional[Any]    ,				):
        lowerCAmelCase				:			Optional[Any]    			=      SpeechaTextaStandaloneDecoderModelTester(self    ,				is_training=UpperCAmelCase_    )
        lowerCAmelCase				:			Dict    			=      ConfigTester(self    ,				config_class=UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Optional[int]    ):
        pass






    def 	lowercase__ (	self			:    Any    ):
        pass






    def 	lowercase__ (	self			:    Dict    ):
        pass






    def 	lowercase__ (	self			:    Dict    ):
        self.config_tester.run_common_tests()






    def 	lowercase__ (	self			:    Optional[int]    ):
        lowerCAmelCase				:			int    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Tuple    ):
        return








def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:



    '''simple docstring'''






    lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
    lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)

    lowerCAmelCase				:			str    			=      [
        [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]
    lowerCAmelCase				:			str    			=      [
        ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]

    for i in range(1,      len_source_seq + 1				):
        lowerCAmelCase				:			int    			=      i * delete_cost
        lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"

    for i in range(1,      len_destination_seq + 1				):
        lowerCAmelCase				:			Tuple    			=      i * insert_cost
        lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"

    for i in range(1,      len_source_seq + 1				):
        for j in range(1,      len_destination_seq + 1				):
            if source_seq[i - 1] == destination_seq[j - 1]:
                lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
            else:
                lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)

            if costs[i - 1][j] + delete_cost < costs[i][j]:
                lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"

            if costs[i][j - 1] + insert_cost < costs[i][j]:
                lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"

    return costs, ops
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:



    '''simple docstring'''






    if i == 0 and j == 0:
        return []
    else:
        if ops[i][j][0] == "C" or ops[i][j][0] == "R":
            lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
            seq.append(ops[i][j]				)
            return seq
        elif ops[i][j][0] == "D":
            lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
            seq.append(ops[i][j]				)
            return seq
        else:
            lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
            seq.append(ops[i][j]				)
            return seq


if __name__ == "__main__":
 __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)

 __A   :     Optional[int]      =			len(operations)
 __A   :     Optional[Any]      =			len(operations[0])
 __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)

 __A   :     int      =			list('''Python''')
 __A   :     Tuple      =			0
 __A   :     Tuple      =			0

 with open('''min_cost.txt''', '''w''') as file:
  for op in sequence:
   print(''''''.join(string))

   if op[0] == "C":
    file.write('''%-16s''' % '''Copy %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost -= 1
   elif op[0] == "R":
    __A   :     Any      =			op[2]

    file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
    file.write('''\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('''%-16s''' % '''Delete %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('''%-16s''' % '''Insert %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2

   i += 1

  print(''''''.join(string))
  print('''Cost: ''', cost)

  file.write('''\r\nMinimum cost: ''' + str(cost))





