





import os

import numpy
import onnx


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			a.name
    SCREAMING_SNAKE_CASE__											=			b.name

    SCREAMING_SNAKE_CASE__											=			''''''
    SCREAMING_SNAKE_CASE__											=			''''''

    SCREAMING_SNAKE_CASE__											=			a == b

    SCREAMING_SNAKE_CASE__											=			name_a
    SCREAMING_SNAKE_CASE__											=			name_b

    return res


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[Any]:
    for i, input_name in enumerate(node_proto.input	):
        if input_name == name:
            node_proto.input.insert(lowerCAmelCase_ ,							lowerCAmelCase_	)
            node_proto.input.pop(i + 1	)

    if node_proto.op_type == "If":
        _graph_replace_input_with(node_proto.attribute[0].g ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
        _graph_replace_input_with(node_proto.attribute[1].g ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    if node_proto.op_type == "Loop":
        _graph_replace_input_with(node_proto.attribute[0].g ,							lowerCAmelCase_ ,							lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						int:
    for n in graph_proto.node:
        _node_replace_input_with(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			list(model.graph.initializer	)
    SCREAMING_SNAKE_CASE__											=			list(model_without_ext.graph.initializer	)
    for i, ref_i in ind_to_replace:
        assert inits_with_data[i].name == inits[i].name
        assert inits_with_data[ref_i].name == inits[ref_i].name
        assert i > ref_i

        SCREAMING_SNAKE_CASE__											=			inits[i].name
        SCREAMING_SNAKE_CASE__											=			inits[ref_i].name

        model_without_ext.graph.initializer.remove(inits[i]	)

        # for n in model.graph.node:
        _graph_replace_input_with(model_without_ext.graph ,							lowerCAmelCase_ ,							lowerCAmelCase_	)



def 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			os.path.dirname(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			os.path.basename(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			onnx.load(os.path.join(lowerCAmelCase_ ,							lowerCAmelCase_	)	)

    SCREAMING_SNAKE_CASE__											=			list(model.graph.initializer	)

    SCREAMING_SNAKE_CASE__											=			set()
    SCREAMING_SNAKE_CASE__											=			{}
    SCREAMING_SNAKE_CASE__											=			[]

    SCREAMING_SNAKE_CASE__											=			0

    for i in range(len(lowerCAmelCase_	)	):
        if i in dup_set:
            continue

        for j in range(i + 1 ,							len(lowerCAmelCase_	)	):
            if j in dup_set:
                continue
            if _is_equal_tensor_proto(inits[i] ,							inits[j]	):
                dup_set.add(lowerCAmelCase_	)
                dup_set.add(lowerCAmelCase_	)

                SCREAMING_SNAKE_CASE__											=			inits[j].data_type
                SCREAMING_SNAKE_CASE__											=			numpy.prod(inits[j].dims	)
                if dtype == 1:
                    mem_size *= 4
                elif dtype == 6:
                    mem_size *= 4
                elif dtype == 7 or dtype == 1_1:
                    mem_size *= 8
                else:
                    print('''unexpected data type: ''' ,							lowerCAmelCase_	)
                total_reduced_size += mem_size

                SCREAMING_SNAKE_CASE__											=			inits[i].name
                SCREAMING_SNAKE_CASE__											=			inits[j].name

                if name_i in dup_map:
                    dup_map[name_i].append(lowerCAmelCase_	)
                else:
                    SCREAMING_SNAKE_CASE__											=			[name_j]
                ind_to_replace.append((j, i)	)

    print('''total reduced size: ''' ,							total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 ,							'''GB'''	)

    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_	)
    _remove_dup_initializers_from_model(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			'''optimized_''' + model_file_name
    SCREAMING_SNAKE_CASE__											=			os.path.join(lowerCAmelCase_ ,							lowerCAmelCase_	)
    onnx.save(lowerCAmelCase_ ,							lowerCAmelCase_	)

    return new_model







import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
  import torch

  from transformers import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
  )



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=14   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=16   ,				A_=2   ,				A_=0.02   ,				A_=3   ,				A_=4   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_token_type_ids
        SCREAMING_SNAKE_CASE__											=			use_input_mask
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			use_mc_token_ids
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			type_vocab_size
        SCREAMING_SNAKE_CASE__											=			type_sequence_label_size
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			num_labels
        SCREAMING_SNAKE_CASE__											=			num_choices
        SCREAMING_SNAKE_CASE__											=			scope
        SCREAMING_SNAKE_CASE__											=			self.vocab_size - 1



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_input_mask:
            SCREAMING_SNAKE_CASE__											=			random_attention_mask([self.batch_size, self.seq_length]     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.type_vocab_size     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_mc_token_ids:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.num_choices]   ,				self.seq_length     )

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        if self.use_labels:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.num_choices     )

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,				2     )

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return CTRLConfig(
            vocab_size=self.vocab_size   ,				n_embd=self.hidden_size   ,				n_layer=self.num_hidden_layers   ,				n_head=self.num_attention_heads   ,				n_positions=self.max_position_embeddings   ,				pad_token_id=self.pad_token_id   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModel(config=A_     )
        model.to(A_     )
        model.eval()

        model(A_   ,				token_type_ids=A_   ,				head_mask=A_     )
        model(A_   ,				token_type_ids=A_     )
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
        self.parent.assertEqual(len(result.past_key_values     )   ,				config.n_layer     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel(A_     )
        model.to(A_     )
        model.eval()

        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.loss.shape   ,				()     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()

        (
            (
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		
        )											=			config_and_inputs

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

        return config, inputs_dict




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.num_labels
        SCREAMING_SNAKE_CASE__											=			CTRLForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Union[str, Any]				=	(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
    lowerCamelCase__    :		List[Any]				=	(CTRLLMHeadModel,) if is_torch_available() else ()
    lowerCamelCase__    :		Tuple				=	(
        {
            """feature-extraction""": CTRLModel,
            """text-classification""": CTRLForSequenceClassification,
            """text-generation""": CTRLLMHeadModel,
            """zero-shot""": CTRLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase__    :		List[Any]				=	True
    lowerCamelCase__    :		Optional[int]				=	False
    lowerCamelCase__    :		Any				=	False



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
            # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
            # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
            # config could not be created.
            return True

        return False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				n_embd=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*A_     )



    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__											=			CTRLModel.from_pretrained(A_     )
            self.assertIsNotNone(A_     )




    @unittest.skip('''The model doesn\'t support left padding'''     )  # and it's not used enough to be worth fixing :)
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass





@require_torch
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel.from_pretrained('''ctrl'''     )
        model.to(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor(
            [[1_18_59, 0, 16_11, 8]]   ,				dtype=torch.long   ,				device=A_     )  # Legal the president is
        SCREAMING_SNAKE_CASE__											=			[
            1_18_59,
            0,
            16_11,
            8,
            5,
            1_50,
            2_64_49,
            2,
            19,
            3_48,
            4_69,
            3,
            25_95,
            48,
            2_07_40,
            24_65_33,
            24_65_33,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        SCREAMING_SNAKE_CASE__											=			model.generate(A_   ,				do_sample=A_     )
        self.assertListEqual(output_ids[0].tolist()   ,				A_     )
